
from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}




class 				A__ (			__a):
		A_		: List[str]            =			'''xlm-roberta'''

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=3_05_22 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE="absolute" ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(pad_token_id=snake_case__ ,	bos_token_id=snake_case__ ,	eos_token_id=snake_case__ ,	**snake_case__				)

			__lowerCAmelCase    :		int		    =   vocab_size
			__lowerCAmelCase    :		Dict		    =   hidden_size
			__lowerCAmelCase    :		Union[str, Any]		    =   num_hidden_layers
			__lowerCAmelCase    :		Optional[Any]		    =   num_attention_heads
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Union[str, Any]		    =   intermediate_size
			__lowerCAmelCase    :		Optional[Any]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Union[str, Any]		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		str		    =   max_position_embeddings
			__lowerCAmelCase    :		int		    =   type_vocab_size
			__lowerCAmelCase    :		int		    =   initializer_range
			__lowerCAmelCase    :		Dict		    =   layer_norm_eps
			__lowerCAmelCase    :		Any		    =   position_embedding_type
			__lowerCAmelCase    :		Any		    =   use_cache
			__lowerCAmelCase    :		Any		    =   classifier_dropout




class 				A__ (			__a):

		@property
		def 							__lowerCamelCase							(   self				):
			if self.task == "multiple-choice":
				__lowerCAmelCase    :		Optional[int]		    =   {0: 'batch', 1: 'choice', 2: 'sequence'}
			else:
				__lowerCAmelCase    :		Dict		    =   {0: 'batch', 1: 'sequence'}
			return OrderedDict(
			    [
			        ('input_ids', dynamic_axis),
			        ('attention_mask', dynamic_axis),
			    ]				)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()