
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					   =data_utils.TransfoXLTokenizer
_lowerCamelCase					   =data_utils.TransfoXLCorpus
_lowerCamelCase					   =data_utils
_lowerCamelCase					   =data_utils





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

  lowerCamelCase		:	List[Any]							=      corpus.__dict__
  corpus_dict_no_vocab.pop("""vocab""",	lowerCamelCase							)
  lowerCamelCase		:	Union[str, Any]							=      pytorch_dump_folder_path + """/""" + CORPUS_NAME
  print(F'''Save dataset to {pytorch_dataset_dump_path}'''							)
  torch.save(lowerCamelCase,	lowerCamelCase							)

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

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

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


if __name__ == "__main__":
      _lowerCamelCase					   =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					   =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,
      )






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			"""camembert"""
      def __init__(      self    ,  __magic_name__=3_0_5_2_2    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=1e-12    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       super().__init__(pad_token_id=__magic_name__    ,  bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	int							=      num_attention_heads
       lowerCamelCase		:	Optional[int]							=      hidden_act
       lowerCamelCase		:	List[Any]							=      intermediate_size
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Optional[int]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Optional[int]							=      max_position_embeddings
       lowerCamelCase		:	str							=      type_vocab_size
       lowerCamelCase		:	Optional[Any]							=      initializer_range
       lowerCamelCase		:	int							=      layer_norm_eps
       lowerCamelCase		:	Any							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      use_cache
       lowerCamelCase		:	Union[str, Any]							=      classifier_dropout





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       if self.task == "multiple-choice":
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]      )




