from ..utils import DummyObject, requires_backends




class  __lowerCAmelCase	(							metaclass=a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			['tensorflow_text']

 def __init__(      self							:		int							,      *lowerCamelCase							:		Dict							,      **lowerCamelCase							:		List[Any]	)			->				Optional[Any]:
        requires_backends(self							,      ['tensorflow_text']	)




from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						List[Any]					     =			'flaubert'
 _lowerCamelCase    :						List[str]					     =			{
     'hidden_size': 'emb_dim',
     'num_attention_heads': 'n_heads',
     'num_hidden_layers': 'n_layers',
     'n_words': 'vocab_size',  # For backward compatibility
 }

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		Tuple=False							,      lowerCamelCase							:		str=0.0							,      lowerCamelCase							:		int=3_0_1_4_5							,      lowerCamelCase							:		List[str]=2_0_4_8							,      lowerCamelCase							:		List[str]=1_2							,      lowerCamelCase							:		Dict=1_6							,      lowerCamelCase							:		Any=0.1							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Any=False							,      lowerCamelCase							:		Optional[int]=False							,      lowerCamelCase							:		int=False							,      lowerCamelCase							:		Optional[Any]=1							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		str=5_1_2							,      lowerCamelCase							:		Tuple=2_0_4_8**-0.5							,      lowerCamelCase							:		str=1e-12							,      lowerCamelCase							:		Any=0.0_2							,      lowerCamelCase							:		Dict=0							,      lowerCamelCase							:		Union[str, Any]=1							,      lowerCamelCase							:		List[Any]=2							,      lowerCamelCase							:		Union[str, Any]=3							,      lowerCamelCase							:		List[Any]=5							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		Dict="first"							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		Any=None							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		List[Any]=5							,      lowerCamelCase							:		Optional[Any]=5							,      lowerCamelCase							:		str=0							,      lowerCamelCase							:		Optional[int]=0							,      lowerCamelCase							:		List[str]=2							,      lowerCamelCase							:		Any=0							,      **lowerCamelCase							:		Tuple							,      )			->				Any:
        _UpperCamelCase  :							Optional[Any]	    =	pre_norm
        _UpperCamelCase  :							Optional[int]	    =	layerdrop
        _UpperCamelCase  :							Dict	    =	vocab_size
        _UpperCamelCase  :							Dict	    =	emb_dim
        _UpperCamelCase  :							int	    =	n_layers
        _UpperCamelCase  :							Tuple	    =	n_heads
        _UpperCamelCase  :							Tuple	    =	dropout
        _UpperCamelCase  :							Any	    =	attention_dropout
        _UpperCamelCase  :							Union[str, Any]	    =	gelu_activation
        _UpperCamelCase  :							Dict	    =	sinusoidal_embeddings
        _UpperCamelCase  :							str	    =	causal
        _UpperCamelCase  :							str	    =	asm
        _UpperCamelCase  :							Tuple	    =	n_langs
        _UpperCamelCase  :							Optional[int]	    =	use_lang_emb
        _UpperCamelCase  :							Optional[Any]	    =	layer_norm_eps
        _UpperCamelCase  :							Optional[Any]	    =	bos_index
        _UpperCamelCase  :							Dict	    =	eos_index
        _UpperCamelCase  :							Union[str, Any]	    =	pad_index
        _UpperCamelCase  :							Union[str, Any]	    =	unk_index
        _UpperCamelCase  :							Optional[Any]	    =	mask_index
        _UpperCamelCase  :							Any	    =	is_encoder
        _UpperCamelCase  :							List[str]	    =	max_position_embeddings
        _UpperCamelCase  :							Union[str, Any]	    =	embed_init_std
        _UpperCamelCase  :							Optional[int]	    =	init_std
        _UpperCamelCase  :							Any	    =	summary_type
        _UpperCamelCase  :							Any	    =	summary_use_proj
        _UpperCamelCase  :							Optional[int]	    =	summary_activation
        _UpperCamelCase  :							Union[str, Any]	    =	summary_proj_to_labels
        _UpperCamelCase  :							Dict	    =	summary_first_dropout
        _UpperCamelCase  :							Union[str, Any]	    =	start_n_top
        _UpperCamelCase  :							Any	    =	end_n_top
        _UpperCamelCase  :							List[Any]	    =	mask_token_id
        _UpperCamelCase  :							str	    =	lang_id

        if "n_words" in kwargs:
               _UpperCamelCase  :							Tuple	    =	kwargs['n_words']

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







class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

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



