






"""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
__a       :	int             = 'src/transformers'
__a       :	Any             = 'docs/source/en'
__a       :	Dict             = '.'





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


# This is to make sure the transformers module imported is the one in the repo.
__a       :	Optional[int]             = direct_transformers_import(TRANSFORMERS_PATH)





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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					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        SCREAMING_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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_=False):
					a__       ,a__       ,a__       ,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__":
							__a       :	List[Any]             = argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							__a       :	List[str]             = parser.parse_args()

							check_model_table(args.fix_and_overwrite)










"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a       :	str             = logging.get_logger(__name__)

__a       :	Dict             = {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='xlm'
							_SCREAMING_SNAKE_CASE    ={
							    'hidden_size': 'emb_dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							    'n_words': 'vocab_size',  # For backward compatibility
							}




							def __init__(   self:     Optional[Any]				,		__A:     str=30145				,		__A:     Union[str, Any]=2048				,		__A:     Union[str, Any]=12				,		__A:     List[Any]=16				,		__A:     List[Any]=0.1				,		__A:     int=0.1				,		__A:     str=True				,		__A:     int=False				,		__A:     Optional[int]=False				,		__A:     Union[str, Any]=False				,		__A:     List[Any]=1				,		__A:     Dict=True				,		__A:     List[Any]=512				,		__A:     Optional[int]=2048**-0.5				,		__A:     Tuple=1e-12				,		__A:     Optional[int]=0.0_2				,		__A:     Optional[int]=0				,		__A:     Union[str, Any]=1				,		__A:     List[Any]=2				,		__A:     Union[str, Any]=3				,		__A:     Tuple=5				,		__A:     Tuple=True				,		__A:     Dict="first"				,		__A:     Optional[int]=True				,		__A:     int=None				,		__A:     Optional[Any]=True				,		__A:     Dict=0.1				,		__A:     Tuple=5				,		__A:     Union[str, Any]=5				,		__A:     Tuple=0				,		__A:     List[str]=0				,		__A:     int=2				,		__A:     Optional[int]=0				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  vocab_size
												a__          =  emb_dim
												a__          =  n_layers
												a__          =  n_heads
												a__          =  dropout
												a__          =  attention_dropout
												a__          =  gelu_activation
												a__          =  sinusoidal_embeddings
												a__          =  causal
												a__          =  asm
												a__          =  n_langs
												a__          =  use_lang_emb
												a__          =  layer_norm_eps
												a__          =  bos_index
												a__          =  eos_index
												a__          =  pad_index
												a__          =  unk_index
												a__          =  mask_index
												a__          =  is_encoder
												a__          =  max_position_embeddings
												a__          =  embed_init_std
												a__          =  init_std
												a__          =  summary_type
												a__          =  summary_use_proj
												a__          =  summary_activation
												a__          =  summary_proj_to_labels
												a__          =  summary_first_dropout
												a__          =  start_n_top
												a__          =  end_n_top
												a__          =  mask_token_id
												a__          =  lang_id

												if "n_words" in kwargs:
																	a__          =  kwargs['''n_words''']

												super().__init__(pad_token_id=__A				,		bos_token_id=__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							@property
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												if self.task == "multiple-choice":
																	a__          =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	a__          =  {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]    )


