
"""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
lowerCAmelCase__							=				'src/transformers'
lowerCAmelCase__							=				'docs/source/en'
lowerCAmelCase__							=				'.'



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

		SCREAMING_SNAKE_CASE_						 = start_index
		while not lines[end_index].startswith(__a    ):
				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__							=				'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase__							=				re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase__							=				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__							=				re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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



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



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = 2 if text == '''✅''' or text == '''❌''' else len(__a    )
		SCREAMING_SNAKE_CASE_						 = (width - text_length) // 2
		SCREAMING_SNAKE_CASE_						 = width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent



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

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

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

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

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

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



def        _lowerCamelCase ( __a=False    ):
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = _find_text_in_file(
		    filename=os.path.join(__a,							'''index.md'''    ),							start_prompt='''<!--This table is updated automatically from the auto modules''',							end_prompt='''<!-- End table-->''',							)
		SCREAMING_SNAKE_CASE_						 = get_model_table_from_auto_modules()

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

						check_model_table(args.fix_and_overwrite)

"""simple docstring"""




from __future__ import annotations



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = len(__a    ) - 1

		while i < j:
				if nums[i] + nums[j] == target:
						return [i, j]
				elif nums[i] + nums[j] < target:
						SCREAMING_SNAKE_CASE_						 = i + 1
				else:
						SCREAMING_SNAKE_CASE_						 = j - 1

		return []


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(f'''{two_pointer([2, 7, 11, 15], 9) = }''')