'''simple docstring'''


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

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






  @torch.no_grad()
  def __call__(			self				,		snake_case_ = 1				,		snake_case_ = None				,		snake_case_ = 5_0				,		snake_case_ = "pil"				,		snake_case_ = True				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				torch.randn(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,		generator=snake_case_				,		)
       UpperCAmelCase_							:       List[str]       =				image.to(self.device						)

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

       for t in self.progress_bar(self.scheduler.timesteps						):
            # 1. predict noise model_output
            UpperCAmelCase_							:       List[Any]       =				self.unet(snake_case_				,		snake_case_						).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
            UpperCAmelCase_							:       Optional[int]       =				self.scheduler.step(snake_case_				,		snake_case_				,		snake_case_						).prev_sample

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

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

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







'''simple docstring'''


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		['''image_processor''', '''tokenizer''']
  lowerCamelCase_		:Optional[Any]						   =		'''ViTImageProcessor'''
  lowerCamelCase_		:int						   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'				,		snake_case_				,		)
            UpperCAmelCase_							:       int       =				kwargs.pop('feature_extractor'						)

       UpperCAmelCase_							:       str       =				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(snake_case_				,		snake_case_						)


  def __call__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       if text is None and visual_prompt is None and images is None:
            raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
            raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
            UpperCAmelCase_							:       Optional[int]       =				self.tokenizer(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None:
            UpperCAmelCase_							:       Optional[Any]       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if images is not None:
            UpperCAmelCase_							:       int       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None and images is not None:
            UpperCAmelCase_							:       Tuple       =				{
                'pixel_values': image_features.pixel_values,
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       elif text is not None and images is not None:
            UpperCAmelCase_							:       Optional[int]       =				image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       elif visual_prompt is not None:
            UpperCAmelCase_							:       Dict       =				{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       else:
            return BatchEncoding(data=dict(**snake_case_						)				,		tensor_type=snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.batch_decode(*snake_case_				,		**snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.decode(*snake_case_				,		**snake_case_						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,		snake_case_				,		)
       return self.image_processor_class






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,		snake_case_				,		)
       return self.image_processor






