


from collections import OrderedDict
from typing import Mapping

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


A_     :       Dict   							=logging.get_logger(__name__)

A_     :       Optional[Any]   							={
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class 							lowercase_					(			a__):






							"""simple docstring"""







							snake_case_										=		"""big_bird"""


							def __init__( self      ,     _UpperCAmelCase=50_358      ,     _UpperCAmelCase=768      ,     _UpperCAmelCase=12      ,     _UpperCAmelCase=12      ,     _UpperCAmelCase=3_072      ,     _UpperCAmelCase="gelu_new"      ,     _UpperCAmelCase=0.1      ,     _UpperCAmelCase=0.1      ,     _UpperCAmelCase=4_096      ,     _UpperCAmelCase=2      ,     _UpperCAmelCase=0.0_2      ,     _UpperCAmelCase=1e-1_2      ,     _UpperCAmelCase=True      ,     _UpperCAmelCase=0      ,     _UpperCAmelCase=1      ,     _UpperCAmelCase=2      ,     _UpperCAmelCase=66      ,     _UpperCAmelCase="block_sparse"      ,     _UpperCAmelCase=True      ,     _UpperCAmelCase=False      ,     _UpperCAmelCase=64      ,     _UpperCAmelCase=3      ,     _UpperCAmelCase=None      ,     **_UpperCAmelCase      ,     ):







													"""simple docstring"""


													super().__init__(
													    pad_token_id=__a      ,     bos_token_id=__a      ,     eos_token_id=__a      ,     sep_token_id=__a      ,     **__a      ,     )

													a_		      =		vocab_size
													a_		      =		max_position_embeddings
													a_		      =		hidden_size
													a_		      =		num_hidden_layers
													a_		      =		num_attention_heads
													a_		      =		intermediate_size
													a_		      =		hidden_act
													a_		      =		hidden_dropout_prob
													a_		      =		attention_probs_dropout_prob
													a_		      =		initializer_range
													a_		      =		type_vocab_size
													a_		      =		layer_norm_eps
													a_		      =		use_cache

													a_		      =		rescale_embeddings
													a_		      =		attention_type
													a_		      =		use_bias
													a_		      =		block_size
													a_		      =		num_random_blocks
													a_		      =		classifier_dropout





class 							lowercase_					(			a__):






							"""simple docstring"""







							@property
							def     lowercase__  ( self		):







													"""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),
													    ]		)





'''simple docstring'''







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase      :   int      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""masked_bert"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=2							,__a=0.02							,__a=1E-12							,__a=0							,__a="topK"							,__a="constant"							,__a=0.0							,**__a							,)      ->				List[str]:
          super().__init__(pad_token_id=__a							,**__a							)

          snake_case			:      Dict        =			vocab_size
          snake_case			:      Optional[Any]        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      List[Any]        =			num_attention_heads
          snake_case			:      Dict        =			hidden_act
          snake_case			:      Any        =			intermediate_size
          snake_case			:      Optional[int]        =			hidden_dropout_prob
          snake_case			:      Optional[Any]        =			attention_probs_dropout_prob
          snake_case			:      List[Any]        =			max_position_embeddings
          snake_case			:      int        =			type_vocab_size
          snake_case			:      int        =			initializer_range
          snake_case			:      int        =			layer_norm_eps
          snake_case			:      Any        =			pruning_method
          snake_case			:      Union[str, Any]        =			mask_init
          snake_case			:      int        =			mask_scale



