




import argparse
import os
import re


__A								=       "src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__A								=       re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
__A								=       re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")







def      lowerCamelCase_						(			UpperCamelCase__							: Tuple					,	UpperCamelCase__							: bool = False )	->			str:





				"""simple docstring"""


				with open(UpperCamelCase__					,	'r'					,	encoding='utf-8' ) as f:
								__lowerCamelCase									=       f.read()

				__lowerCamelCase									=       content.split('\n' )
				__lowerCamelCase									=       []
				__lowerCamelCase									=       0
				while line_idx < len(UpperCamelCase__ ):
								if _re_intro_mapping.search(lines[line_idx] ) is not None:
												__lowerCamelCase									=       len(re.search(R'^(\s*)\S'					,	lines[line_idx] ).groups()[0] ) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(' ' * indent + '(' ):
																new_lines.append(lines[line_idx] )
																line_idx += 1

												__lowerCamelCase									=       []
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				__lowerCamelCase									=       line_idx
																				while not lines[line_idx].startswith(' ' * indent + ')' ):
																								line_idx += 1
																				blocks.append('\n'.join(lines[start_idx : line_idx + 1] ) )
																else:
																				blocks.append(lines[line_idx] )
																line_idx += 1

												# Sort blocks by their identifiers
												__lowerCamelCase									=       sorted(UpperCamelCase__					,	key=lambda UpperCamelCase__ : _re_identifier.search(UpperCamelCase__ ).groups()[0] )
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx] )
												line_idx += 1

				if overwrite:
								with open(UpperCamelCase__					,	'w'					,	encoding='utf-8' ) as f:
												f.write('\n'.join(UpperCamelCase__ ) )
				elif "\n".join(UpperCamelCase__ ) != content:
								return True







def      lowerCamelCase_						(			UpperCamelCase__							: bool = False )	->			Any:





				"""simple docstring"""


				__lowerCamelCase									=       [os.path.join(UpperCamelCase__					,	UpperCamelCase__ ) for f in os.listdir(UpperCamelCase__ ) if f.endswith('.py' )]
				__lowerCamelCase									=       [sort_auto_mapping(UpperCamelCase__					,	overwrite=UpperCamelCase__ ) for fname in fnames]

				if not overwrite and any(UpperCamelCase__ ):
								__lowerCamelCase									=       [f for f, d in zip(UpperCamelCase__					,	UpperCamelCase__ ) if d]
								raise ValueError(
								    F"""The following files have auto mappings that need sorting: {', '.join(UpperCamelCase__ )}. Run `make style` to fix"""
								    ' this.' )


if __name__ == "__main__":
					__A								=       argparse.ArgumentParser()
					parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
					__A								=       parser.parse_args()

					sort_all_auto_mappings(not args.check_only)








from collections import OrderedDict
from typing import Mapping

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


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''xlm-roberta-xl'''


			def __init__( self     ,		lowerCamelCase__=250_880     ,		lowerCamelCase__=2_560     ,		lowerCamelCase__=36     ,		lowerCamelCase__=32     ,		lowerCamelCase__=10_240     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=514     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-05     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0     ,		lowerCamelCase__=2     ,		lowerCamelCase__="absolute"     ,		lowerCamelCase__=True     ,		lowerCamelCase__=None     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__     ,		bos_token_id=lowerCamelCase__     ,		eos_token_id=lowerCamelCase__     ,		**lowerCamelCase__				)
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       position_embedding_type
							__lowerCamelCase									=       use_cache
							__lowerCamelCase									=       classifier_dropout







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			@property
			def  lowercase_       ( self				)       -> Mapping[str, Mapping[int, str]]:



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


