



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






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

						__lowercase					=				start_index
						while not lines[end_index].startswith(lowercase__   ):
												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 |
UpperCamelCase__ =						"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__ =						re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
UpperCamelCase__ =						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.
UpperCamelCase__ =						re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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






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






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Union[str, Any]:
						__lowercase					=				2 if text == """✅""" or text == """❌""" else len(lowercase__   )
						__lowercase					=				(width - text_length) // 2
						__lowercase					=				width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent






def  UpperCAmelCase__    (    ) ->      Optional[Any]:
						__lowercase					=				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						__lowercase					=				{
						    name: config_maping_names[code]
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_maping_names
						}
						__lowercase					=				{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.
						__lowercase					=				collections.defaultdict(lowercase__   )
						__lowercase					=				collections.defaultdict(lowercase__   )
						__lowercase					=				collections.defaultdict(lowercase__   )
						__lowercase					=				collections.defaultdict(lowercase__   )
						__lowercase					=				collections.defaultdict(lowercase__   )

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

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

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

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

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






def  UpperCAmelCase__    (    lowercase__=False   ) ->      List[Any]:
						__lowercase	,			__lowercase	,			__lowercase	,			__lowercase					=				_find_text_in_file(
						    filename=os.path.join(lowercase__						,       """index.md"""   )						,       start_prompt="""<!--This table is updated automatically from the auto modules"""						,       end_prompt="""<!-- End table-->"""						,       )
						__lowercase					=				get_model_table_from_auto_modules()

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

			check_model_table(args.fix_and_overwrite)










def  UpperCAmelCase__    (    lowercase__ = 100   ) ->      int:
						__lowercase					=				n * (n + 1) * (2 * n + 1) / 6
						__lowercase					=				(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares   )


if __name__ == "__main__":
			print(F"""{solution() = }""")





