





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class   __SCREAMING_SNAKE_CASE     (					lowerCAmelCase_   ):







       def __init__( self	:      Optional[int]						,   A	:      List[Any]						,   A	:      Tuple     )  ->List[str]:
          super().__init__()
          self.register_modules(unet=__lowerCAmelCase						,   scheduler=__lowerCAmelCase     )







       @torch.no_grad()
       def __call__( self	:      Dict						,   A	:      Tuple = 1						,   A	:      Optional[int] = None						,   A	:      List[str] = 5_0						,   A	:      Any = "pil"						,   A	:      Union[str, Any] = True						,   **A	:      Union[str, Any]						,   )  ->List[Any]:
          lowerCamelCase__  :		List[str]     =				torch.randn(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,   generator=__lowerCAmelCase						,   )
          lowerCamelCase__  :		Union[str, Any]     =				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
             lowerCamelCase__  :		Union[str, Any]     =				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
             lowerCamelCase__  :		List[str]     =				self.scheduler.step(__lowerCAmelCase						,   __lowerCAmelCase						,   __lowerCAmelCase     ).prev_sample

          lowerCamelCase__  :		Optional[Any]     =				(image / 2 + 0.5).clamp(0						,   1     )
          lowerCamelCase__  :		int     =				image.cpu().permute(0						,   2						,   3						,   1     ).numpy()
          if output_type == "pil":
             lowerCamelCase__  :		Dict     =				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"











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,       __lowerCAmelCase							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   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__(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""

								if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''  )

								if text is not None:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, Any]   =   image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__lowerCAmelCase  )							,       tensor_type=__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor_class






