





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowercase				:	Optional[Any]      						=  {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Union[str, Any]      						=  ["""SpeechEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Any      						=  ["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
    import sys

    lowercase				:	Tuple      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase				:	Union[str, Any]      						=  data_utils.TransfoXLTokenizer
lowercase				:	Optional[int]      						=  data_utils.TransfoXLCorpus
lowercase				:	List[Any]      						=  data_utils
lowercase				:	Tuple      						=  data_utils



def A_     (		A__		,				A__		,				A__		,				A__    )  ->		Optional[Any]:
       if transfo_xl_dataset_file:
              # Convert a pre-processed corpus (see original TensorFlow repo)
              with open(A__		,				'rb'    ) as fp:
                     a__						:     int			=    pickle.load(A__		,				encoding='latin1'    )
              # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
              a__						:     int			=    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
              print(F'Save vocabulary to {pytorch_vocab_dump_path}'    )
              a__						:     List[Any]			=    corpus.vocab.__dict__
              torch.save(A__		,				A__    )

              a__						:     Dict			=    corpus.__dict__
              corpus_dict_no_vocab.pop('vocab'		,				A__    )
              a__						:     Optional[int]			=    pytorch_dump_folder_path + '/' + CORPUS_NAME
              print(F'Save dataset to {pytorch_dataset_dump_path}'    )
              torch.save(A__		,				A__    )

       if tf_checkpoint_path:
              # Convert a pre-trained TensorFlow model
              a__						:     Union[str, Any]			=    os.path.abspath(A__    )
              a__						:     Optional[Any]			=    os.path.abspath(A__    )

              print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'    )
              # Initialise PyTorch model
              if transfo_xl_config_file == "":
                     a__						:     Dict			=    TransfoXLConfig()
              else:
                     a__						:     Dict			=    TransfoXLConfig.from_json_file(A__    )
              print(F'Building PyTorch model from configuration: {config}'    )
              a__						:     Optional[int]			=    TransfoXLLMHeadModel(A__    )

              a__						:     int			=    load_tf_weights_in_transfo_xl(A__		,				A__		,				A__    )
              # Save pytorch-model
              a__						:     Any			=    os.path.join(A__		,				A__    )
              a__						:     Dict			=    os.path.join(A__		,				A__    )
              print(F'Save PyTorch model to {os.path.abspath(A__    )}'    )
              torch.save(model.state_dict()		,				A__    )
              print(F'Save configuration file to {os.path.abspath(A__    )}'    )
              with open(A__		,				'w'		,				encoding='utf-8'    ) as f:
                     f.write(config.to_json_string()    )


if __name__ == "__main__":
    lowercase				:	List[Any]      						=  argparse.ArgumentParser()
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
    )
    parser.add_argument(
        """--tf_checkpoint_path""",
        default="""""",
        type=str,
        help="""An optional path to a TensorFlow checkpoint path to be converted.""",
    )
    parser.add_argument(
        """--transfo_xl_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained BERT model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--transfo_xl_dataset_file""",
        default="""""",
        type=str,
        help="""An optional dataset file to be converted in a vocabulary.""",
    )
    lowercase				:	Any      						=  parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )


