import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		"""src/transformers"""
SCREAMING_SNAKE_CASE			:						Dict				  =		"""docs/source/en"""
SCREAMING_SNAKE_CASE			:						List[str]				  =		"""."""



def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 with open(_A				,					"r"				,					encoding="utf-8"				,					newline="\n"       ) as f:
  __a							=	f.readlines()
 # Find the start prompt.
 __a							=	0
 while not lines[start_index].startswith(_A       ):
  start_index += 1
 start_index += 1

 __a							=	start_index
 while not lines[end_index].startswith(_A       ):
  end_index += 1
 end_index -= 1

 while len(lines[start_index]       ) <= 1:
  start_index += 1
 while len(lines[end_index]       ) <= 1:
  end_index -= 1
 end_index += 1
 return "".join(lines[start_index:end_index]       ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE			:						List[Any]				  =		re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE			:						List[Any]				  =		re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE			:						Optional[int]				  =		direct_transformers_import(TRANSFORMERS_PATH)



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"				,					_A       )
 return [m.group(0       ) for m in matches]



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	2 if text == "✅" or text == "❌" else len(_A       )
 __a							=	(width - text_length) // 2
 __a							=	width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent



def 	__A		(      ):



 """simple docstring"""


 __a							=	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 __a							=	{
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 __a							=	{name: config.replace("Config"				,					""       ) for name, config in model_name_to_config.items()}

 # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
 __a							=	collections.defaultdict(_A       )
 __a							=	collections.defaultdict(_A       )
 __a							=	collections.defaultdict(_A       )
 __a							=	collections.defaultdict(_A       )
 __a							=	collections.defaultdict(_A       )

 # Let's lookup through all transformers object (once).
 for attr_name in dir(_A       ):
  __a							=	None
  if attr_name.endswith("Tokenizer"       ):
   __a							=	slow_tokenizers
   __a							=	attr_name[:-9]
  elif attr_name.endswith("TokenizerFast"       ):
   __a							=	fast_tokenizers
   __a							=	attr_name[:-13]
  elif _re_tf_models.match(_A       ) is not None:
   __a							=	tf_models
   __a							=	_re_tf_models.match(_A       ).groups()[0]
  elif _re_flax_models.match(_A       ) is not None:
   __a							=	flax_models
   __a							=	_re_flax_models.match(_A       ).groups()[0]
  elif _re_pt_models.match(_A       ) is not None:
   __a							=	pt_models
   __a							=	_re_pt_models.match(_A       ).groups()[0]

  if lookup_dict is not None:
   while len(_A       ) > 0:
    if attr_name in model_name_to_prefix.values():
     __a							=	True
     break
    # Try again after removing the last word in the name
    __a							=	"".join(camel_case_split(_A       )[:-1]       )

    # Let's build that table!
 __a							=	list(model_name_to_config.keys()       )
 model_names.sort(key=str.lower       )
 __a							=	["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
 # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
 __a							=	[len(_A       ) + 2 for c in columns]
 __a							=	max([len(_A       ) for name in model_names]       ) + 2

 # Build the table per se
 __a							=	"|" + "|".join([_center_text(_A				,					_A       ) for c, w in zip(_A				,					_A       )]       ) + "|\n"
 # Use ":-----:" format to center-aligned table cell texts
 table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]       ) + "|\n"

 __a							=	{True: "✅", False: "❌"}
 for name in model_names:
  __a							=	model_name_to_prefix[name]
  __a							=	[
      name,
      check[slow_tokenizers[prefix]],
      check[fast_tokenizers[prefix]],
      check[pt_models[prefix]],
      check[tf_models[prefix]],
      check[flax_models[prefix]],
  ]
  table += "|" + "|".join([_center_text(_A				,					_A       ) for l, w in zip(_A				,					_A       )]       ) + "|\n"
 return table



def 	__A		(      _A=False       ):



 """simple docstring"""


 __a							,  __a							,  __a							,  __a							=	_find_text_in_file(
     filename=os.path.join(_A				,					"index.md"       )				,					start_prompt="<!--This table is updated automatically from the auto modules"				,					end_prompt="<!-- End table-->"				,					)
 __a							=	get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(_A				,					"index.md"       )				,					"w"				,					encoding="utf-8"				,					newline="\n"       ) as f:
    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]       )
  else:
   raise ValueError(
       "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Dict				  =		argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  SCREAMING_SNAKE_CASE			:						str				  =		parser.parse_args()

  check_model_table(args.fix_and_overwrite)



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

 # We have the base model one level deeper than the original XLM repository
 __a							=	{}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   __a							=	v
  else:
   __a							=	v

 __a							=	chkpt["params"]
 __a							=	{n: v for n, v in config.items() if not isinstance(_A				,					(torch.FloatTensor, numpy.ndarray)       )}

 __a							=	chkpt["dico_word2id"]
 __a							=	{s + "</w>" if s.find("@@"       ) == -1 and i > 13 else s.replace("@@"				,					""       ): i for s, i in vocab.items()}

 # Save pytorch-model
 __a							=	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 __a							=	pytorch_dump_folder_path + "/" + CONFIG_NAME
 __a							=	pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

 print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
 torch.save(_A				,					_A       )

 print(f"""Save configuration file to {pytorch_config_dump_path}"""       )
 with open(_A				,					"w"				,					encoding="utf-8"       ) as f:
  f.write(json.dumps(_A				,					indent=2       ) + "\n"       )

 print(f"""Save vocab file to {pytorch_config_dump_path}"""       )
 with open(_A				,					"w"				,					encoding="utf-8"       ) as f:
  f.write(json.dumps(_A				,					indent=2       ) + "\n"       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


