



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

def      lowerCAmelCase_					(  _lowercase    :       Optional[int]						,       _lowercase    :       Tuple						,       _lowercase    :       Tuple)							->	Optional[Any]:







			"""simple docstring"""





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

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


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

def      lowerCAmelCase_					(  _lowercase    :       Any)							->	Optional[int]:







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

def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       List[str])							->	List[Any]:







			"""simple docstring"""
			a__       :						Optional[int]							=  2 if text == '''✅''' or text == '''❌''' else len(_snake_case)
			a__       :						str							=  (width - text_length) // 2
			a__       :						List[str]							=  width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent

def      lowerCAmelCase_					(  )							->	List[str]:







			"""simple docstring"""
			a__       :						Optional[int]							=  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			a__       :						Optional[int]							=  {
			    name: config_maping_names[code]
			    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_maping_names
			}
			a__       :						Union[str, Any]							=  {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__       :						List[Any]							=  collections.defaultdict(_snake_case)
			a__       :						List[Any]							=  collections.defaultdict(_snake_case)
			a__       :						Optional[Any]							=  collections.defaultdict(_snake_case)
			a__       :						Optional[int]							=  collections.defaultdict(_snake_case)
			a__       :						Any							=  collections.defaultdict(_snake_case)

			# Let's lookup through all transformers object (once).
			for attr_name in dir(_snake_case):
						a__       :						List[str]							=  None
						if attr_name.endswith("""Tokenizer"""):
									a__       :						Tuple							=  slow_tokenizers
									a__       :						Union[str, Any]							=  attr_name[:-9]
						elif attr_name.endswith("""TokenizerFast"""):
									a__       :						List[Any]							=  fast_tokenizers
									a__       :						Optional[int]							=  attr_name[:-13]
						elif _re_tf_models.match(_snake_case) is not None:
									a__       :						List[Any]							=  tf_models
									a__       :						int							=  _re_tf_models.match(_snake_case).groups()[0]
						elif _re_flax_models.match(_snake_case) is not None:
									a__       :						Tuple							=  flax_models
									a__       :						Optional[int]							=  _re_flax_models.match(_snake_case).groups()[0]
						elif _re_pt_models.match(_snake_case) is not None:
									a__       :						Optional[Any]							=  pt_models
									a__       :						List[Any]							=  _re_pt_models.match(_snake_case).groups()[0]

						if lookup_dict is not None:
									while len(_snake_case) > 0:
												if attr_name in model_name_to_prefix.values():
															a__       :						Optional[int]							=  True
															break
												# Try again after removing the last word in the name
												a__       :						Dict							=  ''''''.join(camel_case_split(_snake_case)[:-1])

    # Let's build that table!
			a__       :						int							=  list(model_name_to_config.keys())
			model_names.sort(key=str.lower)
			a__       :						List[Any]							=  ['''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__       :						int							=  [len(_snake_case) + 2 for c in columns]
			a__       :						List[Any]							=  max([len(_snake_case) for name in model_names]) + 2

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

			a__       :						Optional[int]							=  {True: '''✅''', False: '''❌'''}
			for name in model_names:
						a__       :						Optional[Any]							=  model_name_to_prefix[name]
						a__       :						Optional[Any]							=  [
						    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(_snake_case						,       _snake_case) for l, w in zip(_snake_case						,       _snake_case)]) + "|\n"
			return table

def      lowerCAmelCase_					(  _lowercase    :       List[str]=False)							->	Dict:







			"""simple docstring"""
			a__       :						int							=  _find_text_in_file(
			    filename=os.path.join(_snake_case						,       """index.md""")						,       start_prompt="""<!--This table is updated automatically from the auto modules"""						,       end_prompt="""<!-- End table-->"""						,       )
			a__       :						Optional[Any]							=  get_model_table_from_auto_modules()

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

							check_model_table(args.fix_and_overwrite)









import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


_lowercase		:				Union[str, Any]      		=logging.get_logger(__name__)

class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       *__lowercase ,       **__lowercase  ) ->				None:
									"""simple docstring"""




									warnings.warn(
									    """The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
									    """ Please use MobileViTImageProcessor instead.""" ,       __lowercase ,       )
									super().__init__(*__lowercase ,       **__lowercase  )




