




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"AutoFeatureExtractor"
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"Speech2Text2Tokenizer"






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[Any]     )->			Dict:
     super().__init__(UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: Optional[int]						     =    self.feature_extractor
     _lowercase: int						     =    False






   def __call__(	self   :       List[Any]						,					*UpperCamelCase_   :       Dict						,					**UpperCamelCase_   :       Optional[int]     )->			str:
     # 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.'     )
       _lowercase: Tuple						     =    kwargs.pop('raw_speech'     )
     else:
       _lowercase: Optional[Any]						     =    kwargs.pop('audio'						,					UpperCamelCase_     )
     _lowercase: int						     =    kwargs.pop('sampling_rate'						,					UpperCamelCase_     )
     _lowercase: Dict						     =    kwargs.pop('text'						,					UpperCamelCase_     )
     if len(UpperCamelCase_     ) > 0:
       _lowercase: List[Any]						     =    args[0]
       _lowercase: List[Any]						     =    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:
       _lowercase: Optional[int]						     =    self.feature_extractor(UpperCamelCase_						,					*UpperCamelCase_						,					sampling_rate=UpperCamelCase_						,					**UpperCamelCase_     )
     if text is not None:
       _lowercase: str						     =    self.tokenizer(UpperCamelCase_						,					**UpperCamelCase_     )

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






   def  _a						(	self   :       Optional[int]						,					*UpperCamelCase_   :       Tuple						,					**UpperCamelCase_   :       Dict     )->			List[str]:
     return self.tokenizer.batch_decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   def  _a						(	self   :       Tuple						,					*UpperCamelCase_   :       List[Any]						,					**UpperCamelCase_   :       Union[str, Any]     )->			Optional[int]:
     return self.tokenizer.decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   @contextmanager
   def  _a						(	self   :       Tuple     )->			Union[str, Any]:
     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.'     )
     _lowercase: Any						     =    True
     _lowercase: Optional[Any]						     =    self.tokenizer
     yield
     _lowercase: Union[str, Any]						     =    self.feature_extractor
     _lowercase: str						     =    False
