# 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.
"""Placeholder docstring"""
from __future__ import absolute_import

import logging

import sagemaker
from sagemaker import image_uris
from sagemaker.deserializers import JSONDeserializer
from sagemaker.fw_utils import (
    model_code_key_prefix,
    validate_version_or_image_args,
)
from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
from sagemaker.predictor import Predictor
from sagemaker.serializers import JSONSerializer

logger = logging.getLogger("sagemaker")


class HuggingFacePredictor(Predictor):
    """A Predictor for inference against Hugging Face Endpoints.

    This is able to serialize Python lists, dictionaries, and numpy arrays to
    multidimensional tensors for Hugging Face inference.
    """

    def __init__(
        self,
        endpoint_name,
        sagemaker_session=None,
        serializer=JSONSerializer(),
        deserializer=JSONDeserializer(),
    ):
        """Initialize an ``HuggingFacePredictor``.

        Args:
            endpoint_name (str): The name of the endpoint to perform inference
                on.
            sagemaker_session (sagemaker.session.Session): Session object that
                manages interactions with Amazon SageMaker APIs and any other
                AWS services needed. If not specified, the estimator creates one
                using the default AWS configuration chain.
            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
                serializes input data to .npy format. Handles lists and numpy
                arrays.
            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                Default parses the response from .npy format to numpy array.
        """
        super(HuggingFacePredictor, self).__init__(
            endpoint_name,
            sagemaker_session,
            serializer=serializer,
            deserializer=deserializer,
        )


def _validate_pt_tf_versions(pytorch_version, tensorflow_version, image_uri):
    """Placeholder docstring"""

    if image_uri is not None:
        return

    if tensorflow_version is not None and pytorch_version is not None:
        raise ValueError(
            "tensorflow_version and pytorch_version are both not None. "
            "Specify only tensorflow_version or pytorch_version."
        )
    if tensorflow_version is None and pytorch_version is None:
        raise ValueError(
            "tensorflow_version and pytorch_version are both None. "
            "Specify either tensorflow_version or pytorch_version."
        )


class HuggingFaceModel(FrameworkModel):
    """A Hugging Face SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``."""

    _framework_name = "huggingface"

    def __init__(
        self,
        role,
        model_data=None,
        entry_point=None,
        transformers_version=None,
        tensorflow_version=None,
        pytorch_version=None,
        py_version=None,
        image_uri=None,
        predictor_cls=HuggingFacePredictor,
        model_server_workers=None,
        **kwargs,
    ):
        """Initialize a HuggingFaceModel.

        Args:
            model_data (str): The Amazon S3 location of a SageMaker model data
                ``.tar.gz`` file.
            role (str): An AWS IAM role specified with either the name or full ARN. The Amazon
                SageMaker training jobs and APIs that create Amazon SageMaker
                endpoints use this role to access training data and model
                artifacts. After the endpoint is created, the inference code
                might use the IAM role, if it needs to access an AWS resource.
            entry_point (str): The absolute or relative path to the Python source
                file that should be executed as the entry point to model
                hosting. If ``source_dir`` is specified, then ``entry_point``
                must point to a file located at the root of ``source_dir``.
                Defaults to None.
            transformers_version (str): Transformers version you want to use for
                executing your model training code. Defaults to None. Required
                unless ``image_uri`` is provided.
            tensorflow_version (str): TensorFlow version you want to use for
                executing your inference code. Defaults to ``None``. Required unless
                ``pytorch_version`` is provided. List of supported versions:
                https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
            pytorch_version (str): PyTorch version you want to use for
                executing your inference code. Defaults to ``None``. Required unless
                ``tensorflow_version`` is provided. List of supported versions:
                https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
            py_version (str): Python version you want to use for executing your
                model training code. Defaults to ``None``. Required unless
                ``image_uri`` is provided.
            image_uri (str): A Docker image URI. Defaults to None. If not specified, a
                default image for PyTorch will be used. If ``framework_version``
                or ``py_version`` are ``None``, then ``image_uri`` is required. If
                also ``None``, then a ``ValueError`` will be raised.
            predictor_cls (callable[str, sagemaker.session.Session]): A function
                to call to create a predictor with an endpoint name and
                SageMaker ``Session``. If specified, ``deploy()`` returns the
                result of invoking this function on the created endpoint name.
            model_server_workers (int): Optional. The number of worker processes
                used by the inference server. If None, server will use one
                worker per vCPU.
            **kwargs: Keyword arguments passed to the superclass
                :class:`~sagemaker.model.FrameworkModel` and, subsequently, its
                superclass :class:`~sagemaker.model.Model`.

        .. tip::

            You can find additional parameters for initializing this class at
            :class:`~sagemaker.model.FrameworkModel` and
            :class:`~sagemaker.model.Model`.
        """
        validate_version_or_image_args(transformers_version, py_version, image_uri)
        _validate_pt_tf_versions(
            pytorch_version=pytorch_version,
            tensorflow_version=tensorflow_version,
            image_uri=image_uri,
        )
        if py_version == "py2":
            raise ValueError("py2 is not supported with HuggingFace images")
        self.framework_version = transformers_version
        self.pytorch_version = pytorch_version
        self.tensorflow_version = tensorflow_version
        self.py_version = py_version

        super(HuggingFaceModel, self).__init__(
            model_data, image_uri, role, entry_point, predictor_cls=predictor_cls, **kwargs
        )

        self.model_server_workers = model_server_workers

    def register(
        self,
        content_types,
        response_types,
        inference_instances,
        transform_instances,
        model_package_name=None,
        model_package_group_name=None,
        image_uri=None,
        model_metrics=None,
        metadata_properties=None,
        marketplace_cert=False,
        approval_status=None,
        description=None,
    ):
        """Creates a model package for creating SageMaker models or listing on Marketplace.

        Args:
            content_types (list): The supported MIME types for the input data.
            response_types (list): The supported MIME types for the output data.
            inference_instances (list): A list of the instance types that are used to
                generate inferences in real-time.
            transform_instances (list): A list of the instance types on which a transformation
                job can be run or on which an endpoint can be deployed.
            model_package_name (str): Model Package name, exclusive to `model_package_group_name`,
                using `model_package_name` makes the Model Package un-versioned.
                Defaults to ``None``.
            model_package_group_name (str): Model Package Group name, exclusive to
                `model_package_name`, using `model_package_group_name` makes the Model Package
                versioned. Defaults to ``None``.
            image_uri (str): Inference image URI for the container. Model class' self.image will
                be used if it is None. Defaults to ``None``.
            model_metrics (ModelMetrics): ModelMetrics object. Defaults to ``None``.
            metadata_properties (MetadataProperties): MetadataProperties object.
                Defaults to ``None``.
            marketplace_cert (bool): A boolean value indicating if the Model Package is certified
                for AWS Marketplace. Defaults to ``False``.
            approval_status (str): Model Approval Status, values can be "Approved", "Rejected",
                or "PendingManualApproval". Defaults to ``PendingManualApproval``.
            description (str): Model Package description. Defaults to ``None``.

        Returns:
            A `sagemaker.model.ModelPackage` instance.
        """
        instance_type = inference_instances[0]
        self._init_sagemaker_session_if_does_not_exist(instance_type)

        if image_uri:
            self.image_uri = image_uri
        if not self.image_uri:
            self.image_uri = self.serving_image_uri(
                region_name=self.sagemaker_session.boto_session.region_name,
                instance_type=instance_type,
            )
        return super(HuggingFaceModel, self).register(
            content_types,
            response_types,
            inference_instances,
            transform_instances,
            model_package_name,
            model_package_group_name,
            image_uri,
            model_metrics,
            metadata_properties,
            marketplace_cert,
            approval_status,
            description,
        )

    def prepare_container_def(self, instance_type=None, accelerator_type=None):
        """A container definition with framework configuration set in model environment variables.

        Args:
            instance_type (str): The EC2 instance type to deploy this Model to.
                For example, 'ml.p2.xlarge'.
            accelerator_type (str): The Elastic Inference accelerator type to
                deploy to the instance for loading and making inferences to the
                model.

        Returns:
            dict[str, str]: A container definition object usable with the
            CreateModel API.
        """
        deploy_image = self.image_uri
        if not deploy_image:
            if instance_type is None:
                raise ValueError(
                    "Must supply either an instance type (for choosing CPU vs GPU) or an image URI."
                )

            region_name = self.sagemaker_session.boto_session.region_name
            deploy_image = self.serving_image_uri(
                region_name, instance_type, accelerator_type=accelerator_type
            )

        deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
        self._upload_code(deploy_key_prefix, repack=True)
        deploy_env = dict(self.env)
        deploy_env.update(self._framework_env_vars())

        if self.model_server_workers:
            deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(self.model_server_workers)
        return sagemaker.container_def(
            deploy_image, self.repacked_model_data or self.model_data, deploy_env
        )

    def serving_image_uri(self, region_name, instance_type, accelerator_type=None):
        """Create a URI for the serving image.

        Args:
            region_name (str): AWS region where the image is uploaded.
            instance_type (str): SageMaker instance type. Used to determine device type
                (cpu/gpu/family-specific optimized).
            accelerator_type (str): The Elastic Inference accelerator type to
                deploy to the instance for loading and making inferences to the
                model.

        Returns:
            str: The appropriate image URI based on the given parameters.

        """
        if image_uris._processor(instance_type, ["cpu", "gpu"]) == "gpu":
            container_version = "cu110-ubuntu18.04"
        else:
            container_version = "ubuntu18.04"
        if self.tensorflow_version is not None:  # pylint: disable=no-member
            base_framework_version = (
                f"tensorflow{self.tensorflow_version}"  # pylint: disable=no-member
            )
        else:
            base_framework_version = f"pytorch{self.pytorch_version}"  # pylint: disable=no-member
        return image_uris.retrieve(
            self._framework_name,
            region_name,
            version=self.framework_version,
            py_version=self.py_version,
            instance_type=instance_type,
            accelerator_type=accelerator_type,
            image_scope="inference",
            base_framework_version=base_framework_version,
            container_version=container_version,
        )
