





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
SCREAMING_SNAKE_CASE__							:      Dict         =    data_utils.TransfoXLTokenizer
SCREAMING_SNAKE_CASE__							:      Any         =    data_utils.TransfoXLCorpus
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    data_utils
SCREAMING_SNAKE_CASE__							:      Dict         =    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:
         a__						:    Any   		=	pickle.load(lowerCamelCase				,			encoding="latin1" )
      # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
      a__						:    Any   		=	pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
      print(F"""Save vocabulary to {pytorch_vocab_dump_path}""" )
      a__						:    Tuple   		=	corpus.vocab.__dict__
      torch.save(lowerCamelCase				,			lowerCamelCase )

      a__						:    str   		=	corpus.__dict__
      corpus_dict_no_vocab.pop("vocab"				,			lowerCamelCase )
      a__						:    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
      a__						:    Any   		=	os.path.abspath(lowerCamelCase )
      a__						:    Dict   		=	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 == "":
         a__						:    Union[str, Any]   		=	TransfoXLConfig()
      else:
         a__						:    List[Any]   		=	TransfoXLConfig.from_json_file(lowerCamelCase )
      print(F"""Building PyTorch model from configuration: {config}""" )
      a__						:    int   		=	TransfoXLLMHeadModel(lowerCamelCase )

      a__						:    List[str]   		=	load_tf_weights_in_transfo_xl(lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )
      # Save pytorch-model
      a__						:    Tuple   		=	os.path.join(lowerCamelCase				,			lowerCamelCase )
      a__						:    int   		=	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__":
  SCREAMING_SNAKE_CASE__							:      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.""",
  )
  SCREAMING_SNAKE_CASE__							:      Union[str, 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,
  )








import argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

