moto/moto/sagemaker/models.py

import json
import os
import random
import string
from datetime import datetime
from typing import Any, Dict, List, Optional, Iterable

from moto.core import BaseBackend, BackendDict, BaseModel, CloudFormationModel
from moto.sagemaker import validators
from moto.utilities.paginator import paginate
from .exceptions import (
    MissingModel,
    ValidationError,
    AWSValidationException,
    ResourceNotFound,
)
from .utils import (
    get_pipeline_from_name,
    get_pipeline_execution_from_arn,
    get_pipeline_name_from_execution_arn,
)
from .utils import load_pipeline_definition_from_s3, arn_formatter


PAGINATION_MODEL = {
    "list_experiments": {
        "input_token": "NextToken",
        "limit_key": "MaxResults",
        "limit_default": 100,
        "unique_attribute": "experiment_arn",
        "fail_on_invalid_token": True,
    },
    "list_trials": {
        "input_token": "NextToken",
        "limit_key": "MaxResults",
        "limit_default": 100,
        "unique_attribute": "trial_arn",
        "fail_on_invalid_token": True,
    },
    "list_trial_components": {
        "input_token": "NextToken",
        "limit_key": "MaxResults",
        "limit_default": 100,
        "unique_attribute": "trial_component_arn",
        "fail_on_invalid_token": True,
    },
    "list_tags": {
        "input_token": "NextToken",
        "limit_key": "MaxResults",
        "limit_default": 50,
        "unique_attribute": "Key",
        "fail_on_invalid_token": True,
    },
}


class BaseObject(BaseModel):
    def camelCase(self, key: str) -> str:
        words = []
        for word in key.split("_"):
            words.append(word.title())
        return "".join(words)

    def update(self, details_json: str) -> None:
        details = json.loads(details_json)
        for k in details.keys():
            setattr(self, k, details[k])

    def gen_response_object(self) -> Dict[str, Any]:
        response_object: Dict[str, Any] = dict()
        for key, value in self.__dict__.items():
            if "_" in key:
                response_object[self.camelCase(key)] = value
            else:
                response_object[key[0].upper() + key[1:]] = value
        return response_object

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        return self.gen_response_object()


class FakePipelineExecution(BaseObject):
    def __init__(
        self,
        pipeline_execution_arn: str,
        pipeline_execution_display_name: str,
        pipeline_parameters: List[Dict[str, str]],
        pipeline_execution_description: str,
        parallelism_configuration: Dict[str, int],
        pipeline_definition: str,
        client_request_token: str,
    ):
        self.pipeline_execution_arn = pipeline_execution_arn
        self.pipeline_execution_display_name = pipeline_execution_display_name
        self.pipeline_parameters = pipeline_parameters
        self.pipeline_execution_description = pipeline_execution_description
        self.pipeline_execution_status = "Succeeded"
        self.pipeline_execution_failure_reason = None
        self.parallelism_configuration = parallelism_configuration
        self.pipeline_definition_for_execution = pipeline_definition
        self.client_request_token = client_request_token

        now_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.creation_time = now_string
        self.last_modified_time = now_string
        self.start_time = now_string

        fake_user_profile_name = "fake-user-profile-name"
        fake_domain_id = "fake-domain-id"
        fake_user_profile_arn = arn_formatter(
            "user-profile",
            f"{fake_domain_id}/{fake_user_profile_name}",
            pipeline_execution_arn.split(":")[4],
            pipeline_execution_arn.split(":")[3],
        )
        self.created_by = {
            "UserProfileArn": fake_user_profile_arn,
            "UserProfileName": fake_user_profile_name,
            "DomainId": fake_domain_id,
        }
        self.last_modified_by = {
            "UserProfileArn": fake_user_profile_arn,
            "UserProfileName": fake_user_profile_name,
            "DomainId": fake_domain_id,
        }


class FakePipeline(BaseObject):
    def __init__(
        self,
        pipeline_name: str,
        pipeline_display_name: str,
        pipeline_definition: str,
        pipeline_description: str,
        role_arn: str,
        tags: List[Dict[str, str]],
        account_id: str,
        region_name: str,
        parallelism_configuration: Dict[str, int],
    ):
        self.pipeline_name = pipeline_name
        self.pipeline_arn = arn_formatter(
            "pipeline", pipeline_name, account_id, region_name
        )
        self.pipeline_display_name = pipeline_display_name or pipeline_name
        self.pipeline_definition = pipeline_definition
        self.pipeline_description = pipeline_description
        self.pipeline_executions: Dict[str, FakePipelineExecution] = dict()
        self.role_arn = role_arn
        self.tags = tags or []
        self.parallelism_configuration = parallelism_configuration

        now_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.creation_time = now_string
        self.last_modified_time = now_string
        self.last_execution_time: Optional[str] = None

        self.pipeline_status = "Active"
        fake_user_profile_name = "fake-user-profile-name"
        fake_domain_id = "fake-domain-id"
        fake_user_profile_arn = arn_formatter(
            "user-profile",
            f"{fake_domain_id}/{fake_user_profile_name}",
            account_id,
            region_name,
        )
        self.created_by = {
            "UserProfileArn": fake_user_profile_arn,
            "UserProfileName": fake_user_profile_name,
            "DomainId": fake_domain_id,
        }
        self.last_modified_by = {
            "UserProfileArn": fake_user_profile_arn,
            "UserProfileName": fake_user_profile_name,
            "DomainId": fake_domain_id,
        }


class FakeProcessingJob(BaseObject):
    def __init__(
        self,
        app_specification: Dict[str, Any],
        experiment_config: Dict[str, str],
        network_config: Dict[str, Any],
        processing_inputs: List[Dict[str, Any]],
        processing_job_name: str,
        processing_output_config: Dict[str, Any],
        account_id: str,
        region_name: str,
        role_arn: str,
        tags: List[Dict[str, str]],
        stopping_condition: Dict[str, int],
    ):
        self.processing_job_name = processing_job_name
        self.processing_job_arn = FakeProcessingJob.arn_formatter(
            processing_job_name, account_id, region_name
        )

        now_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.creation_time = now_string
        self.last_modified_time = now_string
        self.processing_end_time = now_string
        self.tags = tags or []
        self.role_arn = role_arn
        self.app_specification = app_specification
        self.experiment_config = experiment_config
        self.network_config = network_config
        self.processing_inputs = processing_inputs
        self.processing_job_status = "Completed"
        self.processing_output_config = processing_output_config
        self.stopping_condition = stopping_condition

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def response_create(self) -> Dict[str, str]:
        return {"ProcessingJobArn": self.processing_job_arn}

    @staticmethod
    def arn_formatter(name: str, account_id: str, region: str) -> str:
        return arn_formatter("processing-job", name, account_id, region)


class FakeTrainingJob(BaseObject):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        training_job_name: str,
        hyper_parameters: Dict[str, str],
        algorithm_specification: Dict[str, Any],
        role_arn: str,
        input_data_config: List[Dict[str, Any]],
        output_data_config: Dict[str, str],
        resource_config: Dict[str, Any],
        vpc_config: Dict[str, List[str]],
        stopping_condition: Dict[str, int],
        tags: List[Dict[str, str]],
        enable_network_isolation: bool,
        enable_inter_container_traffic_encryption: bool,
        enable_managed_spot_training: bool,
        checkpoint_config: Dict[str, str],
        debug_hook_config: Dict[str, Any],
        debug_rule_configurations: List[Dict[str, Any]],
        tensor_board_output_config: Dict[str, str],
        experiment_config: Dict[str, str],
    ):
        self.training_job_name = training_job_name
        self.hyper_parameters = hyper_parameters
        self.algorithm_specification = algorithm_specification
        self.role_arn = role_arn
        self.input_data_config = input_data_config
        self.output_data_config = output_data_config
        self.resource_config = resource_config
        self.vpc_config = vpc_config
        self.stopping_condition = stopping_condition
        self.tags = tags or []
        self.enable_network_isolation = enable_network_isolation
        self.enable_inter_container_traffic_encryption = (
            enable_inter_container_traffic_encryption
        )
        self.enable_managed_spot_training = enable_managed_spot_training
        self.checkpoint_config = checkpoint_config
        self.debug_hook_config = debug_hook_config
        self.debug_rule_configurations = debug_rule_configurations
        self.tensor_board_output_config = tensor_board_output_config
        self.experiment_config = experiment_config
        self.training_job_arn = FakeTrainingJob.arn_formatter(
            training_job_name, account_id, region_name
        )
        self.creation_time = self.last_modified_time = datetime.now().strftime(
            "%Y-%m-%d %H:%M:%S"
        )
        self.model_artifacts = {
            "S3ModelArtifacts": os.path.join(
                self.output_data_config["S3OutputPath"],
                self.training_job_name,
                "output",
                "model.tar.gz",
            )
        }
        self.training_job_status = "Completed"
        self.secondary_status = "Completed"
        self.algorithm_specification["MetricDefinitions"] = [
            {
                "Name": "test:dcg",
                "Regex": "#quality_metric: host=\\S+, test dcg <score>=(\\S+)",
            }
        ]
        now_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.creation_time = now_string
        self.last_modified_time = now_string
        self.training_start_time = now_string
        self.training_end_time = now_string
        self.secondary_status_transitions = [
            {
                "Status": "Starting",
                "StartTime": self.creation_time,
                "EndTime": self.creation_time,
                "StatusMessage": "Preparing the instances for training",
            }
        ]
        self.final_metric_data_list = [
            {
                "MetricName": "train:progress",
                "Value": 100.0,
                "Timestamp": self.creation_time,
            }
        ]

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def response_create(self) -> Dict[str, str]:
        return {"TrainingJobArn": self.training_job_arn}

    @staticmethod
    def arn_formatter(name: str, account_id: str, region_name: str) -> str:
        return arn_formatter("training-job", name, account_id, region_name)


class FakeEndpoint(BaseObject, CloudFormationModel):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        endpoint_name: str,
        endpoint_config_name: str,
        production_variants: List[Dict[str, Any]],
        data_capture_config: Dict[str, Any],
        tags: List[Dict[str, str]],
    ):
        self.endpoint_name = endpoint_name
        self.endpoint_arn = FakeEndpoint.arn_formatter(
            endpoint_name, account_id, region_name
        )
        self.endpoint_config_name = endpoint_config_name
        self.production_variants = self._process_production_variants(
            production_variants
        )
        self.data_capture_config = data_capture_config
        self.tags = tags or []
        self.endpoint_status = "InService"
        self.failure_reason = None
        self.creation_time = self.last_modified_time = datetime.now().strftime(
            "%Y-%m-%d %H:%M:%S"
        )

    def _process_production_variants(
        self, production_variants: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        endpoint_variants = []
        for production_variant in production_variants:
            temp_variant = {}

            # VariantName is the only required param
            temp_variant["VariantName"] = production_variant["VariantName"]

            if production_variant.get("InitialInstanceCount", None):
                temp_variant["CurrentInstanceCount"] = production_variant[
                    "InitialInstanceCount"
                ]
                temp_variant["DesiredInstanceCount"] = production_variant[
                    "InitialInstanceCount"
                ]

            if production_variant.get("InitialVariantWeight", None):
                temp_variant["CurrentWeight"] = production_variant[
                    "InitialVariantWeight"
                ]
                temp_variant["DesiredWeight"] = production_variant[
                    "InitialVariantWeight"
                ]

            if production_variant.get("ServerlessConfig", None):
                temp_variant["CurrentServerlessConfig"] = production_variant[
                    "ServerlessConfig"
                ]
                temp_variant["DesiredServerlessConfig"] = production_variant[
                    "ServerlessConfig"
                ]

            endpoint_variants.append(temp_variant)

        return endpoint_variants

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def response_create(self) -> Dict[str, str]:
        return {"EndpointArn": self.endpoint_arn}

    @staticmethod
    def arn_formatter(endpoint_name: str, account_id: str, region_name: str) -> str:
        return arn_formatter("endpoint", endpoint_name, account_id, region_name)

    @property
    def physical_resource_id(self) -> str:
        return self.endpoint_arn

    @classmethod
    def has_cfn_attr(cls, attr: str) -> bool:
        return attr in ["EndpointName"]

    def get_cfn_attribute(self, attribute_name: str) -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-endpoint.html#aws-resource-sagemaker-endpoint-return-values
        from moto.cloudformation.exceptions import UnformattedGetAttTemplateException

        if attribute_name == "EndpointName":
            return self.endpoint_name
        raise UnformattedGetAttTemplateException()

    @staticmethod
    def cloudformation_name_type() -> str:
        return ""

    @staticmethod
    def cloudformation_type() -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-endpoint.html
        return "AWS::SageMaker::Endpoint"

    @classmethod
    def create_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
        **kwargs: Any,
    ) -> "FakeEndpoint":
        sagemaker_backend = sagemaker_backends[account_id][region_name]

        # Get required properties from provided CloudFormation template
        properties = cloudformation_json["Properties"]
        endpoint_config_name = properties["EndpointConfigName"]

        endpoint = sagemaker_backend.create_endpoint(
            endpoint_name=resource_name,
            endpoint_config_name=endpoint_config_name,
            tags=properties.get("Tags", []),
        )
        return endpoint

    @classmethod
    def update_from_cloudformation_json(  # type: ignore[misc]
        cls,
        original_resource: Any,
        new_resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> "FakeEndpoint":
        # Changes to the Endpoint will not change resource name
        cls.delete_from_cloudformation_json(
            original_resource.endpoint_arn, cloudformation_json, account_id, region_name
        )
        new_resource = cls.create_from_cloudformation_json(
            original_resource.endpoint_name,
            cloudformation_json,
            account_id,
            region_name,
        )
        return new_resource

    @classmethod
    def delete_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> None:
        # Get actual name because resource_name actually provides the ARN
        # since the Physical Resource ID is the ARN despite SageMaker
        # using the name for most of its operations.
        endpoint_name = resource_name.split("/")[-1]

        sagemaker_backends[account_id][region_name].delete_endpoint(endpoint_name)


class FakeEndpointConfig(BaseObject, CloudFormationModel):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        endpoint_config_name: str,
        production_variants: List[Dict[str, Any]],
        data_capture_config: Dict[str, Any],
        tags: List[Dict[str, Any]],
        kms_key_id: str,
    ):
        self.validate_production_variants(production_variants)

        self.endpoint_config_name = endpoint_config_name
        self.endpoint_config_arn = FakeEndpointConfig.arn_formatter(
            endpoint_config_name, account_id, region_name
        )
        self.production_variants = production_variants or []
        self.data_capture_config = data_capture_config or {}
        self.tags = tags or []
        self.kms_key_id = kms_key_id
        self.creation_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    def validate_production_variants(
        self, production_variants: List[Dict[str, Any]]
    ) -> None:
        for production_variant in production_variants:
            if "InstanceType" in production_variant.keys():
                self.validate_instance_type(production_variant["InstanceType"])
            elif "ServerlessConfig" in production_variant.keys():
                self.validate_serverless_config(production_variant["ServerlessConfig"])
            else:
                message = f"Invalid Keys for ProductionVariant: received {production_variant.keys()} but expected it to contain one of {['InstanceType', 'ServerlessConfig']}"
                raise ValidationError(message=message)

    def validate_serverless_config(self, serverless_config: Dict[str, Any]) -> None:
        VALID_SERVERLESS_MEMORY_SIZE = [1024, 2048, 3072, 4096, 5120, 6144]
        if not validators.is_one_of(
            serverless_config["MemorySizeInMB"], VALID_SERVERLESS_MEMORY_SIZE
        ):
            message = f"Value '{serverless_config['MemorySizeInMB']}' at 'MemorySizeInMB' failed to satisfy constraint: Member must satisfy enum value set: {VALID_SERVERLESS_MEMORY_SIZE}"
            raise ValidationError(message=message)

    def validate_instance_type(self, instance_type: str) -> None:
        VALID_INSTANCE_TYPES = [
            "ml.r5d.12xlarge",
            "ml.r5.12xlarge",
            "ml.p2.xlarge",
            "ml.m5.4xlarge",
            "ml.m4.16xlarge",
            "ml.r5d.24xlarge",
            "ml.r5.24xlarge",
            "ml.p3.16xlarge",
            "ml.m5d.xlarge",
            "ml.m5.large",
            "ml.t2.xlarge",
            "ml.p2.16xlarge",
            "ml.m5d.12xlarge",
            "ml.inf1.2xlarge",
            "ml.m5d.24xlarge",
            "ml.c4.2xlarge",
            "ml.c5.2xlarge",
            "ml.c4.4xlarge",
            "ml.inf1.6xlarge",
            "ml.c5d.2xlarge",
            "ml.c5.4xlarge",
            "ml.g4dn.xlarge",
            "ml.g4dn.12xlarge",
            "ml.c5d.4xlarge",
            "ml.g4dn.2xlarge",
            "ml.c4.8xlarge",
            "ml.c4.large",
            "ml.c5d.xlarge",
            "ml.c5.large",
            "ml.g4dn.4xlarge",
            "ml.c5.9xlarge",
            "ml.g4dn.16xlarge",
            "ml.c5d.large",
            "ml.c5.xlarge",
            "ml.c5d.9xlarge",
            "ml.c4.xlarge",
            "ml.inf1.xlarge",
            "ml.g4dn.8xlarge",
            "ml.inf1.24xlarge",
            "ml.m5d.2xlarge",
            "ml.t2.2xlarge",
            "ml.c5d.18xlarge",
            "ml.m5d.4xlarge",
            "ml.t2.medium",
            "ml.c5.18xlarge",
            "ml.r5d.2xlarge",
            "ml.r5.2xlarge",
            "ml.p3.2xlarge",
            "ml.m5d.large",
            "ml.m5.xlarge",
            "ml.m4.10xlarge",
            "ml.t2.large",
            "ml.r5d.4xlarge",
            "ml.r5.4xlarge",
            "ml.m5.12xlarge",
            "ml.m4.xlarge",
            "ml.m5.24xlarge",
            "ml.m4.2xlarge",
            "ml.p2.8xlarge",
            "ml.m5.2xlarge",
            "ml.r5d.xlarge",
            "ml.r5d.large",
            "ml.r5.xlarge",
            "ml.r5.large",
            "ml.p3.8xlarge",
            "ml.m4.4xlarge",
        ]
        if not validators.is_one_of(instance_type, VALID_INSTANCE_TYPES):
            message = f"Value '{instance_type}' at 'instanceType' failed to satisfy constraint: Member must satisfy enum value set: {VALID_INSTANCE_TYPES}"
            raise ValidationError(message=message)

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def response_create(self) -> Dict[str, str]:
        return {"EndpointConfigArn": self.endpoint_config_arn}

    @staticmethod
    def arn_formatter(
        endpoint_config_name: str, account_id: str, region_name: str
    ) -> str:
        return arn_formatter(
            "endpoint-config", endpoint_config_name, account_id, region_name
        )

    @property
    def physical_resource_id(self) -> str:
        return self.endpoint_config_arn

    @classmethod
    def has_cfn_attr(cls, attr: str) -> bool:
        return attr in ["EndpointConfigName"]

    def get_cfn_attribute(self, attribute_name: str) -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-endpointconfig.html#aws-resource-sagemaker-endpointconfig-return-values
        from moto.cloudformation.exceptions import UnformattedGetAttTemplateException

        if attribute_name == "EndpointConfigName":
            return self.endpoint_config_name
        raise UnformattedGetAttTemplateException()

    @staticmethod
    def cloudformation_name_type() -> str:
        return ""

    @staticmethod
    def cloudformation_type() -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-endpointconfig.html
        return "AWS::SageMaker::EndpointConfig"

    @classmethod
    def create_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
        **kwargs: Any,
    ) -> "FakeEndpointConfig":
        sagemaker_backend = sagemaker_backends[account_id][region_name]

        # Get required properties from provided CloudFormation template
        properties = cloudformation_json["Properties"]
        production_variants = properties["ProductionVariants"]

        endpoint_config = sagemaker_backend.create_endpoint_config(
            endpoint_config_name=resource_name,
            production_variants=production_variants,
            data_capture_config=properties.get("DataCaptureConfig", {}),
            kms_key_id=properties.get("KmsKeyId"),
            tags=properties.get("Tags", []),
        )
        return endpoint_config

    @classmethod
    def update_from_cloudformation_json(  # type: ignore[misc]
        cls,
        original_resource: Any,
        new_resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> "FakeEndpointConfig":
        # Most changes to the endpoint config will change resource name for EndpointConfigs
        cls.delete_from_cloudformation_json(
            original_resource.endpoint_config_arn,
            cloudformation_json,
            account_id,
            region_name,
        )
        new_resource = cls.create_from_cloudformation_json(
            new_resource_name, cloudformation_json, account_id, region_name
        )
        return new_resource

    @classmethod
    def delete_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> None:
        # Get actual name because resource_name actually provides the ARN
        # since the Physical Resource ID is the ARN despite SageMaker
        # using the name for most of its operations.
        endpoint_config_name = resource_name.split("/")[-1]

        sagemaker_backends[account_id][region_name].delete_endpoint_config(
            endpoint_config_name
        )


class Model(BaseObject, CloudFormationModel):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        model_name: str,
        execution_role_arn: str,
        primary_container: Dict[str, Any],
        vpc_config: Dict[str, Any],
        containers: Optional[List[Dict[str, Any]]] = None,
        tags: Optional[List[Dict[str, str]]] = None,
    ):
        self.model_name = model_name
        self.creation_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.containers = containers or []
        self.tags = tags or []
        self.enable_network_isolation = False
        self.vpc_config = vpc_config
        self.primary_container = primary_container
        self.execution_role_arn = execution_role_arn or "arn:test"
        self.model_arn = arn_formatter(
            "model", self.model_name, account_id, region_name
        )

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def response_create(self) -> Dict[str, str]:
        return {"ModelArn": self.model_arn}

    @property
    def physical_resource_id(self) -> str:
        return self.model_arn

    @classmethod
    def has_cfn_attr(cls, attr: str) -> bool:
        return attr in ["ModelName"]

    def get_cfn_attribute(self, attribute_name: str) -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html#aws-resource-sagemaker-model-return-values
        from moto.cloudformation.exceptions import UnformattedGetAttTemplateException

        if attribute_name == "ModelName":
            return self.model_name
        raise UnformattedGetAttTemplateException()

    @staticmethod
    def cloudformation_name_type() -> str:
        return ""

    @staticmethod
    def cloudformation_type() -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-model.html
        return "AWS::SageMaker::Model"

    @classmethod
    def create_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
        **kwargs: Any,
    ) -> "Model":
        sagemaker_backend = sagemaker_backends[account_id][region_name]

        # Get required properties from provided CloudFormation template
        properties = cloudformation_json["Properties"]
        execution_role_arn = properties["ExecutionRoleArn"]
        primary_container = properties["PrimaryContainer"]

        model = sagemaker_backend.create_model(
            model_name=resource_name,
            execution_role_arn=execution_role_arn,
            primary_container=primary_container,
            vpc_config=properties.get("VpcConfig", {}),
            containers=properties.get("Containers", []),
            tags=properties.get("Tags", []),
        )
        return model

    @classmethod
    def update_from_cloudformation_json(  # type: ignore[misc]
        cls,
        original_resource: Any,
        new_resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> "Model":
        # Most changes to the model will change resource name for Models
        cls.delete_from_cloudformation_json(
            original_resource.model_arn, cloudformation_json, account_id, region_name
        )
        new_resource = cls.create_from_cloudformation_json(
            new_resource_name, cloudformation_json, account_id, region_name
        )
        return new_resource

    @classmethod
    def delete_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> None:
        # Get actual name because resource_name actually provides the ARN
        # since the Physical Resource ID is the ARN despite SageMaker
        # using the name for most of its operations.
        model_name = resource_name.split("/")[-1]

        sagemaker_backends[account_id][region_name].delete_model(model_name)


class VpcConfig(BaseObject):
    def __init__(self, security_group_ids: List[str], subnets: List[str]):
        self.security_group_ids = security_group_ids
        self.subnets = subnets

    @property
    def response_object(self) -> Dict[str, List[str]]:
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }


class Container(BaseObject):
    def __init__(self, **kwargs: Any):
        self.container_hostname = kwargs.get("container_hostname", "localhost")
        self.model_data_url = kwargs.get("data_url", "")
        self.model_package_name = kwargs.get("package_name", "pkg")
        self.image = kwargs.get("image", "")
        self.environment = kwargs.get("environment", {})

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }


class FakeSagemakerNotebookInstance(CloudFormationModel):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        notebook_instance_name: str,
        instance_type: str,
        role_arn: str,
        subnet_id: Optional[str],
        security_group_ids: Optional[List[str]],
        kms_key_id: Optional[str],
        tags: Optional[List[Dict[str, str]]],
        lifecycle_config_name: Optional[str],
        direct_internet_access: str,
        volume_size_in_gb: int,
        accelerator_types: Optional[List[str]],
        default_code_repository: Optional[str],
        additional_code_repositories: Optional[List[str]],
        root_access: Optional[str],
    ):
        self.validate_volume_size_in_gb(volume_size_in_gb)
        self.validate_instance_type(instance_type)

        self.region_name = region_name
        self.notebook_instance_name = notebook_instance_name
        self.instance_type = instance_type
        self.role_arn = role_arn
        self.subnet_id = subnet_id
        self.security_group_ids = security_group_ids
        self.kms_key_id = kms_key_id
        self.tags = tags or []
        self.lifecycle_config_name = lifecycle_config_name
        self.direct_internet_access = direct_internet_access
        self.volume_size_in_gb = volume_size_in_gb
        self.accelerator_types = accelerator_types
        self.default_code_repository = default_code_repository
        self.additional_code_repositories = additional_code_repositories
        self.root_access = root_access
        self.status: Optional[str] = None
        self.creation_time = self.last_modified_time = datetime.now()
        self.arn = arn_formatter(
            "notebook-instance", notebook_instance_name, account_id, region_name
        )
        self.start()

    def validate_volume_size_in_gb(self, volume_size_in_gb: int) -> None:
        if not validators.is_integer_between(volume_size_in_gb, mn=5, optional=True):
            message = "Invalid range for parameter VolumeSizeInGB, value: {}, valid range: 5-inf"
            raise ValidationError(message=message)

    def validate_instance_type(self, instance_type: str) -> None:
        VALID_INSTANCE_TYPES = [
            "ml.p2.xlarge",
            "ml.m5.4xlarge",
            "ml.m4.16xlarge",
            "ml.t3.xlarge",
            "ml.p3.16xlarge",
            "ml.t2.xlarge",
            "ml.p2.16xlarge",
            "ml.c4.2xlarge",
            "ml.c5.2xlarge",
            "ml.c4.4xlarge",
            "ml.c5d.2xlarge",
            "ml.c5.4xlarge",
            "ml.c5d.4xlarge",
            "ml.c4.8xlarge",
            "ml.c5d.xlarge",
            "ml.c5.9xlarge",
            "ml.c5.xlarge",
            "ml.c5d.9xlarge",
            "ml.c4.xlarge",
            "ml.t2.2xlarge",
            "ml.c5d.18xlarge",
            "ml.t3.2xlarge",
            "ml.t3.medium",
            "ml.t2.medium",
            "ml.c5.18xlarge",
            "ml.p3.2xlarge",
            "ml.m5.xlarge",
            "ml.m4.10xlarge",
            "ml.t2.large",
            "ml.m5.12xlarge",
            "ml.m4.xlarge",
            "ml.t3.large",
            "ml.m5.24xlarge",
            "ml.m4.2xlarge",
            "ml.p2.8xlarge",
            "ml.m5.2xlarge",
            "ml.p3.8xlarge",
            "ml.m4.4xlarge",
        ]
        if not validators.is_one_of(instance_type, VALID_INSTANCE_TYPES):
            message = f"Value '{instance_type}' at 'instanceType' failed to satisfy constraint: Member must satisfy enum value set: {VALID_INSTANCE_TYPES}"
            raise ValidationError(message=message)

    @property
    def url(self) -> str:
        return (
            f"{self.notebook_instance_name}.notebook.{self.region_name}.sagemaker.aws"
        )

    def start(self) -> None:
        self.status = "InService"

    @property
    def is_deletable(self) -> bool:
        return self.status in ["Stopped", "Failed"]

    def stop(self) -> None:
        self.status = "Stopped"

    @property
    def physical_resource_id(self) -> str:
        return self.arn

    @classmethod
    def has_cfn_attr(cls, attr: str) -> bool:
        return attr in ["NotebookInstanceName"]

    def get_cfn_attribute(self, attribute_name: str) -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-notebookinstance.html#aws-resource-sagemaker-notebookinstance-return-values
        from moto.cloudformation.exceptions import UnformattedGetAttTemplateException

        if attribute_name == "NotebookInstanceName":
            return self.notebook_instance_name
        raise UnformattedGetAttTemplateException()

    @staticmethod
    def cloudformation_name_type() -> str:
        return ""

    @staticmethod
    def cloudformation_type() -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-notebookinstance.html
        return "AWS::SageMaker::NotebookInstance"

    @classmethod
    def create_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
        **kwargs: Any,
    ) -> "FakeSagemakerNotebookInstance":
        # Get required properties from provided CloudFormation template
        properties = cloudformation_json["Properties"]
        instance_type = properties["InstanceType"]
        role_arn = properties["RoleArn"]

        notebook = sagemaker_backends[account_id][region_name].create_notebook_instance(
            notebook_instance_name=resource_name,
            instance_type=instance_type,
            role_arn=role_arn,
        )
        return notebook

    @classmethod
    def update_from_cloudformation_json(  # type: ignore[misc]
        cls,
        original_resource: Any,
        new_resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> "FakeSagemakerNotebookInstance":
        # Operations keep same resource name so delete old and create new to mimic update
        cls.delete_from_cloudformation_json(
            original_resource.arn, cloudformation_json, account_id, region_name
        )
        new_resource = cls.create_from_cloudformation_json(
            original_resource.notebook_instance_name,
            cloudformation_json,
            account_id,
            region_name,
        )
        return new_resource

    @classmethod
    def delete_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> None:
        # Get actual name because resource_name actually provides the ARN
        # since the Physical Resource ID is the ARN despite SageMaker
        # using the name for most of its operations.
        notebook_instance_name = resource_name.split("/")[-1]

        backend = sagemaker_backends[account_id][region_name]
        backend.stop_notebook_instance(notebook_instance_name)
        backend.delete_notebook_instance(notebook_instance_name)


class FakeSageMakerNotebookInstanceLifecycleConfig(BaseObject, CloudFormationModel):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        notebook_instance_lifecycle_config_name: str,
        on_create: List[Dict[str, str]],
        on_start: List[Dict[str, str]],
    ):
        self.region_name = region_name
        self.notebook_instance_lifecycle_config_name = (
            notebook_instance_lifecycle_config_name
        )
        self.on_create = on_create
        self.on_start = on_start
        self.creation_time = self.last_modified_time = datetime.now().strftime(
            "%Y-%m-%d %H:%M:%S"
        )
        self.notebook_instance_lifecycle_config_arn = (
            FakeSageMakerNotebookInstanceLifecycleConfig.arn_formatter(
                self.notebook_instance_lifecycle_config_name, account_id, region_name
            )
        )

    @staticmethod
    def arn_formatter(name: str, account_id: str, region_name: str) -> str:
        return arn_formatter(
            "notebook-instance-lifecycle-configuration", name, account_id, region_name
        )

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def physical_resource_id(self) -> str:
        return self.notebook_instance_lifecycle_config_arn

    @classmethod
    def has_cfn_attr(cls, attr: str) -> bool:
        return attr in ["NotebookInstanceLifecycleConfigName"]

    def get_cfn_attribute(self, attribute_name: str) -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-notebookinstancelifecycleconfig.html#aws-resource-sagemaker-notebookinstancelifecycleconfig-return-values
        from moto.cloudformation.exceptions import UnformattedGetAttTemplateException

        if attribute_name == "NotebookInstanceLifecycleConfigName":
            return self.notebook_instance_lifecycle_config_name
        raise UnformattedGetAttTemplateException()

    @staticmethod
    def cloudformation_name_type() -> str:
        return ""

    @staticmethod
    def cloudformation_type() -> str:
        # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-notebookinstancelifecycleconfig.html
        return "AWS::SageMaker::NotebookInstanceLifecycleConfig"

    @classmethod
    def create_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
        **kwargs: Any,
    ) -> "FakeSageMakerNotebookInstanceLifecycleConfig":
        properties = cloudformation_json["Properties"]

        config = sagemaker_backends[account_id][
            region_name
        ].create_notebook_instance_lifecycle_config(
            notebook_instance_lifecycle_config_name=resource_name,
            on_create=properties.get("OnCreate"),
            on_start=properties.get("OnStart"),
        )
        return config

    @classmethod
    def update_from_cloudformation_json(  # type: ignore[misc]
        cls,
        original_resource: Any,
        new_resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> "FakeSageMakerNotebookInstanceLifecycleConfig":
        # Operations keep same resource name so delete old and create new to mimic update
        cls.delete_from_cloudformation_json(
            original_resource.notebook_instance_lifecycle_config_arn,
            cloudformation_json,
            account_id,
            region_name,
        )
        new_resource = cls.create_from_cloudformation_json(
            original_resource.notebook_instance_lifecycle_config_name,
            cloudformation_json,
            account_id,
            region_name,
        )
        return new_resource

    @classmethod
    def delete_from_cloudformation_json(  # type: ignore[misc]
        cls,
        resource_name: str,
        cloudformation_json: Any,
        account_id: str,
        region_name: str,
    ) -> None:
        # Get actual name because resource_name actually provides the ARN
        # since the Physical Resource ID is the ARN despite SageMaker
        # using the name for most of its operations.
        config_name = resource_name.split("/")[-1]

        backend = sagemaker_backends[account_id][region_name]
        backend.delete_notebook_instance_lifecycle_config(config_name)


class SageMakerModelBackend(BaseBackend):
    def __init__(self, region_name: str, account_id: str):
        super().__init__(region_name, account_id)
        self._models: Dict[str, Model] = {}
        self.notebook_instances: Dict[str, FakeSagemakerNotebookInstance] = {}
        self.endpoint_configs: Dict[str, FakeEndpointConfig] = {}
        self.endpoints: Dict[str, FakeEndpoint] = {}
        self.experiments: Dict[str, FakeExperiment] = {}
        self.pipelines: Dict[str, FakePipeline] = {}
        self.pipeline_executions: Dict[str, FakePipelineExecution] = {}
        self.processing_jobs: Dict[str, FakeProcessingJob] = {}
        self.trials: Dict[str, FakeTrial] = {}
        self.trial_components: Dict[str, FakeTrialComponent] = {}
        self.training_jobs: Dict[str, FakeTrainingJob] = {}
        self.notebook_instance_lifecycle_configurations: Dict[
            str, FakeSageMakerNotebookInstanceLifecycleConfig
        ] = {}

    @staticmethod
    def default_vpc_endpoint_service(
        service_region: str, zones: List[str]
    ) -> List[Dict[str, str]]:
        """Default VPC endpoint services."""
        api_service = BaseBackend.default_vpc_endpoint_service_factory(
            service_region, zones, "api.sagemaker", special_service_name="sagemaker.api"
        )

        notebook_service_id = f"vpce-svc-{BaseBackend.vpce_random_number()}"
        studio_service_id = f"vpce-svc-{BaseBackend.vpce_random_number()}"

        notebook_service = {
            "AcceptanceRequired": False,
            "AvailabilityZones": zones,
            "BaseEndpointDnsNames": [
                f"{notebook_service_id}.{service_region}.vpce.amazonaws.com",
                f"notebook.{service_region}.vpce.sagemaker.aws",
            ],
            "ManagesVpcEndpoints": False,
            "Owner": "amazon",
            "PrivateDnsName": f"*.notebook.{service_region}.sagemaker.aws",
            "PrivateDnsNameVerificationState": "verified",
            "PrivateDnsNames": [
                {"PrivateDnsName": f"*.notebook.{service_region}.sagemaker.aws"}
            ],
            "ServiceId": notebook_service_id,
            "ServiceName": f"aws.sagemaker.{service_region}.notebook",
            "ServiceType": [{"ServiceType": "Interface"}],
            "Tags": [],
            "VpcEndpointPolicySupported": True,
        }
        studio_service = {
            "AcceptanceRequired": False,
            "AvailabilityZones": zones,
            "BaseEndpointDnsNames": [
                f"{studio_service_id}.{service_region}.vpce.amazonaws.com",
                f"studio.{service_region}.vpce.sagemaker.aws",
            ],
            "ManagesVpcEndpoints": False,
            "Owner": "amazon",
            "PrivateDnsName": f"*.studio.{service_region}.sagemaker.aws",
            "PrivateDnsNameVerificationState": "verified",
            "PrivateDnsNames": [
                {"PrivateDnsName": f"*.studio.{service_region}.sagemaker.aws"}
            ],
            "ServiceId": studio_service_id,
            "ServiceName": f"aws.sagemaker.{service_region}.studio",
            "ServiceType": [{"ServiceType": "Interface"}],
            "Tags": [],
            "VpcEndpointPolicySupported": True,
        }
        return api_service + [notebook_service, studio_service]

    def create_model(
        self,
        model_name: str,
        execution_role_arn: str,
        primary_container: Optional[Dict[str, Any]],
        vpc_config: Optional[Dict[str, Any]],
        containers: Optional[List[Dict[str, Any]]],
        tags: Optional[List[Dict[str, str]]],
    ) -> Model:
        model_obj = Model(
            account_id=self.account_id,
            region_name=self.region_name,
            model_name=model_name,
            execution_role_arn=execution_role_arn,
            primary_container=primary_container or {},
            vpc_config=vpc_config or {},
            containers=containers or [],
            tags=tags or [],
        )

        self._models[model_name] = model_obj
        return model_obj

    def describe_model(self, model_name: str) -> Model:
        model = self._models.get(model_name)
        if model:
            return model
        arn = arn_formatter("model", model_name, self.account_id, self.region_name)
        raise ValidationError(message=f"Could not find model '{arn}'.")

    def list_models(self) -> Iterable[Model]:
        return self._models.values()

    def delete_model(self, model_name: str) -> None:
        for model in self._models.values():
            if model.model_name == model_name:
                self._models.pop(model.model_name)
                break
        else:
            raise MissingModel(model=model_name)

    def create_experiment(self, experiment_name: str) -> Dict[str, str]:
        experiment = FakeExperiment(
            account_id=self.account_id,
            region_name=self.region_name,
            experiment_name=experiment_name,
            tags=[],
        )
        self.experiments[experiment_name] = experiment
        return experiment.response_create

    def describe_experiment(self, experiment_name: str) -> Dict[str, Any]:
        experiment_data = self.experiments[experiment_name]
        return {
            "ExperimentName": experiment_data.experiment_name,
            "ExperimentArn": experiment_data.experiment_arn,
            "CreationTime": experiment_data.creation_time,
            "LastModifiedTime": experiment_data.last_modified_time,
        }

    def _get_resource_from_arn(self, arn: str) -> Any:
        resources = {
            "model": self._models,
            "notebook-instance": self.notebook_instances,
            "endpoint": self.endpoints,
            "endpoint-config": self.endpoint_configs,
            "training-job": self.training_jobs,
            "experiment": self.experiments,
            "experiment-trial": self.trials,
            "experiment-trial-component": self.trial_components,
            "processing-job": self.processing_jobs,
            "pipeline": self.pipelines,
        }
        target_resource, target_name = arn.split(":")[-1].split("/")
        try:
            resource = resources.get(target_resource).get(target_name)  # type: ignore
        except KeyError:
            message = f"Could not find {target_resource} with name {target_name}"
            raise ValidationError(message=message)
        return resource

    def add_tags(self, arn: str, tags: List[Dict[str, str]]) -> List[Dict[str, str]]:
        resource = self._get_resource_from_arn(arn)
        resource.tags.extend(tags)
        return resource.tags

    @paginate(pagination_model=PAGINATION_MODEL)
    def list_tags(self, arn: str) -> List[Dict[str, str]]:  # type: ignore[misc]
        resource = self._get_resource_from_arn(arn)
        return resource.tags

    def delete_tags(self, arn: str, tag_keys: List[str]) -> None:
        resource = self._get_resource_from_arn(arn)
        resource.tags = [tag for tag in resource.tags if tag["Key"] not in tag_keys]

    @paginate(pagination_model=PAGINATION_MODEL)
    def list_experiments(self) -> List["FakeExperiment"]:  # type: ignore[misc]
        return list(self.experiments.values())

    def search(self, resource: Any = None, search_expression: Any = None) -> Any:
        next_index = None

        valid_resources = [
            "Pipeline",
            "ModelPackageGroup",
            "TrainingJob",
            "ExperimentTrialComponent",
            "FeatureGroup",
            "Endpoint",
            "PipelineExecution",
            "Project",
            "ExperimentTrial",
            "Image",
            "ImageVersion",
            "ModelPackage",
            "Experiment",
        ]

        if resource not in valid_resources:
            raise AWSValidationException(
                f"An error occurred (ValidationException) when calling the Search operation: 1 validation error detected: Value '{resource}' at 'resource' failed to satisfy constraint: Member must satisfy enum value set: {valid_resources}"
            )

        def evaluate_search_expression(item: Any) -> bool:
            filters = None
            if search_expression is not None:
                filters = search_expression.get("Filters")

            if filters is not None:
                for f in filters:
                    if f["Operator"] == "Equals":
                        if f["Name"].startswith("Tags."):
                            key = f["Name"][5:]
                            value = f["Value"]

                            if (
                                len(
                                    [
                                        e
                                        for e in item.tags
                                        if e["Key"] == key and e["Value"] == value
                                    ]
                                )
                                == 0
                            ):
                                return False
                        if f["Name"] == "ExperimentName":
                            experiment_name = f["Value"]

                            if hasattr(item, "experiment_name"):
                                if getattr(item, "experiment_name") != experiment_name:
                                    return False
                            else:
                                raise ValidationError(
                                    message="Unknown property name: ExperimentName"
                                )

                        if f["Name"] == "TrialName":
                            raise AWSValidationException(
                                f"An error occurred (ValidationException) when calling the Search operation: Unknown property name: {f['Name']}"
                            )

                        if f["Name"] == "Parents.TrialName":
                            trial_name = f["Value"]

                            if getattr(item, "trial_name") != trial_name:
                                return False

            return True

        result: Dict[str, Any] = {
            "Results": [],
            "NextToken": str(next_index) if next_index is not None else None,
        }
        if resource == "Experiment":
            experiments_fetched = list(self.experiments.values())

            experiment_summaries = [
                {
                    "ExperimentName": experiment_data.experiment_name,
                    "ExperimentArn": experiment_data.experiment_arn,
                    "CreationTime": experiment_data.creation_time,
                    "LastModifiedTime": experiment_data.last_modified_time,
                }
                for experiment_data in experiments_fetched
                if evaluate_search_expression(experiment_data)
            ]

            for experiment_summary in experiment_summaries:
                result["Results"].append({"Experiment": experiment_summary})

        if resource == "ExperimentTrial":
            trials_fetched = list(self.trials.values())

            trial_summaries = [
                {
                    "TrialName": trial_data.trial_name,
                    "TrialArn": trial_data.trial_arn,
                    "CreationTime": trial_data.creation_time,
                    "LastModifiedTime": trial_data.last_modified_time,
                }
                for trial_data in trials_fetched
                if evaluate_search_expression(trial_data)
            ]

            for trial_summary in trial_summaries:
                result["Results"].append({"Trial": trial_summary})

        if resource == "ExperimentTrialComponent":
            trial_components_fetched = list(self.trial_components.values())

            trial_component_summaries = [
                {
                    "TrialComponentName": trial_component_data.trial_component_name,
                    "TrialComponentArn": trial_component_data.trial_component_arn,
                    "CreationTime": trial_component_data.creation_time,
                    "LastModifiedTime": trial_component_data.last_modified_time,
                }
                for trial_component_data in trial_components_fetched
                if evaluate_search_expression(trial_component_data)
            ]

            for trial_component_summary in trial_component_summaries:
                result["Results"].append({"TrialComponent": trial_component_summary})
        return result

    def delete_experiment(self, experiment_name: str) -> None:
        try:
            del self.experiments[experiment_name]
        except KeyError:
            arn = FakeTrial.arn_formatter(
                experiment_name, self.account_id, self.region_name
            )
            raise ValidationError(
                message=f"Could not find experiment configuration '{arn}'."
            )

    def create_trial(self, trial_name: str, experiment_name: str) -> Dict[str, str]:
        trial = FakeTrial(
            account_id=self.account_id,
            region_name=self.region_name,
            trial_name=trial_name,
            experiment_name=experiment_name,
            tags=[],
            trial_components=[],
        )
        self.trials[trial_name] = trial
        return trial.response_create

    def describe_trial(self, trial_name: str) -> Dict[str, Any]:
        try:
            return self.trials[trial_name].response_object
        except KeyError:
            arn = FakeTrial.arn_formatter(trial_name, self.account_id, self.region_name)
            raise ValidationError(message=f"Could not find trial '{arn}'.")

    def delete_trial(self, trial_name: str) -> None:
        try:
            del self.trials[trial_name]
        except KeyError:
            arn = FakeTrial.arn_formatter(trial_name, self.account_id, self.region_name)
            raise ValidationError(
                message=f"Could not find trial configuration '{arn}'."
            )

    @paginate(pagination_model=PAGINATION_MODEL)
    def list_trials(self, experiment_name: Optional[str] = None, trial_component_name: Optional[str] = None) -> List["FakeTrial"]:  # type: ignore[misc]
        trials_fetched = list(self.trials.values())

        def evaluate_filter_expression(trial_data: FakeTrial) -> bool:
            if experiment_name is not None:
                if trial_data.experiment_name != experiment_name:
                    return False

            if trial_component_name is not None:
                if trial_component_name not in trial_data.trial_components:
                    return False

            return True

        return [
            trial_data
            for trial_data in trials_fetched
            if evaluate_filter_expression(trial_data)
        ]

    def create_trial_component(
        self, trial_component_name: str, trial_name: str
    ) -> Dict[str, Any]:
        trial_component = FakeTrialComponent(
            account_id=self.account_id,
            region_name=self.region_name,
            trial_component_name=trial_component_name,
            trial_name=trial_name,
            tags=[],
        )
        self.trial_components[trial_component_name] = trial_component
        return trial_component.response_create

    def delete_trial_component(self, trial_component_name: str) -> None:
        try:
            del self.trial_components[trial_component_name]
        except KeyError:
            arn = FakeTrial.arn_formatter(
                trial_component_name, self.account_id, self.region_name
            )
            raise ValidationError(
                message=f"Could not find trial-component configuration '{arn}'."
            )

    def describe_trial_component(self, trial_component_name: str) -> Dict[str, Any]:
        try:
            return self.trial_components[trial_component_name].response_object
        except KeyError:
            arn = FakeTrialComponent.arn_formatter(
                trial_component_name, self.account_id, self.region_name
            )
            raise ValidationError(message=f"Could not find trial component '{arn}'.")

    def _update_trial_component_details(
        self, trial_component_name: str, details_json: str
    ) -> None:
        self.trial_components[trial_component_name].update(details_json)

    @paginate(pagination_model=PAGINATION_MODEL)
    def list_trial_components(self, trial_name: Optional[str] = None) -> List["FakeTrialComponent"]:  # type: ignore[misc]
        trial_components_fetched = list(self.trial_components.values())

        return [
            trial_component_data
            for trial_component_data in trial_components_fetched
            if trial_name is None or trial_component_data.trial_name == trial_name
        ]

    def associate_trial_component(
        self, trial_name: str, trial_component_name: str
    ) -> Dict[str, str]:
        if trial_name in self.trials.keys():
            self.trials[trial_name].trial_components.extend([trial_component_name])
        else:
            raise ResourceNotFound(
                message=f"Trial 'arn:aws:sagemaker:{self.region_name}:{self.account_id}:experiment-trial/{trial_name}' does not exist."
            )

        if trial_component_name in self.trial_components.keys():
            self.trial_components[trial_component_name].trial_name = trial_name

        return {
            "TrialComponentArn": self.trial_components[
                trial_component_name
            ].trial_component_arn,
            "TrialArn": self.trials[trial_name].trial_arn,
        }

    def disassociate_trial_component(
        self, trial_name: str, trial_component_name: str
    ) -> Dict[str, str]:
        if trial_component_name in self.trial_components.keys():
            self.trial_components[trial_component_name].trial_name = None

        if trial_name in self.trials.keys():
            self.trials[trial_name].trial_components = list(
                filter(
                    lambda x: x != trial_component_name,  # type: ignore
                    self.trials[trial_name].trial_components,
                )
            )

        return {
            "TrialComponentArn": f"arn:aws:sagemaker:{self.region_name}:{self.account_id}:experiment-trial-component/{trial_component_name}",
            "TrialArn": f"arn:aws:sagemaker:{self.region_name}:{self.account_id}:experiment-trial/{trial_name}",
        }

    def create_notebook_instance(
        self,
        notebook_instance_name: str,
        instance_type: str,
        role_arn: str,
        subnet_id: Optional[str] = None,
        security_group_ids: Optional[List[str]] = None,
        kms_key_id: Optional[str] = None,
        tags: Optional[List[Dict[str, str]]] = None,
        lifecycle_config_name: Optional[str] = None,
        direct_internet_access: str = "Enabled",
        volume_size_in_gb: int = 5,
        accelerator_types: Optional[List[str]] = None,
        default_code_repository: Optional[str] = None,
        additional_code_repositories: Optional[List[str]] = None,
        root_access: Optional[str] = None,
    ) -> FakeSagemakerNotebookInstance:
        self._validate_unique_notebook_instance_name(notebook_instance_name)

        notebook_instance = FakeSagemakerNotebookInstance(
            account_id=self.account_id,
            region_name=self.region_name,
            notebook_instance_name=notebook_instance_name,
            instance_type=instance_type,
            role_arn=role_arn,
            subnet_id=subnet_id,
            security_group_ids=security_group_ids,
            kms_key_id=kms_key_id,
            tags=tags,
            lifecycle_config_name=lifecycle_config_name,
            direct_internet_access=direct_internet_access
            if direct_internet_access is not None
            else "Enabled",
            volume_size_in_gb=volume_size_in_gb if volume_size_in_gb is not None else 5,
            accelerator_types=accelerator_types,
            default_code_repository=default_code_repository,
            additional_code_repositories=additional_code_repositories,
            root_access=root_access,
        )
        self.notebook_instances[notebook_instance_name] = notebook_instance
        return notebook_instance

    def _validate_unique_notebook_instance_name(
        self, notebook_instance_name: str
    ) -> None:
        if notebook_instance_name in self.notebook_instances:
            duplicate_arn = self.notebook_instances[notebook_instance_name].arn
            message = f"Cannot create a duplicate Notebook Instance ({duplicate_arn})"
            raise ValidationError(message=message)

    def get_notebook_instance(
        self, notebook_instance_name: str
    ) -> FakeSagemakerNotebookInstance:
        try:
            return self.notebook_instances[notebook_instance_name]
        except KeyError:
            raise ValidationError(message="RecordNotFound")

    def start_notebook_instance(self, notebook_instance_name: str) -> None:
        notebook_instance = self.get_notebook_instance(notebook_instance_name)
        notebook_instance.start()

    def stop_notebook_instance(self, notebook_instance_name: str) -> None:
        notebook_instance = self.get_notebook_instance(notebook_instance_name)
        notebook_instance.stop()

    def delete_notebook_instance(self, notebook_instance_name: str) -> None:
        notebook_instance = self.get_notebook_instance(notebook_instance_name)
        if not notebook_instance.is_deletable:
            message = f"Status ({notebook_instance.status}) not in ([Stopped, Failed]). Unable to transition to (Deleting) for Notebook Instance ({notebook_instance.arn})"
            raise ValidationError(message=message)
        del self.notebook_instances[notebook_instance_name]

    def create_notebook_instance_lifecycle_config(
        self,
        notebook_instance_lifecycle_config_name: str,
        on_create: List[Dict[str, str]],
        on_start: List[Dict[str, str]],
    ) -> FakeSageMakerNotebookInstanceLifecycleConfig:
        if (
            notebook_instance_lifecycle_config_name
            in self.notebook_instance_lifecycle_configurations
        ):
            arn = FakeSageMakerNotebookInstanceLifecycleConfig.arn_formatter(
                notebook_instance_lifecycle_config_name,
                self.account_id,
                self.region_name,
            )
            message = f"Unable to create Notebook Instance Lifecycle Config {arn}. (Details: Notebook Instance Lifecycle Config already exists.)"
            raise ValidationError(message=message)
        lifecycle_config = FakeSageMakerNotebookInstanceLifecycleConfig(
            account_id=self.account_id,
            region_name=self.region_name,
            notebook_instance_lifecycle_config_name=notebook_instance_lifecycle_config_name,
            on_create=on_create,
            on_start=on_start,
        )
        self.notebook_instance_lifecycle_configurations[
            notebook_instance_lifecycle_config_name
        ] = lifecycle_config
        return lifecycle_config

    def describe_notebook_instance_lifecycle_config(
        self, notebook_instance_lifecycle_config_name: str
    ) -> Dict[str, Any]:
        try:
            return self.notebook_instance_lifecycle_configurations[
                notebook_instance_lifecycle_config_name
            ].response_object
        except KeyError:
            arn = FakeSageMakerNotebookInstanceLifecycleConfig.arn_formatter(
                notebook_instance_lifecycle_config_name,
                self.account_id,
                self.region_name,
            )
            message = f"Unable to describe Notebook Instance Lifecycle Config '{arn}'. (Details: Notebook Instance Lifecycle Config does not exist.)"
            raise ValidationError(message=message)

    def delete_notebook_instance_lifecycle_config(
        self, notebook_instance_lifecycle_config_name: str
    ) -> None:
        try:
            del self.notebook_instance_lifecycle_configurations[
                notebook_instance_lifecycle_config_name
            ]
        except KeyError:
            arn = FakeSageMakerNotebookInstanceLifecycleConfig.arn_formatter(
                notebook_instance_lifecycle_config_name,
                self.account_id,
                self.region_name,
            )
            message = f"Unable to delete Notebook Instance Lifecycle Config '{arn}'. (Details: Notebook Instance Lifecycle Config does not exist.)"
            raise ValidationError(message=message)

    def create_endpoint_config(
        self,
        endpoint_config_name: str,
        production_variants: List[Dict[str, Any]],
        data_capture_config: Dict[str, Any],
        tags: List[Dict[str, str]],
        kms_key_id: str,
    ) -> FakeEndpointConfig:
        endpoint_config = FakeEndpointConfig(
            account_id=self.account_id,
            region_name=self.region_name,
            endpoint_config_name=endpoint_config_name,
            production_variants=production_variants,
            data_capture_config=data_capture_config,
            tags=tags,
            kms_key_id=kms_key_id,
        )
        self.validate_production_variants(production_variants)

        self.endpoint_configs[endpoint_config_name] = endpoint_config
        return endpoint_config

    def validate_production_variants(
        self, production_variants: List[Dict[str, Any]]
    ) -> None:
        for production_variant in production_variants:
            if production_variant["ModelName"] not in self._models:
                arn = arn_formatter(
                    "model",
                    production_variant["ModelName"],
                    self.account_id,
                    self.region_name,
                )
                raise ValidationError(message=f"Could not find model '{arn}'.")

    def describe_endpoint_config(self, endpoint_config_name: str) -> Dict[str, Any]:
        try:
            return self.endpoint_configs[endpoint_config_name].response_object
        except KeyError:
            arn = FakeEndpointConfig.arn_formatter(
                endpoint_config_name, self.account_id, self.region_name
            )
            raise ValidationError(
                message=f"Could not find endpoint configuration '{arn}'."
            )

    def delete_endpoint_config(self, endpoint_config_name: str) -> None:
        try:
            del self.endpoint_configs[endpoint_config_name]
        except KeyError:
            arn = FakeEndpointConfig.arn_formatter(
                endpoint_config_name, self.account_id, self.region_name
            )
            raise ValidationError(
                message=f"Could not find endpoint configuration '{arn}'."
            )

    def create_endpoint(
        self, endpoint_name: str, endpoint_config_name: str, tags: List[Dict[str, str]]
    ) -> FakeEndpoint:
        try:
            endpoint_config = self.describe_endpoint_config(endpoint_config_name)
        except KeyError:
            arn = FakeEndpointConfig.arn_formatter(
                endpoint_config_name, self.account_id, self.region_name
            )
            raise ValidationError(message=f"Could not find endpoint_config '{arn}'.")

        endpoint = FakeEndpoint(
            account_id=self.account_id,
            region_name=self.region_name,
            endpoint_name=endpoint_name,
            endpoint_config_name=endpoint_config_name,
            production_variants=endpoint_config["ProductionVariants"],
            data_capture_config=endpoint_config["DataCaptureConfig"],
            tags=tags,
        )

        self.endpoints[endpoint_name] = endpoint
        return endpoint

    def describe_endpoint(self, endpoint_name: str) -> Dict[str, Any]:
        try:
            return self.endpoints[endpoint_name].response_object
        except KeyError:
            arn = FakeEndpoint.arn_formatter(
                endpoint_name, self.account_id, self.region_name
            )
            raise ValidationError(message=f"Could not find endpoint '{arn}'.")

    def delete_endpoint(self, endpoint_name: str) -> None:
        try:
            del self.endpoints[endpoint_name]
        except KeyError:
            arn = FakeEndpoint.arn_formatter(
                endpoint_name, self.account_id, self.region_name
            )
            raise ValidationError(message=f"Could not find endpoint '{arn}'.")

    def create_processing_job(
        self,
        app_specification: Dict[str, Any],
        experiment_config: Dict[str, str],
        network_config: Dict[str, Any],
        processing_inputs: List[Dict[str, Any]],
        processing_job_name: str,
        processing_output_config: Dict[str, Any],
        role_arn: str,
        tags: List[Dict[str, str]],
        stopping_condition: Dict[str, int],
    ) -> FakeProcessingJob:
        processing_job = FakeProcessingJob(
            app_specification=app_specification,
            experiment_config=experiment_config,
            network_config=network_config,
            processing_inputs=processing_inputs,
            processing_job_name=processing_job_name,
            processing_output_config=processing_output_config,
            account_id=self.account_id,
            region_name=self.region_name,
            role_arn=role_arn,
            stopping_condition=stopping_condition,
            tags=tags,
        )
        self.processing_jobs[processing_job_name] = processing_job
        return processing_job

    def describe_processing_job(self, processing_job_name: str) -> Dict[str, Any]:
        try:
            return self.processing_jobs[processing_job_name].response_object
        except KeyError:
            arn = FakeProcessingJob.arn_formatter(
                processing_job_name, self.account_id, self.region_name
            )
            raise ValidationError(message=f"Could not find processing job '{arn}'.")

    def create_pipeline(
        self,
        pipeline_name: str,
        pipeline_display_name: str,
        pipeline_definition: str,
        pipeline_definition_s3_location: Dict[str, Any],
        pipeline_description: str,
        role_arn: str,
        tags: List[Dict[str, str]],
        parallelism_configuration: Dict[str, int],
    ) -> FakePipeline:
        if not any([pipeline_definition, pipeline_definition_s3_location]):
            raise ValidationError(
                "An error occurred (ValidationException) when calling the CreatePipeline operation: Either "
                "Pipeline Definition or Pipeline Definition S3 location should be provided"
            )
        if all([pipeline_definition, pipeline_definition_s3_location]):
            raise ValidationError(
                "An error occurred (ValidationException) when calling the CreatePipeline operation: "
                "Both Pipeline Definition and Pipeline Definition S3 Location shouldn't be present"
            )

        if pipeline_name in self.pipelines:
            raise ValidationError(
                f"An error occurred (ValidationException) when calling the CreatePipeline operation: Pipeline names "
                f"must be unique within an AWS account and region. Pipeline with name ({pipeline_name}) already exists."
            )

        if pipeline_definition_s3_location:
            pipeline_definition = load_pipeline_definition_from_s3(  # type: ignore
                pipeline_definition_s3_location, self.account_id
            )

        pipeline = FakePipeline(
            pipeline_name,
            pipeline_display_name,
            pipeline_definition,
            pipeline_description,
            role_arn,
            tags,
            self.account_id,
            self.region_name,
            parallelism_configuration,
        )

        self.pipelines[pipeline_name] = pipeline
        return pipeline

    def delete_pipeline(self, pipeline_name: str) -> str:
        pipeline = get_pipeline_from_name(self.pipelines, pipeline_name)
        del self.pipelines[pipeline.pipeline_name]
        return pipeline.pipeline_arn

    def update_pipeline(self, pipeline_name: str, **kwargs: Any) -> str:
        pipeline = get_pipeline_from_name(self.pipelines, pipeline_name)
        if all(
            [
                kwargs.get("pipeline_definition"),
                kwargs.get("pipeline_definition_s3_location"),
            ]
        ):
            raise ValidationError(
                "An error occurred (ValidationException) when calling the UpdatePipeline operation: "
                "Both Pipeline Definition and Pipeline Definition S3 Location shouldn't be present"
            )

        for attr_key, attr_value in kwargs.items():
            if attr_value:
                if attr_key == "pipeline_definition_s3_location":
                    self.pipelines[
                        pipeline_name
                    ].pipeline_definition = load_pipeline_definition_from_s3(  # type: ignore
                        attr_value, self.account_id
                    )
                    continue
                setattr(self.pipelines[pipeline_name], attr_key, attr_value)

        return pipeline.pipeline_arn

    def start_pipeline_execution(
        self,
        pipeline_name: str,
        pipeline_execution_display_name: str,
        pipeline_parameters: List[Dict[str, Any]],
        pipeline_execution_description: str,
        parallelism_configuration: Dict[str, int],
        client_request_token: str,
    ) -> Dict[str, str]:
        pipeline = get_pipeline_from_name(self.pipelines, pipeline_name)
        execution_id = "".join(
            random.choices(string.ascii_lowercase + string.digits, k=12)
        )
        pipeline_execution_arn = arn_formatter(
            _type="pipeline",
            _id=f"{pipeline.pipeline_name}/execution/{execution_id}",
            account_id=self.account_id,
            region_name=self.region_name,
        )

        fake_pipeline_execution = FakePipelineExecution(
            pipeline_execution_arn=pipeline_execution_arn,
            pipeline_execution_display_name=pipeline_execution_display_name,
            pipeline_parameters=pipeline_parameters,
            pipeline_execution_description=pipeline_execution_description,
            pipeline_definition=pipeline.pipeline_definition,
            parallelism_configuration=parallelism_configuration
            or pipeline.parallelism_configuration,
            client_request_token=client_request_token,
        )

        self.pipelines[pipeline_name].pipeline_executions[
            pipeline_execution_arn
        ] = fake_pipeline_execution
        self.pipelines[
            pipeline_name
        ].last_execution_time = fake_pipeline_execution.start_time

        return {"PipelineExecutionArn": pipeline_execution_arn}

    def list_pipeline_executions(self, pipeline_name: str) -> Dict[str, Any]:
        pipeline = get_pipeline_from_name(self.pipelines, pipeline_name)
        return {
            "PipelineExecutionSummaries": [
                {
                    "PipelineExecutionArn": pipeline_execution_arn,
                    "StartTime": pipeline_execution.start_time,
                    "PipelineExecutionStatus": pipeline_execution.pipeline_execution_status,
                    "PipelineExecutionDescription": pipeline_execution.pipeline_execution_description,
                    "PipelineExecutionDisplayName": pipeline_execution.pipeline_execution_display_name,
                    "PipelineExecutionFailureReason": str(
                        pipeline_execution.pipeline_execution_failure_reason
                    ),
                }
                for pipeline_execution_arn, pipeline_execution in pipeline.pipeline_executions.items()
            ]
        }

    def describe_pipeline_definition_for_execution(
        self, pipeline_execution_arn: str
    ) -> Dict[str, Any]:
        pipeline_execution = get_pipeline_execution_from_arn(
            self.pipelines, pipeline_execution_arn
        )
        return {
            "PipelineDefinition": str(
                pipeline_execution.pipeline_definition_for_execution
            ),
            "CreationTime": pipeline_execution.creation_time,
        }

    def list_pipeline_parameters_for_execution(
        self, pipeline_execution_arn: str
    ) -> Dict[str, Any]:
        pipeline_execution = get_pipeline_execution_from_arn(
            self.pipelines, pipeline_execution_arn
        )
        return {
            "PipelineParameters": pipeline_execution.pipeline_parameters,
        }

    def describe_pipeline_execution(
        self, pipeline_execution_arn: str
    ) -> Dict[str, Any]:
        pipeline_execution = get_pipeline_execution_from_arn(
            self.pipelines, pipeline_execution_arn
        )
        pipeline_name = get_pipeline_name_from_execution_arn(pipeline_execution_arn)
        pipeline = get_pipeline_from_name(self.pipelines, pipeline_name)

        return {
            "PipelineArn": pipeline.pipeline_arn,
            "PipelineExecutionArn": pipeline_execution.pipeline_execution_arn,
            "PipelineExecutionDisplayName": pipeline_execution.pipeline_execution_display_name,
            "PipelineExecutionStatus": pipeline_execution.pipeline_execution_status,
            "PipelineExecutionDescription": pipeline_execution.pipeline_execution_description,
            "PipelineExperimentConfig": {},
            "FailureReason": "",
            "CreationTime": pipeline_execution.creation_time,
            "LastModifiedTime": pipeline_execution.last_modified_time,
            "CreatedBy": pipeline_execution.created_by,
            "LastModifiedBy": pipeline_execution.last_modified_by,
            "ParallelismConfiguration": pipeline_execution.parallelism_configuration,
        }

    def describe_pipeline(self, pipeline_name: str) -> Dict[str, Any]:
        pipeline = get_pipeline_from_name(self.pipelines, pipeline_name)
        return {
            "PipelineArn": pipeline.pipeline_arn,
            "PipelineName": pipeline.pipeline_name,
            "PipelineDisplayName": pipeline.pipeline_display_name,
            "PipelineDescription": pipeline.pipeline_description,
            "PipelineDefinition": pipeline.pipeline_definition,
            "RoleArn": pipeline.role_arn,
            "PipelineStatus": pipeline.pipeline_status,
            "CreationTime": pipeline.creation_time,
            "LastModifiedTime": pipeline.last_modified_time,
            "LastRunTime": pipeline.last_execution_time,
            "CreatedBy": pipeline.created_by,
            "LastModifiedBy": pipeline.last_modified_by,
            "ParallelismConfiguration": pipeline.parallelism_configuration,
        }

    def list_pipelines(
        self,
        pipeline_name_prefix: str,
        created_after: str,
        created_before: str,
        next_token: str,
        max_results: int,
        sort_by: str,
        sort_order: str,
    ) -> Dict[str, Any]:
        if next_token:
            try:
                starting_index = int(next_token)
                if starting_index > len(self.pipelines):
                    raise ValueError  # invalid next_token
            except ValueError:
                raise AWSValidationException('Invalid pagination token because "{0}".')
        else:
            starting_index = 0

        if max_results:
            end_index = max_results + starting_index
            pipelines_fetched: Iterable[FakePipeline] = list(self.pipelines.values())[
                starting_index:end_index
            ]
            if end_index >= len(self.pipelines):
                next_index = None
            else:
                next_index = end_index
        else:
            pipelines_fetched = list(self.pipelines.values())
            next_index = None

        if pipeline_name_prefix is not None:
            pipelines_fetched = filter(
                lambda x: pipeline_name_prefix in x.pipeline_name,
                pipelines_fetched,
            )

        def format_time(x: Any) -> str:
            return (
                x
                if isinstance(x, str)
                else datetime.fromtimestamp(x).strftime("%Y-%m-%d " "%H:%M:%S")
            )

        if created_after is not None:
            pipelines_fetched = filter(
                lambda x: x.creation_time > format_time(created_after),
                pipelines_fetched,
            )

        if created_before is not None:
            pipelines_fetched = filter(
                lambda x: x.creation_time < format_time(created_before),
                pipelines_fetched,
            )

        sort_key = "pipeline_name" if sort_by == "Name" else "creation_time"
        pipelines_fetched = sorted(
            pipelines_fetched,
            key=lambda pipeline_fetched: getattr(pipeline_fetched, sort_key),
            reverse=sort_order != "Ascending",
        )

        pipeline_summaries = [
            {
                "PipelineArn": pipeline_data.pipeline_arn,
                "PipelineName": pipeline_data.pipeline_name,
                "PipelineDisplayName": pipeline_data.pipeline_display_name,
                "PipelineDescription": pipeline_data.pipeline_description,
                "RoleArn": pipeline_data.role_arn,
                "CreationTime": pipeline_data.creation_time,
                "LastModifiedTime": pipeline_data.last_modified_time,
                "LastExecutionTime": pipeline_data.last_execution_time,
            }
            for pipeline_data in pipelines_fetched
        ]

        return {
            "PipelineSummaries": pipeline_summaries,
            "NextToken": str(next_index) if next_index is not None else None,
        }

    def list_processing_jobs(
        self,
        next_token: str,
        max_results: int,
        creation_time_after: str,
        creation_time_before: str,
        last_modified_time_after: str,
        last_modified_time_before: str,
        name_contains: str,
        status_equals: str,
    ) -> Dict[str, Any]:
        if next_token:
            try:
                starting_index = int(next_token)
                if starting_index > len(self.processing_jobs):
                    raise ValueError  # invalid next_token
            except ValueError:
                raise AWSValidationException('Invalid pagination token because "{0}".')
        else:
            starting_index = 0

        if max_results:
            end_index = max_results + starting_index
            processing_jobs_fetched: Iterable[FakeProcessingJob] = list(
                self.processing_jobs.values()
            )[starting_index:end_index]
            if end_index >= len(self.processing_jobs):
                next_index = None
            else:
                next_index = end_index
        else:
            processing_jobs_fetched = list(self.processing_jobs.values())
            next_index = None

        if name_contains is not None:
            processing_jobs_fetched = filter(
                lambda x: name_contains in x.processing_job_name,
                processing_jobs_fetched,
            )

        if creation_time_after is not None:
            processing_jobs_fetched = filter(
                lambda x: x.creation_time > creation_time_after, processing_jobs_fetched
            )

        if creation_time_before is not None:
            processing_jobs_fetched = filter(
                lambda x: x.creation_time < creation_time_before,
                processing_jobs_fetched,
            )

        if last_modified_time_after is not None:
            processing_jobs_fetched = filter(
                lambda x: x.last_modified_time > last_modified_time_after,
                processing_jobs_fetched,
            )

        if last_modified_time_before is not None:
            processing_jobs_fetched = filter(
                lambda x: x.last_modified_time < last_modified_time_before,
                processing_jobs_fetched,
            )
        if status_equals is not None:
            processing_jobs_fetched = filter(
                lambda x: x.processing_job_status == status_equals,
                processing_jobs_fetched,
            )

        processing_job_summaries = [
            {
                "ProcessingJobName": processing_job_data.processing_job_name,
                "ProcessingJobArn": processing_job_data.processing_job_arn,
                "CreationTime": processing_job_data.creation_time,
                "ProcessingEndTime": processing_job_data.processing_end_time,
                "LastModifiedTime": processing_job_data.last_modified_time,
                "ProcessingJobStatus": processing_job_data.processing_job_status,
            }
            for processing_job_data in processing_jobs_fetched
        ]

        return {
            "ProcessingJobSummaries": processing_job_summaries,
            "NextToken": str(next_index) if next_index is not None else None,
        }

    def create_training_job(
        self,
        training_job_name: str,
        hyper_parameters: Dict[str, str],
        algorithm_specification: Dict[str, Any],
        role_arn: str,
        input_data_config: List[Dict[str, Any]],
        output_data_config: Dict[str, str],
        resource_config: Dict[str, Any],
        vpc_config: Dict[str, List[str]],
        stopping_condition: Dict[str, int],
        tags: List[Dict[str, str]],
        enable_network_isolation: bool,
        enable_inter_container_traffic_encryption: bool,
        enable_managed_spot_training: bool,
        checkpoint_config: Dict[str, str],
        debug_hook_config: Dict[str, Any],
        debug_rule_configurations: List[Dict[str, Any]],
        tensor_board_output_config: Dict[str, str],
        experiment_config: Dict[str, str],
    ) -> FakeTrainingJob:
        training_job = FakeTrainingJob(
            account_id=self.account_id,
            region_name=self.region_name,
            training_job_name=training_job_name,
            hyper_parameters=hyper_parameters,
            algorithm_specification=algorithm_specification,
            role_arn=role_arn,
            input_data_config=input_data_config,
            output_data_config=output_data_config,
            resource_config=resource_config,
            vpc_config=vpc_config,
            stopping_condition=stopping_condition,
            tags=tags,
            enable_network_isolation=enable_network_isolation,
            enable_inter_container_traffic_encryption=enable_inter_container_traffic_encryption,
            enable_managed_spot_training=enable_managed_spot_training,
            checkpoint_config=checkpoint_config,
            debug_hook_config=debug_hook_config,
            debug_rule_configurations=debug_rule_configurations,
            tensor_board_output_config=tensor_board_output_config,
            experiment_config=experiment_config,
        )
        self.training_jobs[training_job_name] = training_job
        return training_job

    def describe_training_job(self, training_job_name: str) -> Dict[str, Any]:
        try:
            return self.training_jobs[training_job_name].response_object
        except KeyError:
            arn = FakeTrainingJob.arn_formatter(
                training_job_name, self.account_id, self.region_name
            )
            message = f"Could not find training job '{arn}'."
            raise ValidationError(message=message)

    def list_training_jobs(
        self,
        next_token: str,
        max_results: int,
        creation_time_after: str,
        creation_time_before: str,
        last_modified_time_after: str,
        last_modified_time_before: str,
        name_contains: str,
        status_equals: str,
    ) -> Dict[str, Any]:
        if next_token:
            try:
                starting_index = int(next_token)
                if starting_index > len(self.training_jobs):
                    raise ValueError  # invalid next_token
            except ValueError:
                raise AWSValidationException('Invalid pagination token because "{0}".')
        else:
            starting_index = 0

        if max_results:
            end_index = max_results + starting_index
            training_jobs_fetched: Iterable[FakeTrainingJob] = list(
                self.training_jobs.values()
            )[starting_index:end_index]
            if end_index >= len(self.training_jobs):
                next_index = None
            else:
                next_index = end_index
        else:
            training_jobs_fetched = list(self.training_jobs.values())
            next_index = None

        if name_contains is not None:
            training_jobs_fetched = filter(
                lambda x: name_contains in x.training_job_name, training_jobs_fetched
            )

        if creation_time_after is not None:
            training_jobs_fetched = filter(
                lambda x: x.creation_time > creation_time_after, training_jobs_fetched
            )

        if creation_time_before is not None:
            training_jobs_fetched = filter(
                lambda x: x.creation_time < creation_time_before, training_jobs_fetched
            )

        if last_modified_time_after is not None:
            training_jobs_fetched = filter(
                lambda x: x.last_modified_time > last_modified_time_after,
                training_jobs_fetched,
            )

        if last_modified_time_before is not None:
            training_jobs_fetched = filter(
                lambda x: x.last_modified_time < last_modified_time_before,
                training_jobs_fetched,
            )
        if status_equals is not None:
            training_jobs_fetched = filter(
                lambda x: x.training_job_status == status_equals, training_jobs_fetched
            )

        training_job_summaries = [
            {
                "TrainingJobName": training_job_data.training_job_name,
                "TrainingJobArn": training_job_data.training_job_arn,
                "CreationTime": training_job_data.creation_time,
                "TrainingEndTime": training_job_data.training_end_time,
                "LastModifiedTime": training_job_data.last_modified_time,
                "TrainingJobStatus": training_job_data.training_job_status,
            }
            for training_job_data in training_jobs_fetched
        ]

        return {
            "TrainingJobSummaries": training_job_summaries,
            "NextToken": str(next_index) if next_index is not None else None,
        }

    def update_endpoint_weights_and_capacities(
        self, endpoint_name: str, desired_weights_and_capacities: List[Dict[str, Any]]
    ) -> str:
        # Validate inputs
        endpoint = self.endpoints.get(endpoint_name, None)
        if not endpoint:
            arn = FakeEndpoint.arn_formatter(
                endpoint_name, self.account_id, self.region_name
            )
            raise AWSValidationException(f'Could not find endpoint "{arn}".')

        names_checked = []
        for variant_config in desired_weights_and_capacities:
            name = variant_config.get("VariantName")

            if name in names_checked:
                raise AWSValidationException(
                    f'The variant name "{name}" was non-unique within the request.'
                )

            if not any(
                variant["VariantName"] == name
                for variant in endpoint.production_variants
            ):
                raise AWSValidationException(
                    f'The variant name(s) "{name}" is/are not present within endpoint configuration "{endpoint.endpoint_config_name}".'
                )

            names_checked.append(name)

        # Update endpoint variants
        endpoint.endpoint_status = "Updating"

        for variant_config in desired_weights_and_capacities:
            name = variant_config.get("VariantName")
            desired_weight = variant_config.get("DesiredWeight")
            desired_instance_count = variant_config.get("DesiredInstanceCount")

            for variant in endpoint.production_variants:
                if variant.get("VariantName") == name:
                    variant["DesiredWeight"] = desired_weight
                    variant["CurrentWeight"] = desired_weight
                    variant["DesiredInstanceCount"] = desired_instance_count
                    variant["CurrentInstanceCount"] = desired_instance_count
                    break

        endpoint.endpoint_status = "InService"
        return endpoint.endpoint_arn


class FakeExperiment(BaseObject):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        experiment_name: str,
        tags: List[Dict[str, str]],
    ):
        self.experiment_name = experiment_name
        self.experiment_arn = arn_formatter(
            "experiment", experiment_name, account_id, region_name
        )
        self.tags = tags
        self.creation_time = self.last_modified_time = datetime.now().strftime(
            "%Y-%m-%d %H:%M:%S"
        )

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def response_create(self) -> Dict[str, str]:
        return {"ExperimentArn": self.experiment_arn}


class FakeTrial(BaseObject):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        trial_name: str,
        experiment_name: str,
        tags: List[Dict[str, str]],
        trial_components: List[str],
    ):
        self.trial_name = trial_name
        self.trial_arn = FakeTrial.arn_formatter(trial_name, account_id, region_name)
        self.tags = tags
        self.trial_components = trial_components
        self.experiment_name = experiment_name
        self.creation_time = self.last_modified_time = datetime.now().strftime(
            "%Y-%m-%d %H:%M:%S"
        )

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def response_create(self) -> Dict[str, str]:
        return {"TrialArn": self.trial_arn}

    @staticmethod
    def arn_formatter(name: str, account_id: str, region: str) -> str:
        return arn_formatter("experiment-trial", name, account_id, region)


class FakeTrialComponent(BaseObject):
    def __init__(
        self,
        account_id: str,
        region_name: str,
        trial_component_name: str,
        trial_name: Optional[str],
        tags: List[Dict[str, str]],
    ):
        self.trial_component_name = trial_component_name
        self.trial_component_arn = FakeTrialComponent.arn_formatter(
            trial_component_name, account_id, region_name
        )
        self.tags = tags
        self.trial_name = trial_name
        now_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.creation_time = self.last_modified_time = now_string

    @property
    def response_object(self) -> Dict[str, Any]:  # type: ignore[misc]
        response_object = self.gen_response_object()
        return {
            k: v for k, v in response_object.items() if v is not None and v != [None]
        }

    @property
    def response_create(self) -> Dict[str, str]:
        return {"TrialComponentArn": self.trial_component_arn}

    @staticmethod
    def arn_formatter(
        trial_component_name: str, account_id: str, region_name: str
    ) -> str:
        return arn_formatter(
            "experiment-trial-component", trial_component_name, account_id, region_name
        )


sagemaker_backends = BackendDict(SageMakerModelBackend, "sagemaker")