
"""simple docstring"""


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






def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  int						,   snake_case__ :  List[str]  ):
	with open(__lowerCAmelCase						,   'r'						,   encoding='utf-8'						,   newline='\n'  ) as f:
		A     = f.readlines()
	# Find the start prompt.
	A     = 0
	while not lines[start_index].startswith(__lowerCAmelCase  ):
		start_index += 1
	start_index += 1

	A     = 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 |
_lowercase				 =					"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowercase				 =					re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
_lowercase				 =					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				 =					re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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






def    _snake_case					(		snake_case__ :  Any  ):
	A     = re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'						,   __lowerCAmelCase  )
	return [m.group(0  ) for m in matches]






def    _snake_case					(		snake_case__ :  int						,   snake_case__ :  List[str]  ):
	A     = 2 if text == '✅' or text == '❌' else len(__lowerCAmelCase  )
	A     = (width - text_length) // 2
	A     = width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def    _snake_case					(		):
	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(__lowerCAmelCase  )
	A     = collections.defaultdict(__lowerCAmelCase  )
	A     = collections.defaultdict(__lowerCAmelCase  )
	A     = collections.defaultdict(__lowerCAmelCase  )
	A     = collections.defaultdict(__lowerCAmelCase  )

	# Let's lookup through all transformers object (once).
	for attr_name in dir(__lowerCAmelCase  ):
		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(__lowerCAmelCase  ) is not None:
			A     = tf_models
			A     = _re_tf_models.match(__lowerCAmelCase  ).groups()[0]
		elif _re_flax_models.match(__lowerCAmelCase  ) is not None:
			A     = flax_models
			A     = _re_flax_models.match(__lowerCAmelCase  ).groups()[0]
		elif _re_pt_models.match(__lowerCAmelCase  ) is not None:
			A     = pt_models
			A     = _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():
					A     = True
					break
				# Try again after removing the last word in the name
				A     = ''.join(camel_case_split(__lowerCAmelCase  )[:-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(__lowerCAmelCase  ) + 2 for c in columns]
	A     = max([len(__lowerCAmelCase  ) for name in model_names]  ) + 2

	# Build the table per se
	A     = '|' + '|'.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"

	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(__lowerCAmelCase						,   __lowerCAmelCase  ) for l, w in zip(__lowerCAmelCase						,   __lowerCAmelCase  )]  ) + "|\n"
	return table






def    _snake_case					(		snake_case__ :  Optional[Any]=False  ):
	A     = _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-->'						,   )
	A     = 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__":
		_lowercase				 =					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		_lowercase				 =					parser.parse_args()

		check_model_table(args.fix_and_overwrite)




from __future__ import annotations

import numpy as np

def        __a			(   __lowerCAmelCase   )     ->     Optional[Any]:
					return np.maximum(0      ,	__lowerCAmelCase   )


if __name__ == "__main__":
			print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]