from argparse import ArgumentParser, Namespace

from logging import getLogger

from transformers import AutoModel, AutoTokenizer
from transformers.commands import BaseTransformersCLICommand


def convert_command_factory(args: Namespace):
    """
    Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint.
    :return: ServeCommand
    """
    return ConvertCommand(args.model_type, args.tf_checkpoint, args.pytorch_dump_output,
                          args.config, args.finetuning_task_name)


class ConvertCommand(BaseTransformersCLICommand):

    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        """
        Register this command to argparse so it's available for the transformer-cli
        :param parser: Root parser to register command-specific arguments
        :return:
        """
        train_parser = parser.add_parser('convert', help="CLI tool to run convert model from original "
                                                         "author checkpoints to Transformesr PyTorch checkpoints.")
        train_parser.add_argument('--model_type', type=str, required=True,
                                  help='Model\'s type.')
        train_parser.add_argument('--tf_checkpoint', type=str, required=True,
                                  help='TensorFlow checkpoint path or folder.')
        train_parser.add_argument('--pytorch_dump_output', type=str, required=True,
                                  help='Path to the PyTorch savd model output.')
        train_parser.add_argument('--config', type=str, default="",
                                  help='Configuration file path or folder.')
        train_parser.add_argument('--finetuning_task_name', type=str, default=None,
                                  help='Optional fine-tuning task name if the TF model was a finetuned model.')
        train_parser.set_defaults(func=convert_command_factory)

    def __init__(self, model_type: str, tf_checkpoint: str, pytorch_dump_output: str,
                 config: str, finetuning_task_name: str, *args):
        self._logger = getLogger('transformers-cli/converting')

        self._logger.info('Loading model {}'.format(model_type))
        self._model_type = model_type
        self._tf_checkpoint = tf_checkpoint
        self._pytorch_dump_output = pytorch_dump_output
        self._config = config
        self._finetuning_task_name = finetuning_task_name

    def run(self):
        if self._model_type == "bert":
            try:
                from transformers.convert_bert_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
            except ImportError:
                msg = "transformers can only be used from the commandline to convert TensorFlow models in PyTorch, " \
                    "In that case, it requires TensorFlow to be installed. Please see " \
                    "https://www.tensorflow.org/install/ for installation instructions."
                raise ImportError(msg)

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
        elif self._model_type == "gpt":
            from transformers.convert_openai_original_tf_checkpoint_to_pytorch import convert_openai_checkpoint_to_pytorch
            convert_openai_checkpoint_to_pytorch(self._tf_checkpoint,
                                                    self._config,
                                                    self._pytorch_dump_output)
        elif self._model_type == "transfo_xl":
            try:
                from transformers.convert_transfo_xl_original_tf_checkpoint_to_pytorch import convert_transfo_xl_checkpoint_to_pytorch
            except ImportError:
                msg = "transformers can only be used from the commandline to convert TensorFlow models in PyTorch, " \
                    "In that case, it requires TensorFlow to be installed. Please see " \
                    "https://www.tensorflow.org/install/ for installation instructions."
                raise ImportError(msg)

            if 'ckpt' in self._tf_checkpoint.lower():
                TF_CHECKPOINT = self._tf_checkpoint
                TF_DATASET_FILE = ""
            else:
                TF_DATASET_FILE = self._tf_checkpoint
                TF_CHECKPOINT = ""
            convert_transfo_xl_checkpoint_to_pytorch(TF_CHECKPOINT,
                                                        self._config,
                                                        self._pytorch_dump_output,
                                                        TF_DATASET_FILE)
        elif self._model_type == "gpt2":
            try:
                from transformers.convert_gpt2_original_tf_checkpoint_to_pytorch import convert_gpt2_checkpoint_to_pytorch
            except ImportError:
                msg = "transformers can only be used from the commandline to convert TensorFlow models in PyTorch, " \
                    "In that case, it requires TensorFlow to be installed. Please see " \
                    "https://www.tensorflow.org/install/ for installation instructions."
                raise ImportError(msg)

            convert_gpt2_checkpoint_to_pytorch(self._tf_checkpoint, self._config, self._pytorch_dump_output)
        elif self._model_type == "xlnet":
            try:
                from transformers.convert_xlnet_original_tf_checkpoint_to_pytorch import convert_xlnet_checkpoint_to_pytorch
            except ImportError:
                msg = "transformers can only be used from the commandline to convert TensorFlow models in PyTorch, " \
                    "In that case, it requires TensorFlow to be installed. Please see " \
                    "https://www.tensorflow.org/install/ for installation instructions."
                raise ImportError(msg)

            convert_xlnet_checkpoint_to_pytorch(self._tf_checkpoint,
                                                self._config,
                                                self._pytorch_dump_output,
                                                self._finetuning_task_name)
        elif self._model_type == "xlm":
            from transformers.convert_xlm_original_pytorch_checkpoint_to_pytorch import convert_xlm_checkpoint_to_pytorch

            convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output)
        else:
            raise ValueError("--model_type should be selected in the list [bert, gpt, gpt2, transfo_xl, xlnet, xlm]")
