'''simple docstring'''






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "MCTCTFeatureExtractor"
   __lowerCAmelCase         =   "AutoTokenizer"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Union[str, Any]     )  ->			Dict:
        super().__init__(UpperCamelCase__, UpperCamelCase__     )
        _A    		=  self.feature_extractor
        _A    		=  False
   def __call__(      self      :			Any, *UpperCamelCase__      :			Any, **UpperCamelCase__      :			Optional[int]     )  ->			List[Any]:
        # 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 	__UpperCAmelCase   (      self      :			str, *UpperCamelCase__      :			Any, **UpperCamelCase__      :			Optional[int]     )  ->			List[str]:
        return self.tokenizer.batch_decode(*UpperCamelCase__, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any, *UpperCamelCase__      :			Union[str, Any], **UpperCamelCase__      :			Tuple     )  ->			Union[str, Any]:
        # For backward compatibility
        if self._in_target_context_manager:
             return self.current_processor.pad(*UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  kwargs.pop('input_features', UpperCamelCase__     )
        _A    		=  kwargs.pop('labels', UpperCamelCase__     )
        if len(UpperCamelCase__     ) > 0:
             _A    		=  args[0]
             _A    		=  args[1:]

        if input_features is not None:
             _A    		=  self.feature_extractor.pad(UpperCamelCase__, *UpperCamelCase__, **UpperCamelCase__     )
        if labels is not None:
             _A    		=  self.tokenizer.pad(UpperCamelCase__, **UpperCamelCase__     )

        if labels is None:
             return input_features
        elif input_features is None:
             return labels
        else:
             _A    		=  labels['input_ids']
             return input_features
   def 	__UpperCAmelCase   (      self      :			List[Any], *UpperCamelCase__      :			int, **UpperCamelCase__      :			Tuple     )  ->			List[Any]:
        return self.tokenizer.decode(*UpperCamelCase__, **UpperCamelCase__     )

   @contextmanager
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Optional[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.'     )
        _A    		=  True
        _A    		=  self.tokenizer
        yield
        _A    		=  self.feature_extractor
        _A    		=  False







# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

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

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

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

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

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

