import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	"AutoFeatureExtractor"
       A							:	Tuple          =	"Speech2Text2Tokenizer"

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Union[str, Any]			):
           super().__init__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Union[str, Any] 			=    self.feature_extractor
           __snake_case       :				Tuple 			=    False

       def __call__(							self					:     Any			,			*_lowerCAmelCase					:     Dict			,			**_lowerCAmelCase					:     Optional[Any]			):
           # For backward compatibility
           if self._in_target_context_manager:
               return self.current_processor(*_lowerCAmelCase			,			**_lowerCAmelCase			)

           if "raw_speech" in kwargs:
               warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""raw_speech"""			)
           else:
               __snake_case       :				Union[str, Any] 			=    kwargs.pop("""audio"""			,			_lowerCAmelCase			)
           __snake_case       :				List[str] 			=    kwargs.pop("""sampling_rate"""			,			_lowerCAmelCase			)
           __snake_case       :				Any 			=    kwargs.pop("""text"""			,			_lowerCAmelCase			)
           if len(_lowerCAmelCase			) > 0:
               __snake_case       :				Dict 			=    args[0]
               __snake_case       :				Any 			=    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:
               __snake_case       :				Dict 			=    self.feature_extractor(_lowerCAmelCase			,			*_lowerCAmelCase			,			sampling_rate=_lowerCAmelCase			,			**_lowerCAmelCase			)
           if text is not None:
               __snake_case       :				int 			=    self.tokenizer(_lowerCAmelCase			,			**_lowerCAmelCase			)

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

       def 			snake_case__    (							self					:     str			,			*_lowerCAmelCase					:     Dict			,			**_lowerCAmelCase					:     str			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Dict			,			*_lowerCAmelCase					:     Any			,			**_lowerCAmelCase					:     int			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)





       @contextmanager
       def 			snake_case__    (							self					:     str			):
           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."""			)
           __snake_case       :				Dict 			=    True
           __snake_case       :				Tuple 			=    self.tokenizer
           yield
           __snake_case       :				str 			=    self.feature_extractor
           __snake_case       :				List[str] 			=    False




from collections import OrderedDict
from typing import Mapping

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


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



