




from typing import Dict

from .base import GenericTensor, Pipeline




class       _A				(		__lowercase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								if tokenize_kwargs is None:
														_UpperCAmelCase      =			{}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
														_UpperCAmelCase      =			truncation

								_UpperCAmelCase      =			tokenize_kwargs

								_UpperCAmelCase      =			{}
								if return_tensors is not None:
														_UpperCAmelCase      =			return_tensors

								return preprocess_params, {}, postprocess_params






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.framework
								_UpperCAmelCase      =			self.tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)





import logging

from transformers.configuration_utils import PretrainedConfig


a													=			logging.getLogger(__name__)




class       _A				(		__lowercase     ):
		__a					    =       """masked_bert"""






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3_0522						,		_SCREAMING_SNAKE_CASE=768						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=3072						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-12						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE="topK"						,		_SCREAMING_SNAKE_CASE="constant"						,		_SCREAMING_SNAKE_CASE=0.0						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			vocab_size
								_UpperCAmelCase      =			hidden_size
								_UpperCAmelCase      =			num_hidden_layers
								_UpperCAmelCase      =			num_attention_heads
								_UpperCAmelCase      =			hidden_act
								_UpperCAmelCase      =			intermediate_size
								_UpperCAmelCase      =			hidden_dropout_prob
								_UpperCAmelCase      =			attention_probs_dropout_prob
								_UpperCAmelCase      =			max_position_embeddings
								_UpperCAmelCase      =			type_vocab_size
								_UpperCAmelCase      =			initializer_range
								_UpperCAmelCase      =			layer_norm_eps
								_UpperCAmelCase      =			pruning_method
								_UpperCAmelCase      =			mask_init
								_UpperCAmelCase      =			mask_scale