


"""simple docstring"""




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class __snake_case		(					UpperCAmelCase__      ):







			"""simple docstring"""




			def __init__(		self							:str				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Optional[Any]       ):
										super().__init__()
										self.register_modules(unet=lowerCamelCase__				,  scheduler=lowerCamelCase__       )




			@torch.no_grad()
			def __call__(		self							:Optional[int]				,  UpperCamelCase__							:Union[str, Any] = 1				,  UpperCamelCase__							:Tuple = None				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:List[Any] = "pil"				,  UpperCamelCase__							:List[str] = True				,  **UpperCamelCase__							:Union[str, Any]				,  ):
										_a          =				torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,  generator=lowerCamelCase__				,  )
										_a          =				image.to(self.device       )

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

										for t in self.progress_bar(self.scheduler.timesteps       ):
																	# 1. predict noise model_output
																	_a          =				self.unet(lowerCamelCase__				,  lowerCamelCase__       ).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
																	_a          =				self.scheduler.step(lowerCamelCase__				,  lowerCamelCase__				,  lowerCamelCase__       ).prev_sample

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

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

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




"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   lowerCAmelCase_				:   str     	=			'AutoFeatureExtractor'
   lowerCAmelCase_				:   List[Any]     	=			'Speech2Text2Tokenizer'




   def __init__(		self							:int				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Union[str, Any]       ):
          super().__init__(UpperCamelCase__				,  UpperCamelCase__       )
          _a          =				self.feature_extractor
          _a          =				False




   def __call__(		self							:List[str]				,  *UpperCamelCase__							:List[str]				,  **UpperCamelCase__							:Dict       ):
          # 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."       )
                 _a          =				kwargs.pop("raw_speech"       )
          else:
                 _a          =				kwargs.pop("audio"				,  UpperCamelCase__       )
          _a          =				kwargs.pop("sampling_rate"				,  UpperCamelCase__       )
          _a          =				kwargs.pop("text"				,  UpperCamelCase__       )
          if len(UpperCamelCase__       ) > 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 audio is not None:
                 _a          =				self.feature_extractor(UpperCamelCase__				,  *UpperCamelCase__				,  sampling_rate=UpperCamelCase__				,  **UpperCamelCase__       )
          if text is not None:
                 _a          =				self.tokenizer(UpperCamelCase__				,  **UpperCamelCase__       )

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




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  *UpperCamelCase__							:int				,  **UpperCamelCase__							:Optional[Any]       ):
          return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  *UpperCamelCase__							:List[Any]				,  **UpperCamelCase__							:List[Any]       ):
          return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )




   @contextmanager
   def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
          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."       )
          _a          =				True
          _a          =				self.tokenizer
          yield
          _a          =				self.feature_extractor
          _a          =				False
