
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'Speech2TextFeatureExtractor'
	lowerCAmelCase__         = 'Speech2TextTokenizer'



	def __init__(    self						,  lowercase						,  lowercase )				->       Optional[int]:
					super().__init__(lowercase						,  lowercase )
					lowerCamelCase_						=			self.feature_extractor
					lowerCamelCase_						=			False



	def __call__(    self						,  *lowercase						,  **lowercase )				->       Any:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*lowercase						,  **lowercase )

					if "raw_speech" in kwargs:
									warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
									lowerCamelCase_						=			kwargs.pop("raw_speech" )
					else:
									lowerCamelCase_						=			kwargs.pop("audio"						,  lowercase )
					lowerCamelCase_						=			kwargs.pop("sampling_rate"						,  lowercase )
					lowerCamelCase_						=			kwargs.pop("text"						,  lowercase )
					if len(lowercase ) > 0:
									lowerCamelCase_						=			args[0]
									lowerCamelCase_						=			args[1:]

					if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process." )

					if audio is not None:
									lowerCamelCase_						=			self.feature_extractor(lowercase						,  *lowercase						,  sampling_rate=lowercase						,  **lowercase )
					if text is not None:
									lowerCamelCase_						=			self.tokenizer(lowercase						,  **lowercase )

					if text is None:
									return inputs
					elif audio is None:
									return encodings
					else:
									lowerCamelCase_						=			encodings["input_ids"]
									return inputs



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       Any:
					return self.tokenizer.batch_decode(*lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       Dict:
					return self.tokenizer.decode(*lowercase						,  **lowercase )



	@contextmanager
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					warnings.warn(
					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
					    "your audio inputs, or in a separate call." )
					lowerCamelCase_						=			True
					lowerCamelCase_						=			self.tokenizer
					yield
					lowerCamelCase_						=			self.feature_extractor
					lowerCamelCase_						=			False







import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case						:Any      		=     logging.getLogger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						'''masked_bert'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     int=30_522     ,		__SCREAMING_SNAKE_CASE							:     str=768     ,		__SCREAMING_SNAKE_CASE							:     List[str]=12     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3_072     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0     ,		__SCREAMING_SNAKE_CASE							:     List[str]="topK"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="constant"     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

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





