



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)










import cmath
import math






def     a(      A       :    float ,     A       :    float ,     A       :    float ,     A       :    float       )		->       complex:
					"""simple docstring"""





					a					     =   math.radians(A       )
					a					     =   math.radians(A       )

					# Convert voltage and current to rectangular form
					a					     =   cmath.rect(A ,     A       )
					a					     =   cmath.rect(A ,     A       )

					# Calculate apparent power
					return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()
