




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
_lowerCAmelCase						:   Dict		=   "src/transformers"
_lowerCAmelCase						:   List[str]		=   "docs/source/en"
_lowerCAmelCase						:   List[Any]		=   "."



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Any	):




 """simple docstring"""







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

 UpperCAmelCase__							 =						start_index
 while not lines[end_index].startswith(_lowerCAmelCase	):
  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 |
_lowerCAmelCase						:   Any		=   "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowerCAmelCase						:   Dict		=   re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowerCAmelCase						:   int		=   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.
_lowerCAmelCase						:   Union[str, Any]		=   re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
_lowerCAmelCase						:   Any		=   direct_transformers_import(TRANSFORMERS_PATH)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 UpperCAmelCase__							 =						{
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 UpperCAmelCase__							 =						{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.
 UpperCAmelCase__							 =						collections.defaultdict(_lowerCAmelCase	)
 UpperCAmelCase__							 =						collections.defaultdict(_lowerCAmelCase	)
 UpperCAmelCase__							 =						collections.defaultdict(_lowerCAmelCase	)
 UpperCAmelCase__							 =						collections.defaultdict(_lowerCAmelCase	)
 UpperCAmelCase__							 =						collections.defaultdict(_lowerCAmelCase	)

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

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

    # Let's build that table!
 UpperCAmelCase__							 =						list(model_name_to_config.keys()	)
 model_names.sort(key=str.lower	)
 UpperCAmelCase__							 =						["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).
 UpperCAmelCase__							 =						[len(_lowerCAmelCase	) + 2 for c in columns]
 UpperCAmelCase__							 =						max([len(_lowerCAmelCase	) for name in model_names]	) + 2

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

 UpperCAmelCase__							 =						{True: "✅", False: "❌"}
 for name in model_names:
  UpperCAmelCase__							 =						model_name_to_prefix[name]
  UpperCAmelCase__							 =						[
      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(_lowerCAmelCase						,							_lowerCAmelCase	) for l, w in zip(_lowerCAmelCase						,							_lowerCAmelCase	)]	) + "|\n"
 return table



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]=False	):




 """simple docstring"""







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

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(_lowerCAmelCase						,							"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__":
  _lowerCAmelCase						:   str		=   argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  _lowerCAmelCase						:   Any		=   parser.parse_args()

  check_model_table(args.fix_and_overwrite)











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Dict		=   argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  _lowerCAmelCase						:   Union[str, Any]		=   parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase						:   Union[str, Any]		=   os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
    _lowerCAmelCase						:   List[str]		=   F'''./DialoGPT-{MODEL}'''
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )





