



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
__snake_case						:List[str]      		=     '''src/transformers'''
__snake_case						:Tuple      		=     '''docs/source/en'''
__snake_case						:Dict      		=     '''.'''





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    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 |
__snake_case						:Union[str, Any]      		=     '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__snake_case						:Union[str, Any]      		=     re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__snake_case						:int      		=     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.
__snake_case						:Tuple      		=     re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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





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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __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      __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(_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      __snake_case	(			_UpperCAmelCase=False				):
    __a       ,   __a       ,   __a       ,   __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__":
 __snake_case						:str      		=     argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 __snake_case						:List[str]      		=     parser.parse_args()

 check_model_table(args.fix_and_overwrite)










from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__snake_case						:Optional[int]      		=     200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__snake_case						:List[str]      		=     50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__snake_case						:List[Any]      		=     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				len([g for position, g in enumerate(_UpperCAmelCase				) if g == main_target[position]]				)
    return (item, float(_UpperCAmelCase				))





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				random.randint(0   , len(_UpperCAmelCase				) - 1				)
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    __a						       =				parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				list(_UpperCAmelCase				)
    if random.uniform(0   , 1				) < MUTATION_PROBABILITY:
        __a						       =				random.choice(_UpperCAmelCase				)
    return "".join(_UpperCAmelCase				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , ):
    __a						       =				[]
    # Generate more children proportionally to the fitness score.
    __a						       =				int(parent_a[1] * 100				) + 1
    __a						       =				10 if child_n >= 10 else child_n
    for _ in range(_UpperCAmelCase				):
        __a						       =				population_score[random.randint(0   , _UpperCAmelCase				)][0]

        __a       ,   __a						       =				crossover(parent_a[0]   , _UpperCAmelCase				)
        # Append new string to the population list.
        pop.append(mutate(_UpperCAmelCase   , _UpperCAmelCase				)				)
        pop.append(mutate(_UpperCAmelCase   , _UpperCAmelCase				)				)
    return pop





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = True				):

    # Verify if N_POPULATION is bigger than N_SELECTED
    if N_POPULATION < N_SELECTED:
        __a						       =				f'{N_POPULATION} must be bigger than {N_SELECTED}'
        raise ValueError(_UpperCAmelCase				)
    # Verify that the target contains no genes besides the ones inside genes variable.
    __a						       =				sorted({c for c in target if c not in genes}				)
    if not_in_genes_list:
        __a						       =				f'{not_in_genes_list} is not in genes list, evolution cannot converge'
        raise ValueError(_UpperCAmelCase				)

    # Generate random starting population.
    __a						       =				[]
    for _ in range(_UpperCAmelCase				):
        population.append(''''''.join([random.choice(_UpperCAmelCase				) for i in range(len(_UpperCAmelCase				)				)]				)				)

    # Just some logs to know what the algorithms is doing.
    __a       ,   __a						       =				0, 0

    # This loop will end when we find a perfect match for our target.
    while True:
        generation += 1
        total_population += len(_UpperCAmelCase				)

        # Random population created. Now it's time to evaluate.

        # Adding a bit of concurrency can make everything faster,
        #
        # import concurrent.futures
        # population_score: list[tuple[str, float]] = []
        # with concurrent.futures.ThreadPoolExecutor(
        #                                   max_workers=NUM_WORKERS) as executor:
        #     futures = {executor.submit(evaluate, item) for item in population}
        #     concurrent.futures.wait(futures)
        #     population_score = [item.result() for item in futures]
        #
        # but with a simple algorithm like this, it will probably be slower.
        # We just need to call evaluate for every item inside the population.
        __a						       =				[evaluate(_UpperCAmelCase   , _UpperCAmelCase				) for item in population]

        # Check if there is a matching evolution.
        __a						       =				sorted(_UpperCAmelCase   , key=lambda _UpperCAmelCase				: x[1]   , reverse=_UpperCAmelCase				)
        if population_score[0][0] == target:
            return (generation, total_population, population_score[0][0])

        # Print the best result every 10 generation.
        # Just to know that the algorithm is working.
        if debug and generation % 10 == 0:
            print(
                f'\nGeneration: {generation}'
                f'\nTotal Population:{total_population}'
                f'\nBest score: {population_score[0][1]}'
                f'\nBest string: {population_score[0][0]}'				)

        # Flush the old population, keeping some of the best evolutions.
        # Keeping this avoid regression of evolution.
        __a						       =				population[: int(N_POPULATION / 3				)]
        population.clear()
        population.extend(_UpperCAmelCase				)
        # Normalize population score to be between 0 and 1.
        __a						       =				[
            (item, score / len(_UpperCAmelCase				)) for item, score in population_score
        ]

        # This is selection
        for i in range(_UpperCAmelCase				):
            population.extend(select(population_score[int(_UpperCAmelCase				)]   , _UpperCAmelCase   , _UpperCAmelCase				)				)
            # Check if the population has already reached the maximum value and if so,
            # break the cycle.  If this check is disabled, the algorithm will take
            # forever to compute large strings, but will also calculate small strings in
            # a far fewer generations.
            if len(_UpperCAmelCase				) > N_POPULATION:
                break


if __name__ == "__main__":
 __snake_case						:Optional[int]      		=     (
     '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
 )
 __snake_case						:List[Any]      		=     list(
     ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
     '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
 )
 __snake_case		,__snake_case		,__snake_case						:Dict      		=     basic(target_str, genes_list)
 print(
     f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
 )





