


'''simple docstring'''






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


a_					:    Dict          =    logging.get_logger(__name__)  # pylint: disable=invalid-name




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__      ,				__magic_name__=7_68						)					->							str:
      super().__init__(UpperCamelCase_						)
      _a			      =							proj_size

      _a			      =							CLIPVisionModel(UpperCamelCase_						)
      _a			      =							PaintByExampleMapper(UpperCamelCase_						)
      _a			      =							nn.LayerNorm(config.hidden_size						)
      _a			      =							nn.Linear(config.hidden_size      ,				self.proj_size						)

      # uncondition for scaling
      _a			      =							nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__=False						)					->							Optional[int]:
      _a			      =							self.model(pixel_values=UpperCamelCase_						)
      _a			      =							clip_output.pooler_output
      _a			      =							self.mapper(latent_states[:, None]						)
      _a			      =							self.final_layer_norm(UpperCamelCase_						)
      _a			      =							self.proj_out(UpperCamelCase_						)
      if return_uncond_vector:
           return latent_states, self.uncond_vector

      return latent_states





class  a     (     nn.Module       ):
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      super().__init__()
      _a			      =							(config.num_hidden_layers + 1) // 5
      _a			      =							config.hidden_size
      _a			      =							1
      _a			      =							nn.ModuleList(
          [
              BasicTransformerBlock(UpperCamelCase_      ,				UpperCamelCase_      ,				UpperCamelCase_      ,				activation_fn='gelu'      ,				attention_bias=UpperCamelCase_						)
              for _ in range(UpperCamelCase_						)
          ]						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      for block in self.blocks:
           _a			      =							block(UpperCamelCase_						)

      return hidden_states





'''simple docstring'''






from typing import List, Optional

import numpy as np

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




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""EncodecFeatureExtractor"""
 _lowerCAmelCase 					=	("""T5Tokenizer""", """T5TokenizerFast""")
 def __init__( self      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      super().__init__(__magic_name__      ,				__magic_name__						)
      _a			      =							self.feature_extractor
      _a			      =							False
 def 							__UpperCAmelCase		( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=True						)					->							Tuple:
      return self.tokenizer.get_decoder_prompt_ids(task=__magic_name__      ,				language=__magic_name__      ,				no_timestamps=__magic_name__						)
 def __call__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[str]:
      # For backward compatibility
      if self._in_target_context_manager:
           return self.current_processor(*__magic_name__      ,				**__magic_name__						)

      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('sampling_rate'      ,				__magic_name__						)
      _a			      =							kwargs.pop('text'      ,				__magic_name__						)
      if len(__magic_name__						) > 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 text is not None:
           _a			      =							self.tokenizer(__magic_name__      ,				**__magic_name__						)

      if audio is not None:
           _a			      =							self.feature_extractor(__magic_name__      ,				*__magic_name__      ,				sampling_rate=__magic_name__      ,				**__magic_name__						)

      if audio is None:
           return inputs

      elif text is None:
           return audio_inputs

      else:
           _a			      =							audio_inputs['input_values']
           if "padding_mask" in audio_inputs:
                _a			      =							audio_inputs['padding_mask']
           return inputs
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('padding_mask'      ,				__magic_name__						)

      if len(__magic_name__						) > 0:
           _a			      =							args[0]
           _a			      =							args[1:]

      if audio_values is not None:
           return self._decode_audio(__magic_name__      ,				padding_mask=__magic_name__						)
      else:
           return self.tokenizer.batch_decode(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[Any]:
      return self.tokenizer.decode(*__magic_name__      ,				**__magic_name__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[np.ndarray]:
      _a			      =							to_numpy(__magic_name__						)
      _a      ,   _a      ,   _a			      =							audio_values.shape

      if padding_mask is None:
           return list(__magic_name__						)

      _a			      =							to_numpy(__magic_name__						)

      # 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)
      _a			      =							seq_len - padding_mask.shape[-1]
      _a			      =							1 - self.feature_extractor.padding_value
      _a			      =							np.pad(__magic_name__      ,				((0, 0), (0, difference))      ,				'constant'      ,				constant_values=__magic_name__						)

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

      return audio_values

