# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""The pipeline context for workflow"""
from __future__ import absolute_import

import warnings
import inspect
from functools import wraps
from typing import Dict, Optional

from sagemaker.session import Session, SessionSettings
from sagemaker.local import LocalSession


class _StepArguments:
    """Step arguments entity for `Step`"""

    def __init__(self, caller_name: str = None):
        """Create a `_StepArguments`

        Args:
            caller_name (str): The name of the caller function which is intercepted by the
                PipelineSession to get the step arguments.
        """
        self.caller_name = caller_name


class _JobStepArguments(_StepArguments):
    """Step arguments entity for job step types

    Job step types include: TrainingStep, ProcessingStep, TuningStep, TransformStep
    """

    def __init__(self, caller_name: str, args: dict):
        """Create a `_JobStepArguments`

        Args:
            caller_name (str): The name of the caller function which is intercepted by the
                PipelineSession to get the step arguments.
            args (dict): The arguments to be used for composing the SageMaker API request.
        """
        super(_JobStepArguments, self).__init__(caller_name)
        self.args = args


class _ModelStepArguments(_StepArguments):
    """Step arguments entity for `ModelStep`"""

    def __init__(self, model):
        """Create a `_ModelStepArguments`

        Args:
            model (Model or PipelineModel): A `sagemaker.model.Model`
                or `sagemaker.pipeline.PipelineModel` instance
        """
        super(_ModelStepArguments, self).__init__()
        self.model = model
        self.create_model_package_request = None
        self.create_model_request = None
        self.need_runtime_repack = set()
        self.runtime_repack_output_prefix = None


class PipelineSession(Session):
    """Managing interactions with SageMaker APIs and AWS services needed under Pipeline Context

    This class inherits the SageMaker session, it provides convenient methods
    for manipulating entities and resources that Amazon SageMaker uses,
    such as training jobs, endpoints, and input datasets in S3. When composing
    SageMaker Model-Building Pipeline, PipelineSession is recommended over
    regular SageMaker Session
    """

    def __init__(
        self,
        boto_session=None,
        sagemaker_client=None,
        default_bucket=None,
        settings=SessionSettings(),
    ):
        """Initialize a ``PipelineSession``.

        Args:
            boto_session (boto3.session.Session): The underlying Boto3 session which AWS service
                calls are delegated to (default: None). If not provided, one is created with
                default AWS configuration chain.
            sagemaker_client (boto3.SageMaker.Client): Client which makes Amazon SageMaker service
                calls other than ``InvokeEndpoint`` (default: None). Estimators created using this
                ``Session`` use this client. If not provided, one will be created using this
                instance's ``boto_session``.
            default_bucket (str): The default Amazon S3 bucket to be used by this session.
                This will be created the next time an Amazon S3 bucket is needed (by calling
                :func:`default_bucket`).
                If not provided, a default bucket will be created based on the following format:
                "sagemaker-{region}-{aws-account-id}".
                Example: "sagemaker-my-custom-bucket".
            settings (sagemaker.session_settings.SessionSettings): Optional. Set of optional
                parameters to apply to the session.
        """
        super().__init__(
            boto_session=boto_session,
            sagemaker_client=sagemaker_client,
            default_bucket=default_bucket,
            settings=settings,
        )
        self._context = None

    @property
    def context(self):
        """Hold contextual information useful to the session"""
        return self._context

    @context.setter
    def context(self, value: Optional[_StepArguments] = None):
        self._context = value

    def _intercept_create_request(self, request: Dict, create, func_name: str = None):
        """This function intercepts the create job request

        Args:
            request (dict): the create job request
            create (functor): a functor calls the sagemaker client create method
            func_name (str): the name of the function needed intercepting
        """
        if func_name == self.create_model.__name__:
            self.context.create_model_request = request
            self.context.caller_name = func_name
        elif func_name == self.create_model_package_from_containers.__name__:
            self.context.create_model_package_request = request
            self.context.caller_name = func_name
        else:
            self.context = _JobStepArguments(func_name, request)

    def init_model_step_arguments(self, model):
        """Create a `_ModelStepArguments` (if not exist) as pipeline context

        Args:
            model (Model or PipelineModel): A `sagemaker.model.Model`
                or `sagemaker.pipeline.PipelineModel` instance
        """
        if not isinstance(self._context, _ModelStepArguments):
            self._context = _ModelStepArguments(model)


class LocalPipelineSession(LocalSession, PipelineSession):
    """Managing a session that executes Sagemaker pipelines and jobs locally in a pipeline context.

    This class inherits from the LocalSession and PipelineSession classes.
    When running Sagemaker pipelines locally, this class is preferred over LocalSession.
    """

    def __init__(
        self, boto_session=None, default_bucket=None, s3_endpoint_url=None, disable_local_code=False
    ):
        """Initialize a ``LocalPipelineSession``.

        Args:
            boto_session (boto3.session.Session): The underlying Boto3 session which AWS service
                calls are delegated to (default: None). If not provided, one is created with
                default AWS configuration chain.
            default_bucket (str): The default Amazon S3 bucket to be used by this session.
                This will be created the next time an Amazon S3 bucket is needed (by calling
                :func:`default_bucket`).
                If not provided, a default bucket will be created based on the following format:
                "sagemaker-{region}-{aws-account-id}".
                Example: "sagemaker-my-custom-bucket".
            s3_endpoint_url (str): Override the default endpoint URL for Amazon S3,
                if set (default: None).
            disable_local_code (bool): Set to True to override the default AWS configuration chain
                to disable the `local.local_code` setting, which may not be supported for some SDK
                features (default: False).
        """

        super().__init__(
            boto_session=boto_session,
            default_bucket=default_bucket,
            s3_endpoint_url=s3_endpoint_url,
            disable_local_code=disable_local_code,
        )


def runnable_by_pipeline(run_func):
    """A convenient Decorator

    This is a decorator designed to annotate, during pipeline session,
    the methods that downstream managed to
        1. preprocess user inputs, outputs, and configurations
        2. generate the create request
        3. start the job.
    For instance, `Processor.run`, `Estimator.fit`, or `Transformer.transform`.
    This decorator will essentially run 1, and capture the request shape from 2,
    then instead of starting a new job in 3, it will return request shape from 2
    to `sagemaker.workflow.steps.Step`. The request shape will be used to construct
    the arguments needed to compose that particular step as part of the pipeline.
    The job will be started during pipeline execution.
    """

    @wraps(run_func)
    def wrapper(*args, **kwargs):
        self_instance = args[0]
        if isinstance(self_instance.sagemaker_session, PipelineSession):
            run_func_params = inspect.signature(run_func).parameters
            arg_list = list(args)

            override_wait, override_logs = False, False
            for i, (arg_name, _) in enumerate(run_func_params.items()):
                if i >= len(arg_list):
                    break
                if arg_name == "wait":
                    override_wait = True
                    arg_list[i] = False
                elif arg_name == "logs":
                    override_logs = True
                    arg_list[i] = False

            args = tuple(arg_list)

            if not override_wait and "wait" in run_func_params.keys():
                kwargs["wait"] = False
            if not override_logs and "logs" in run_func_params.keys():
                kwargs["logs"] = False

            warnings.warn(
                "Running within a PipelineSession, there will be No Wait, "
                "No Logs, and No Job being started.",
                UserWarning,
            )
            if run_func.__name__ in ["register", "create"]:
                self_instance.sagemaker_session.init_model_step_arguments(self_instance)
                run_func(*args, **kwargs)
                context = self_instance.sagemaker_session.context
                self_instance.sagemaker_session.context = None
                return context

            run_func(*args, **kwargs)
            return self_instance.sagemaker_session.context

        return run_func(*args, **kwargs)

    return wrapper