'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class       lowerCamelCase_							(	__UpperCAmelCase  ):






   '''simple docstring'''

   A :     Optional[int]				=	"AutoFeatureExtractor"
   A :     Dict				=	"Speech2Text2Tokenizer"


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__(lowercase_     ,						lowercase_ )
       UpperCamelCase__      =			self.feature_extractor
       UpperCamelCase__      =			False


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

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

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

       if text is None:
           return inputs
       elif audio is None:
           return encodings
       else:
           UpperCamelCase__      =			encodings['''input_ids''']
           return inputs


   def 	_UpperCamelCase       (  self     ,						*__UpperCamelCase     ,						**__UpperCamelCase ):
       return self.tokenizer.batch_decode(*lowercase_     ,						**lowercase_ )


   def 	_UpperCamelCase       (  self     ,						*__UpperCamelCase     ,						**__UpperCamelCase ):
       return self.tokenizer.decode(*lowercase_     ,						**lowercase_ )







   @contextmanager
   def 	_UpperCamelCase       (  self ):
       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." )
       UpperCamelCase__      =			True
       UpperCamelCase__      =			self.tokenizer
       yield
       UpperCamelCase__      =			self.feature_extractor
       UpperCamelCase__      =			False









'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

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

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

       self.register_modules(unet=__UpperCamelCase     ,						scheduler=__UpperCamelCase )







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,						generator=__UpperCamelCase     ,						device=self.device     ,						)

       self.scheduler.set_timesteps(__UpperCamelCase )
       for t in self.progress_bar(self.scheduler.timesteps ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

       UpperCamelCase__      =			(image / 2 + 0.5).clamp(0     ,						1 )
       UpperCamelCase__      =			image.cpu().permute(0     ,						2     ,						3     ,						1 ).numpy()
       if output_type == "pil":
           UpperCamelCase__      =			self.numpy_to_pil(__UpperCamelCase )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




