# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------

# pylint: disable=protected-access

import json
import os
import re
from pathlib import Path
from typing import TYPE_CHECKING, Dict

from marshmallow.exceptions import ValidationError as SchemaValidationError

from azure.ai.ml._artifacts._artifact_utilities import _upload_and_generate_remote_uri
from azure.ai.ml._azure_environments import _get_aml_resource_id_from_metadata, _resource_to_scopes
from azure.ai.ml._exception_helper import log_and_raise_error
from azure.ai.ml._restclient.v2020_09_01_dataplanepreview.models import BatchJobResource
from azure.ai.ml._restclient.v2022_05_01 import AzureMachineLearningWorkspaces as ServiceClient052022
from azure.ai.ml._schema._deployment.batch.batch_job import BatchJobSchema
from azure.ai.ml._scope_dependent_operations import (
    OperationConfig,
    OperationsContainer,
    OperationScope,
    _ScopeDependentOperations,
)
# from azure.ai.ml._telemetry import ActivityType, monitor_with_activity
from azure.ai.ml._utils._arm_id_utils import get_datastore_arm_id, is_ARM_id_for_resource, remove_datastore_prefix
from azure.ai.ml._utils._azureml_polling import AzureMLPolling
from azure.ai.ml._utils._endpoint_utils import validate_response
from azure.ai.ml._utils._http_utils import HttpPipeline
from azure.ai.ml._utils._logger_utils import OpsLogger
from azure.ai.ml._utils.utils import _get_mfe_base_url_from_discovery_service, modified_operation_client
from azure.ai.ml.constants._common import (
    ARM_ID_FULL_PREFIX,
    BASE_PATH_CONTEXT_KEY,
    HTTP_PREFIX,
    LONG_URI_REGEX_FORMAT,
    PARAMS_OVERRIDE_KEY,
    SHORT_URI_REGEX_FORMAT,
    AssetTypes,
    AzureMLResourceType,
    LROConfigurations,
)
from azure.ai.ml.constants._endpoint import EndpointInvokeFields, EndpointYamlFields
from azure.ai.ml.entities import BatchEndpoint, BatchJob
from azure.ai.ml.entities._inputs_outputs import Input
from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, MlException, ValidationErrorType, ValidationException
from azure.core.credentials import TokenCredential
from azure.core.exceptions import HttpResponseError
from azure.core.paging import ItemPaged
from azure.core.polling import LROPoller
from azure.core.tracing.decorator import distributed_trace

from ._operation_orchestrator import OperationOrchestrator

if TYPE_CHECKING:
    from azure.ai.ml.operations import DatastoreOperations

ops_logger = OpsLogger(__name__)
module_logger = ops_logger.module_logger


class BatchEndpointOperations(_ScopeDependentOperations):
    """BatchEndpointOperations.

    You should not instantiate this class directly. Instead, you should
    create an MLClient instance that instantiates it for you and
    attaches it as an attribute.
    """

    def __init__(
        self,
        operation_scope: OperationScope,
        operation_config: OperationConfig,
        service_client_05_2022: ServiceClient052022,
        all_operations: OperationsContainer,
        credentials: TokenCredential = None,
        **kwargs: Dict,
    ):

        super(BatchEndpointOperations, self).__init__(operation_scope, operation_config)
        # ops_logger.update_info(kwargs)
        self._batch_operation = service_client_05_2022.batch_endpoints
        self._batch_deployment_operation = service_client_05_2022.batch_deployments
        self._batch_job_endpoint = kwargs.pop("service_client_09_2020_dataplanepreview").batch_job_endpoint
        self._all_operations = all_operations
        self._credentials = credentials
        self._init_kwargs = kwargs

        self._requests_pipeline: HttpPipeline = kwargs.pop("requests_pipeline")

    @property
    def _datastore_operations(self) -> "DatastoreOperations":
        return self._all_operations.all_operations[AzureMLResourceType.DATASTORE]

    @distributed_trace
    # @monitor_with_activity(logger, "BatchEndpoint.List", ActivityType.PUBLICAPI)
    def list(self) -> ItemPaged[BatchEndpoint]:
        """List endpoints of the workspace.

        :return: A list of endpoints
        :rtype: ~azure.core.paging.ItemPaged[~azure.ai.ml.entities.BatchEndpoint]
        """
        return self._batch_operation.list(
            resource_group_name=self._resource_group_name,
            workspace_name=self._workspace_name,
            cls=lambda objs: [BatchEndpoint._from_rest_object(obj) for obj in objs],
            **self._init_kwargs,
        )

    @distributed_trace
    # @monitor_with_activity(logger, "BatchEndpoint.Get", ActivityType.PUBLICAPI)
    def get(
        self,
        name: str,
    ) -> BatchEndpoint:
        """Get a Endpoint resource.

        :param name: Name of the endpoint.
        :type name: str
        :return: Endpoint object retrieved from the service.
        :rtype: ~azure.ai.ml.entities.BatchEndpoint
        """
        # first get the endpoint
        endpoint = self._batch_operation.get(
            resource_group_name=self._resource_group_name,
            workspace_name=self._workspace_name,
            endpoint_name=name,
            **self._init_kwargs,
        )

        endpoint_data = BatchEndpoint._from_rest_object(endpoint)
        return endpoint_data

    @distributed_trace
    # @monitor_with_activity(logger, "BatchEndpoint.BeginDelete", ActivityType.PUBLICAPI)
    def begin_delete(self, name: str) -> LROPoller[None]:
        """Delete a batch Endpoint.

        :param name: Name of the batch endpoint.
        :type name: str
        :return: A poller to track the operation status.
        :rtype: ~azure.core.polling.LROPoller[None]
        """
        path_format_arguments = {
            "endpointName": name,
            "resourceGroupName": self._resource_group_name,
            "workspaceName": self._workspace_name,
        }

        delete_poller = self._batch_operation.begin_delete(
            resource_group_name=self._resource_group_name,
            workspace_name=self._workspace_name,
            endpoint_name=name,
            polling=AzureMLPolling(
                LROConfigurations.POLL_INTERVAL,
                path_format_arguments=path_format_arguments,
                **self._init_kwargs,
            ),
            polling_interval=LROConfigurations.POLL_INTERVAL,
            **self._init_kwargs,
        )
        return delete_poller

    @distributed_trace
    # @monitor_with_activity(logger, "BatchEndpoint.BeginCreateOrUpdate", ActivityType.PUBLICAPI)
    def begin_create_or_update(self, endpoint: BatchEndpoint) -> LROPoller[BatchEndpoint]:
        """Create or update a batch endpoint.

        :param endpoint: The endpoint entity.
        :type endpoint: ~azure.ai.ml.entities.BatchEndpoint
        :return: A poller to track the operation status.
        :rtype: ~azure.core.polling.LROPoller[~azure.ai.ml.entities.BatchEndpoint]
        """

        try:
            location = self._get_workspace_location()

            endpoint_resource = endpoint._to_rest_batch_endpoint(location=location)
            poller = self._batch_operation.begin_create_or_update(
                resource_group_name=self._resource_group_name,
                workspace_name=self._workspace_name,
                endpoint_name=endpoint.name,
                body=endpoint_resource,
                polling=True,
                **self._init_kwargs,
            )
            return poller

        except Exception as ex:
            if isinstance(ex, (ValidationException, SchemaValidationError)):
                log_and_raise_error(ex)
            raise ex

    @distributed_trace
    # @monitor_with_activity(logger, "BatchEndpoint.Invoke", ActivityType.PUBLICAPI)
    def invoke(
        self,
        endpoint_name: str,
        *,
        deployment_name: str = None,
        inputs: Dict[str, Input] = None,
        **kwargs,
    ) -> BatchJob:
        """Invokes the batch endpoint with the provided payload.

        :param endpoint_name: The endpoint name.
        :type endpoint_name: str
        :param deployment_name: (Optional) The name of a specific deployment to invoke. This is optional.
            By default requests are routed to any of the deployments according to the traffic rules.
        :type deployment_name: str
        :param inputs: (Optional) A dictionary of existing data asset, public uri file or folder
            to use with the deployment
        :type inputs: Dict[str, Input]
        :raises ~azure.ai.ml.exceptions.ValidationException: Raised if deployment cannot be successfully validated.
            Details will be provided in the error message.
        :raises ~azure.ai.ml.exceptions.AssetException: Raised if BatchEndpoint assets
            (e.g. Data, Code, Model, Environment) cannot be successfully validated.
            Details will be provided in the error message.
        :raises ~azure.ai.ml.exceptions.ModelException: Raised if BatchEndpoint model cannot be successfully validated.
            Details will be provided in the error message.
        :raises ~azure.ai.ml.exceptions.EmptyDirectoryError: Raised if local path provided points to an empty directory.
        :return: The invoked batch deployment job.
        :rtype: ~azure.ai.ml.entities.BatchJob
        """
        params_override = kwargs.get("params_override", None) or []
        input = kwargs.get("input", None) # pylint: disable=redefined-builtin
        # Until this bug is resolved https://msdata.visualstudio.com/Vienna/_workitems/edit/1446538
        if deployment_name:
            self._validate_deployment_name(endpoint_name, deployment_name)

        if input and isinstance(input, Input):
            if HTTP_PREFIX not in input.path:
                self._resolve_input(input, os.getcwd())
            # MFE expects a dictionary as input_data that's why we are using
            # "UriFolder" or "UriFile" as keys depending on the input type
            if input.type == "uri_folder":
                params_override.append({EndpointYamlFields.BATCH_JOB_INPUT_DATA: {"UriFolder": input}})
            elif input.type == "uri_file":
                params_override.append({EndpointYamlFields.BATCH_JOB_INPUT_DATA: {"UriFile": input}})
        elif inputs:
            for key, input_data in inputs.items():
                if isinstance(input_data, Input) and HTTP_PREFIX not in input_data.path:
                    self._resolve_input(input_data, os.getcwd())
            params_override.append({EndpointYamlFields.BATCH_JOB_INPUT_DATA: inputs})
        else:
            msg = (
                "Unsupported input type please use a dictionary of either a path on the datastore, public URI, "
                "a registered data asset, or a local folder path."
            )
            raise ValidationException(
                message=msg,
                target=ErrorTarget.BATCH_ENDPOINT,
                no_personal_data_message=msg,
                error_category=ErrorCategory.USER_ERROR,
                error_type=ValidationErrorType.INVALID_VALUE,
            )

        # Batch job doesn't have a python class, loading a rest object using params override
        context = {
            BASE_PATH_CONTEXT_KEY: Path(".").parent,
            PARAMS_OVERRIDE_KEY: params_override,
        }

        batch_job = BatchJobSchema(context=context).load(data={})  # pylint: disable=no-member
        # update output datastore to arm id if needed
        # TODO: Unify datastore name -> arm id logic, TASK: 1104172
        if (
            batch_job.output_dataset
            and batch_job.output_dataset.datastore_id
            and (not is_ARM_id_for_resource(batch_job.output_dataset.datastore_id))
        ):
            batch_job.output_dataset.datastore_id = get_datastore_arm_id(
                batch_job.output_dataset.datastore_id, self._operation_scope
            )

        endpoint = self._batch_operation.get(
            resource_group_name=self._resource_group_name,
            workspace_name=self._workspace_name,
            endpoint_name=endpoint_name,
            **self._init_kwargs,
        )

        headers = EndpointInvokeFields.DEFAULT_HEADER
        ml_audience_scopes = _resource_to_scopes(_get_aml_resource_id_from_metadata())
        module_logger.debug("ml_audience_scopes used: `%s`\n", ml_audience_scopes)
        key = self._credentials.get_token(*ml_audience_scopes).token
        headers[EndpointInvokeFields.AUTHORIZATION] = f"Bearer {key}"

        if deployment_name:
            headers[EndpointInvokeFields.MODEL_DEPLOYMENT] = deployment_name

        response = self._requests_pipeline.post(
           endpoint.properties.scoring_uri,
           json=BatchJobResource(properties=batch_job).serialize(),
           headers=headers,
        )
        validate_response(response)
        batch_job = json.loads(response.text())
        return BatchJobResource.deserialize(batch_job)

    @distributed_trace
    # @monitor_with_activity(logger, "BatchEndpoint.ListJobs", ActivityType.PUBLICAPI)
    def list_jobs(self, endpoint_name: str) -> ItemPaged[BatchJob]:
        """List jobs under the provided batch endpoint deployment. This is only
        valid for batch endpoint.

        :param endpoint_name: The endpoint name
        :type endpoint_name: str
        :return: List of jobs
        :rtype: ~azure.core.paging.ItemPaged[~azure.ai.ml.entities.BatchJob]
        """

        workspace_operations = self._all_operations.all_operations[AzureMLResourceType.WORKSPACE]
        mfe_base_uri = _get_mfe_base_url_from_discovery_service(
            workspace_operations, self._workspace_name, self._requests_pipeline
        )

        with modified_operation_client(self._batch_job_endpoint, mfe_base_uri):
            result = self._batch_job_endpoint.list(
                endpoint_name=endpoint_name,
                resource_group_name=self._resource_group_name,
                workspace_name=self._workspace_name,
                **self._init_kwargs,
            )

            # This is necessary as the paged result need to be resolved inside the context manager
            return list(result)

    def _get_workspace_location(self) -> str:
        return self._all_operations.all_operations[AzureMLResourceType.WORKSPACE].get(self._workspace_name).location

    def _validate_deployment_name(self, endpoint_name, deployment_name):
        deployments_list = self._batch_deployment_operation.list(
            endpoint_name=endpoint_name,
            resource_group_name=self._resource_group_name,
            workspace_name=self._workspace_name,
            cls=lambda objs: [obj.name for obj in objs],
            **self._init_kwargs,
        )
        if deployments_list:
            if deployment_name not in deployments_list:
                msg = f"Deployment name {deployment_name} not found for this endpoint"
                raise ValidationException(
                    message=msg.format(deployment_name),
                    no_personal_data_message=msg.format("[deployment_name]"),
                    target=ErrorTarget.DEPLOYMENT,
                    error_category=ErrorCategory.USER_ERROR,
                    error_type=ValidationErrorType.RESOURCE_NOT_FOUND,
                )
        else:
            msg = "No deployment exists for this endpoint"
            raise ValidationException(
                message=msg,
                no_personal_data_message=msg,
                error_category=ErrorCategory.USER_ERROR,
                target=ErrorTarget.DEPLOYMENT,
                error_type=ValidationErrorType.RESOURCE_NOT_FOUND,
            )

    def _resolve_input(self, entry: Input, base_path: str) -> None:
        # We should not verify anything that is not of type Input
        if not isinstance(entry, Input):
            return

        # Input path should not be empty
        if not entry.path:
            raise Exception("Input path can't be empty for batch endpoint invoke")

        try:
            if entry.path.startswith(ARM_ID_FULL_PREFIX):
                if not is_ARM_id_for_resource(entry.path, AzureMLResourceType.DATA):
                    raise ValidationException(
                        message="Invalid input path",
                        target=ErrorTarget.BATCH_ENDPOINT,
                        no_personal_data_message="Invalid input path",
                        error_type=ValidationErrorType.INVALID_VALUE,
                    )
            elif os.path.isabs(entry.path):  # absolute local path, upload, transform to remote url
                if entry.type == AssetTypes.URI_FOLDER and not os.path.isdir(entry.path):
                    raise ValidationException(
                        message="There is no folder on target path: {}".format(entry.path),
                        target=ErrorTarget.BATCH_ENDPOINT,
                        no_personal_data_message="There is no folder on target path",
                        error_type=ValidationErrorType.FILE_OR_FOLDER_NOT_FOUND,
                    )
                if entry.type == AssetTypes.URI_FILE and not os.path.isfile(entry.path):
                    raise ValidationException(
                        message="There is no file on target path: {}".format(entry.path),
                        target=ErrorTarget.BATCH_ENDPOINT,
                        no_personal_data_message="There is no file on target path",
                        error_type=ValidationErrorType.FILE_OR_FOLDER_NOT_FOUND,
                    )
                # absolute local path
                entry.path = _upload_and_generate_remote_uri(
                    self._operation_scope,
                    self._datastore_operations,
                    entry.path,
                )
                if entry.type == AssetTypes.URI_FOLDER and entry.path and not entry.path.endswith("/"):
                    entry.path = entry.path + "/"
            elif ":" in entry.path or "@" in entry.path:  # Check registered file or folder datastore
                # If we receive a datastore path in long/short form we don't need
                # to get the arm asset id
                if re.match(SHORT_URI_REGEX_FORMAT, entry.path) or re.match(LONG_URI_REGEX_FORMAT, entry.path):
                    return
                asset_type = AzureMLResourceType.DATASTORE
                entry.path = remove_datastore_prefix(entry.path)
                orchestrator = OperationOrchestrator(
                    self._all_operations, self._operation_scope, self._operation_config
                )
                entry.path = orchestrator.get_asset_arm_id(entry.path, asset_type)
            else:  # relative local path, upload, transform to remote url
                local_path = Path(base_path, entry.path).resolve()
                entry.path = _upload_and_generate_remote_uri(
                    self._operation_scope,
                    self._datastore_operations,
                    local_path,
                )
                if entry.type == AssetTypes.URI_FOLDER and entry.path and not entry.path.endswith("/"):
                    entry.path = entry.path + "/"
        except (MlException, HttpResponseError) as e:
            raise e
        except Exception as e:
            raise ValidationException(
                message=f"Supported input path value are: path on the datastore, public URI, "
                "a registered data asset, or a local folder path.\n"
                f"Met {type(e)}:\n{e}",
                target=ErrorTarget.BATCH_ENDPOINT,
                no_personal_data_message="Supported input path value are: path on the datastore, "
                "public URI, a registered data asset, or a local folder path.",
                error=e,
                error_type=ValidationErrorType.INVALID_VALUE,
            )
