






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """EncodecFeatureExtractor"""
  A_						       = ("""T5Tokenizer""", """T5TokenizerFast""")




  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			int:
     super().__init__(_UpperCAmelCase		,		_UpperCAmelCase			)
     UpperCamelCase_						=  self.feature_extractor
     UpperCamelCase_						=  False




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=True			)      ->			List[str]:
     return self.tokenizer.get_decoder_prompt_ids(task=_UpperCAmelCase		,		language=_UpperCAmelCase		,		no_timestamps=_UpperCAmelCase			)




  def __call__(  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			List[str]:
     # For backward compatibility
     if self._in_target_context_manager:
        return self.current_processor(*_UpperCAmelCase		,		**_UpperCAmelCase			)

     UpperCamelCase_						=  kwargs.pop('audio'		,		_UpperCAmelCase			)
     UpperCamelCase_						=  kwargs.pop('sampling_rate'		,		_UpperCAmelCase			)
     UpperCamelCase_						=  kwargs.pop('text'		,		_UpperCAmelCase			)
     if len(_UpperCAmelCase			) > 0:
        UpperCamelCase_						=  args[0]
        UpperCamelCase_						=  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 text is not None:
        UpperCamelCase_						=  self.tokenizer(_UpperCAmelCase		,		**_UpperCAmelCase			)

     if audio is not None:
        UpperCamelCase_						=  self.feature_extractor(_UpperCAmelCase		,		*_UpperCAmelCase		,		sampling_rate=_UpperCAmelCase		,		**_UpperCAmelCase			)

     if audio is None:
        return inputs

     elif text is None:
        return audio_inputs

     else:
        UpperCamelCase_						=  audio_inputs['input_values']
        if "padding_mask" in audio_inputs:
           UpperCamelCase_						=  audio_inputs['padding_mask']
        return inputs




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			int:
     UpperCamelCase_						=  kwargs.pop('audio'		,		_UpperCAmelCase			)
     UpperCamelCase_						=  kwargs.pop('padding_mask'		,		_UpperCAmelCase			)

     if len(_UpperCAmelCase			) > 0:
        UpperCamelCase_						=  args[0]
        UpperCamelCase_						=  args[1:]

     if audio_values is not None:
        return self._decode_audio(_UpperCAmelCase		,		padding_mask=_UpperCAmelCase			)
     else:
        return self.tokenizer.batch_decode(*_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Optional[Any]:
     return self.tokenizer.decode(*_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			List[np.ndarray]:
     UpperCamelCase_						=  to_numpy(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  audio_values.shape

     if padding_mask is None:
        return list(_UpperCAmelCase			)

     UpperCamelCase_						=  to_numpy(_UpperCAmelCase			)

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     UpperCamelCase_						=  seq_len - padding_mask.shape[-1]
     UpperCamelCase_						=  1 - self.feature_extractor.padding_value
     UpperCamelCase_						=  np.pad(_UpperCAmelCase		,		((0, 0), (0, difference))		,		'constant'		,		constant_values=_UpperCAmelCase			)

     UpperCamelCase_						=  audio_values.tolist()
     for i in range(_UpperCAmelCase			):
        UpperCamelCase_						=  np.asarray(audio_values[i]			)[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        UpperCamelCase_						=  sliced_audio.reshape(_UpperCAmelCase		,		-1			)

     return audio_values









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    _lowerCamelCase=512   ,    _lowerCamelCase="cls"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Tuple									=			project_dim
       a		:Optional[int]									=			pooler_fn
       a		:int									=			learn_encoder
       a		:int									=			use_attention_mask







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			[r'pooler', r'logit_scale']
 SCREAMING_SNAKE_CASE__ =			[r'position_ids', r'predictions.decoder.bias']
 SCREAMING_SNAKE_CASE__ =			'roberta'
 SCREAMING_SNAKE_CASE__ =			RobertaSeriesConfig




 def __init__(						self   ,    _lowerCamelCase       ):
       super().__init__(_lowerCamelCase       )
       a		:Tuple									=			XLMRobertaModel(_lowerCamelCase       )
       a		:Optional[Any]									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
       a		:Optional[int]									=			getattr(_lowerCamelCase   ,    '''has_pre_transformation'''   ,    _lowerCamelCase       )
       if self.has_pre_transformation:
             a		:Tuple									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
             a		:Union[str, Any]									=			nn.LayerNorm(config.hidden_size   ,    eps=config.layer_norm_eps       )
       self.post_init()




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    ):
       a		:Optional[int]									=			return_dict if return_dict is not None else self.config.use_return_dict

       a		:int									=			self.base_model(
           input_ids=_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    token_type_ids=_lowerCamelCase   ,    position_ids=_lowerCamelCase   ,    head_mask=_lowerCamelCase   ,    inputs_embeds=_lowerCamelCase   ,    encoder_hidden_states=_lowerCamelCase   ,    encoder_attention_mask=_lowerCamelCase   ,    output_attentions=_lowerCamelCase   ,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states   ,    return_dict=_lowerCamelCase   ,    )

       if self.has_pre_transformation:
             a		:Optional[int]									=			outputs['''hidden_states'''][-2]
             a		:List[Any]									=			self.pre_LN(_lowerCamelCase       )
             a		:Optional[Any]									=			self.transformation_pre(_lowerCamelCase       )

             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )
       else:
             a		:List[str]									=			self.transformation(outputs.last_hidden_state       )
             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )





