




'''simple docstring'''





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class    _a				(						_UpperCamelCase					,					_UpperCamelCase					,					_UpperCamelCase							):






     @register_to_config
     def __init__( self					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE = False					,)							->			Optional[int]:
          super().__init__()

          _snake_case							=      nn.Linear(a__					,a__					,bias=a__    )

          _snake_case							=      nn.Embedding(a__					,a__    )
          _snake_case							=      False

          _snake_case							=      nn.Dropout(p=a__    )

          _snake_case							=      TaConfig(
              d_model=a__					,num_heads=a__					,d_kv=a__					,d_ff=a__					,feed_forward_proj=a__					,dropout_rate=a__					,is_decoder=a__					,is_encoder_decoder=a__					,)
          _snake_case							=      nn.ModuleList()
          for lyr_num in range(a__    ):
               _snake_case							=      TaBlock(a__    )
               self.encoders.append(a__    )

          _snake_case							=      TaLayerNorm(a__    )
          _snake_case							=      nn.Dropout(p=a__    )






     def 				_lowercase	( self					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE    )							->			int:
          _snake_case							=      self.input_proj(a__    )

          # terminal relative positional encodings
          _snake_case							=      encoder_inputs.shape[1]
          _snake_case							=      torch.arange(a__					,device=encoder_inputs.device    )

          _snake_case							=      encoder_inputs_mask.sum(-1    )
          _snake_case							=      torch.roll(input_positions.unsqueeze(0    )					,tuple(seq_lens.tolist()    )					,dims=0    )
          x += self.position_encoding(a__    )

          _snake_case							=      self.dropout_pre(a__    )

          # inverted the attention mask
          _snake_case							=      encoder_inputs.size()
          _snake_case							=      self.get_extended_attention_mask(a__					,a__    )

          for lyr in self.encoders:
               _snake_case							=      lyr(a__					,a__    )[0]
          _snake_case							=      self.layer_norm(a__    )

          return self.dropout_post(a__    ), encoder_inputs_mask






"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	'AutoFeatureExtractor'
		__UpperCamelCase							=	'Speech2Text2Tokenizer'







		def __init__(    self					:				int			,      a__					:				Tuple			,      a__					:				Optional[Any]						)				->  str:





						'''simple docstring'''







						super().__init__(a__			,      a__						)
						_A         =	self.feature_extractor
						_A         =	False







		def __call__(    self					:				Optional[int]			,      *a__					:				List[str]			,      **a__					:				Any						)				->  List[Any]:





						'''simple docstring'''







						if self._in_target_context_manager:
										return self.current_processor(*a__			,      **a__						)

						if "raw_speech" in kwargs:
										warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."						)
										_A         =	kwargs.pop("raw_speech"						)
						else:
										_A         =	kwargs.pop("audio"			,      a__						)
						_A         =	kwargs.pop("sampling_rate"			,      a__						)
						_A         =	kwargs.pop("text"			,      a__						)
						if len(a__						) > 0:
										_A         =	args[0]
										_A         =	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:
										_A         =	self.feature_extractor(a__			,      *a__			,      sampling_rate=a__			,      **a__						)
						if text is not None:
										_A         =	self.tokenizer(a__			,      **a__						)

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







		def        a_       (    self					:				List[Any]			,      *a__					:				Tuple			,      **a__					:				List[Any]						)				->  List[Any]:





						'''simple docstring'''







						return self.tokenizer.batch_decode(*a__			,      **a__						)







		def        a_       (    self					:				Any			,      *a__					:				Dict			,      **a__					:				Tuple						)				->  Union[str, Any]:





						'''simple docstring'''







						return self.tokenizer.decode(*a__			,      **a__						)







		@contextmanager
		def        a_       (    self					:				Optional[int]						)				->  Optional[int]:





						'''simple docstring'''







						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."						)
						_A         =	True
						_A         =	self.tokenizer
						yield
						_A         =	self.feature_extractor
						_A         =	False