
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    :   Optional[int]		       =				data_utils.TransfoXLTokenizer
lowercase    :   List[str]		       =				data_utils.TransfoXLCorpus
lowercase    :   List[str]		       =				data_utils
lowercase    :   List[Any]		       =				data_utils







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Tuple:






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

              lowercase				:	List[str]			      = corpus.__dict__
              corpus_dict_no_vocab.pop("""vocab"""   ,      lowerCAmelCase__							)
              lowercase				:	Optional[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
              lowercase				:	Optional[int]			      = os.path.abspath(lowerCAmelCase__							)
              lowercase				:	Any			      = 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 == "":
                     lowercase				:	Any			      = TransfoXLConfig()
              else:
                     lowercase				:	Tuple			      = TransfoXLConfig.from_json_file(lowerCAmelCase__							)
              print(f"Building PyTorch model from configuration: {config}"							)
              lowercase				:	str			      = TransfoXLLMHeadModel(lowerCAmelCase__							)

              lowercase				:	Optional[Any]			      = load_tf_weights_in_transfo_xl(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__							)
              # Save pytorch-model
              lowercase				:	Dict			      = os.path.join(lowerCAmelCase__   ,      lowerCAmelCase__							)
              lowercase				:	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__":
  lowercase    :   Tuple		       =				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    :   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,
  )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :Tuple			       ={
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Any								=     'transfo-xl'
				A_   :     Dict								=     ['mems']
				A_   :     int								=     {
				    'n_token': 'vocab_size',
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}


				def __init__(							self      :  Optional[Any]						, __UpperCamelCase      :  str=267_735						, __UpperCamelCase      :  int=[20_000, 40_000, 200_000]						, __UpperCamelCase      :  List[str]=1_024						, __UpperCamelCase      :  Union[str, Any]=1_024						, __UpperCamelCase      :  Optional[Any]=16						, __UpperCamelCase      :  Dict=64						, __UpperCamelCase      :  Optional[Any]=4_096						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  List[str]=False						, __UpperCamelCase      :  Optional[int]=18						, __UpperCamelCase      :  int=1_600						, __UpperCamelCase      :  Dict=1_000						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[Any]=0						, __UpperCamelCase      :  Any=-1						, __UpperCamelCase      :  Any=True						, __UpperCamelCase      :  List[Any]=0.1						, __UpperCamelCase      :  str=0.0						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  int="normal"						, __UpperCamelCase      :  List[Any]=0.0_1						, __UpperCamelCase      :  str=0.0_1						, __UpperCamelCase      :  Any=0.0_2						, __UpperCamelCase      :  str=1e-5						, __UpperCamelCase      :  Optional[int]=0						, **__UpperCamelCase      :  Any						, )		->     int:
											A      						=    vocab_size
											A      						=    []
											self.cutoffs.extend(__UpperCamelCase       )
											if proj_share_all_but_first:
																		A      						=    [False] + [True] * len(self.cutoffs       )
											else:
																		A      						=    [False] + [False] * len(self.cutoffs       )
											A      						=    d_model
											A      						=    d_embed
											A      						=    d_head
											A      						=    d_inner
											A      						=    div_val
											A      						=    pre_lnorm
											A      						=    n_layer
											A      						=    n_head
											A      						=    mem_len
											A      						=    same_length
											A      						=    attn_type
											A      						=    clamp_len
											A      						=    sample_softmax
											A      						=    adaptive
											A      						=    dropout
											A      						=    dropatt
											A      						=    untie_r
											A      						=    init
											A      						=    init_range
											A      						=    proj_init_std
											A      						=    init_std
											A      						=    layer_norm_epsilon
											super().__init__(eos_token_id=__UpperCamelCase						, **__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  Any       )		->     Optional[int]:
											# Message copied from Transformer-XL documentation
											logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )
											return -1


				@max_position_embeddings.setter
				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  List[Any]       )		->     List[Any]:
											# Message copied from Transformer-XL documentation
											raise NotImplementedError(
											    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )