




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    =	"src/transformers"
SCREAMING_SNAKE_CASE    =	"docs/source/en"
SCREAMING_SNAKE_CASE    =	"."







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

 A__	=    start_index
 while not lines[end_index].startswith(UpperCAmelCase__						):
  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    =	"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE    =	re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
SCREAMING_SNAKE_CASE    =	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    =	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    =	direct_transformers_import(TRANSFORMERS_PATH)







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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		List[Any]:
 A__	=    2 if text == """✅""" or text == """❌""" else len(UpperCAmelCase__						)
 A__	=    (width - text_length) // 2
 A__	=    width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent







def   _SCREAMING_SNAKE_CASE       (							)						->		Any:
 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(UpperCAmelCase__						)
 A__	=    collections.defaultdict(UpperCAmelCase__						)
 A__	=    collections.defaultdict(UpperCAmelCase__						)
 A__	=    collections.defaultdict(UpperCAmelCase__						)
 A__	=    collections.defaultdict(UpperCAmelCase__						)

 # Let's lookup through all transformers object (once).
 for attr_name in dir(UpperCAmelCase__						):
  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(UpperCAmelCase__						) is not None:
   A__	=    tf_models
   A__	=    _re_tf_models.match(UpperCAmelCase__						).groups()[0]
  elif _re_flax_models.match(UpperCAmelCase__						) is not None:
   A__	=    flax_models
   A__	=    _re_flax_models.match(UpperCAmelCase__						).groups()[0]
  elif _re_pt_models.match(UpperCAmelCase__						) is not None:
   A__	=    pt_models
   A__	=    _re_pt_models.match(UpperCAmelCase__						).groups()[0]

  if lookup_dict is not None:
   while len(UpperCAmelCase__						) > 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(UpperCAmelCase__						)[:-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(UpperCAmelCase__						) + 2 for c in columns]
 A__	=    max([len(UpperCAmelCase__						) for name in model_names]						) + 2

 # Build the table per se
 A__	=    """|""" + """|""".join([_center_text(UpperCAmelCase__      ,			UpperCAmelCase__						) for c, w in zip(UpperCAmelCase__      ,			UpperCAmelCase__						)]						) + """|\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(UpperCAmelCase__      ,			UpperCAmelCase__						) for l, w in zip(UpperCAmelCase__      ,			UpperCAmelCase__						)]						) + "|\n"
 return table







def   _SCREAMING_SNAKE_CASE       (							lowercase_=False						)						->		str:
 A__	=    _find_text_in_file(
     filename=os.path.join(UpperCAmelCase__      ,			"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(UpperCAmelCase__      ,			"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    =	argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

      check_model_table(args.fix_and_overwrite)











"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand







def   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 A__	=    ArgumentParser("Diffusers CLI tool"      ,			usage="diffusers-cli <command> [<args>]"						)
 A__	=    parser.add_subparsers(help="diffusers-cli command helpers"						)

 # Register commands
 EnvironmentCommand.register_subcommand(lowercase_						)

 # Let's go
 A__	=    parser.parse_args()

 if not hasattr(lowercase_      ,			"func"						):
  parser.print_help()
  exit(1						)

 # Run
 A__	=    args.func(lowercase_						)
 service.run()


if __name__ == "__main__":
      main()





