


'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  "Speech2TextFeatureExtractor"
  _lowerCamelCase		    =  "Speech2TextTokenizer"






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  self.feature_extractor
   lowerCamelCase_      =  False






  def __call__(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   # For backward compatibility
   if self._in_target_context_manager:
    return self.current_processor(*UpperCamelCase			,  **UpperCamelCase     )

   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"			,  UpperCamelCase     )
   lowerCamelCase_      =  kwargs.pop("sampling_rate"			,  UpperCamelCase     )
   lowerCamelCase_      =  kwargs.pop("text"			,  UpperCamelCase     )
   if len(UpperCamelCase     ) > 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(UpperCamelCase			,  *UpperCamelCase			,  sampling_rate=UpperCamelCase			,  **UpperCamelCase     )
   if text is not None:
    lowerCamelCase_      =  self.tokenizer(UpperCamelCase			,  **UpperCamelCase     )

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






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.tokenizer.batch_decode(*UpperCamelCase			,  **UpperCamelCase     )






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.tokenizer.decode(*UpperCamelCase			,  **UpperCamelCase     )







  @contextmanager
  def   snake_case		(    self     ):







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







'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  42






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   self.register_modules(unet=UpperCamelCase			,  scheduler=UpperCamelCase     )







  @torch.no_grad()
  def __call__(    self			,  UpperCamelCase = 1			,  UpperCamelCase = 2000			,  UpperCamelCase = None			,  UpperCamelCase = "pil"			,  UpperCamelCase = True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.unet.config.sample_size
   lowerCamelCase_      =  (batch_size, 3, img_size, img_size)

   lowerCamelCase_      =  self.unet

   lowerCamelCase_      =  randn_tensor(UpperCamelCase			,  generator=UpperCamelCase     ) * self.scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(self.device     )

   self.scheduler.set_timesteps(UpperCamelCase     )
   self.scheduler.set_sigmas(UpperCamelCase     )

   for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
    lowerCamelCase_      =  self.scheduler.sigmas[i] * torch.ones(shape[0]			,  device=self.device     )

    # correction step
    for _ in range(self.scheduler.config.correct_steps     ):
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase     ).sample
     lowerCamelCase_      =  self.scheduler.step_correct(UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    # prediction step
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     ).sample
    lowerCamelCase_      =  self.scheduler.step_pred(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     )

    lowerCamelCase_ ,lowerCamelCase_      =  output.prev_sample, output.prev_sample_mean

   lowerCamelCase_      =  sample_mean.clamp(0			,  1     )
   lowerCamelCase_      =  sample.cpu().permute(0			,  2			,  3			,  1     ).numpy()
   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (sample,)

   return ImagePipelineOutput(images=UpperCamelCase     )



