
'''simple docstring'''




import warnings

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





class  A				(							__snake_case						):
     __magic_name__						=		['''image_processor''', '''tokenizer''']
     __magic_name__						=		'''ViTImageProcessor'''
     __magic_name__						=		('''CLIPTokenizer''', '''CLIPTokenizerFast''')





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''						,			SCREAMING_SNAKE_CASE						,			)
             A       :   int					=						kwargs.pop('''feature_extractor'''	)

         A       :   Dict					=						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__(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def __call__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE	) ->				Any:






         """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:
             A       :   str					=						self.tokenizer(SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

         if visual_prompt is not None:
             A       :   str					=						self.image_processor(SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

         if images is not None:
             A       :   Any					=						self.image_processor(SCREAMING_SNAKE_CASE						,			return_tensors=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

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





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






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





     @property
     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






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





     @property
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






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






'''simple docstring'''




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)





@dataclass
class  A				(							__snake_case						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipeline_if import IFPipeline
      from .pipeline_if_imgaimg import IFImgaImgPipeline
      from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
      from .pipeline_if_inpainting import IFInpaintingPipeline
      from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
      from .pipeline_if_superresolution import IFSuperResolutionPipeline
      from .safety_checker import IFSafetyChecker
      from .watermark import IFWatermarker




