

'''simple docstring'''
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
lowerCamelCase			:				Dict 			=					data_utils.TransfoXLTokenizer
lowerCamelCase			:				Dict 			=					data_utils.TransfoXLCorpus
lowerCamelCase			:				List[Any] 			=					data_utils
lowerCamelCase			:				Any 			=					data_utils


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					Optional[Any]    )				->      List[Any]:
     """simple docstring"""




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

          _SCREAMING_SNAKE_CASE							   =corpus.__dict__
          corpus_dict_no_vocab.pop('vocab'							,			_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =pytorch_dump_folder_path + '/' + CORPUS_NAME
          print(f"Save dataset to {pytorch_dataset_dump_path}"    )
          torch.save(_UpperCamelCase							,			_UpperCamelCase    )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          _SCREAMING_SNAKE_CASE							   =os.path.abspath(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =os.path.abspath(_UpperCamelCase    )

          print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."    )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               _SCREAMING_SNAKE_CASE							   =TransfoXLConfig()
          else:
               _SCREAMING_SNAKE_CASE							   =TransfoXLConfig.from_json_file(_UpperCamelCase    )
          print(f"Building PyTorch model from configuration: {config}"    )
          _SCREAMING_SNAKE_CASE							   =TransfoXLLMHeadModel(_UpperCamelCase    )

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


if __name__ == "__main__":
   lowerCamelCase			:				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.",
   )
   lowerCamelCase			:				Optional[int] 			=					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,
   )



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
