import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE_		(      __SCREAMING_SNAKE_CASE		):
   __lowerCAmelCase           =				["""image_processor""", """tokenizer"""]
   __lowerCAmelCase           =				"""ViTImageProcessor"""
   __lowerCAmelCase           =				("""CLIPTokenizer""", """CLIPTokenizerFast""")







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						List[Any]=None   ,   **lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      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__   ,   )
     UpperCamelCase			  =      kwargs.pop("""feature_extractor"""				)

    UpperCamelCase			  =      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__				)







   def __call__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						List[str]=None   ,   lowerCamelCase_						:						List[Any]=None   ,   lowerCamelCase_						:						List[Any]=None   ,   **lowerCamelCase_						:						str				):





    """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			  =      self.tokenizer(lowerCAmelCase__   ,   return_tensors=lowerCAmelCase__   ,   **lowerCAmelCase__				)

    if visual_prompt is not None:
     UpperCamelCase			  =      self.image_processor(lowerCAmelCase__   ,   return_tensors=lowerCAmelCase__   ,   **lowerCAmelCase__				)

    if images is not None:
     UpperCamelCase			  =      self.image_processor(lowerCAmelCase__   ,   return_tensors=lowerCAmelCase__   ,   **lowerCAmelCase__				)

    if visual_prompt is not None and images is not None:
     UpperCamelCase			  =      {
         '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			  =      image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    elif visual_prompt is not None:
     UpperCamelCase			  =      {
         'conditional_pixel_values': prompt_features.pixel_values,
     }
     return encoding
    else:
     return BatchEncoding(data=dict(**lowerCAmelCase__				)   ,   tensor_type=lowerCAmelCase__				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   *lowerCamelCase_						:						List[str]   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


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







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   *lowerCamelCase_						:						Dict   ,   **lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


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







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """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







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


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




import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowerCAmelCase__			:      Optional[int]								=0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowerCAmelCase__			:      str								=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					WATERMARK_BITS
										SCREAMING_SNAKE_CASE_		:     int					=					WatermarkEncoder()

										self.encoder.set_watermark('bits'			,						self.watermark       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if images.shape[-1] < 2_5_6:
													return images

										SCREAMING_SNAKE_CASE_		:     Tuple					=					(2_5_5 * (images / 2 + 0.5)).cpu().permute(0			,						2			,						3			,						1       ).float().numpy()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.encoder.encode(lowerCAmelCase__			,						'dwtDct'       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.from_numpy(np.array(lowerCAmelCase__       )       ).permute(0			,						3			,						1			,						2       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.clamp(2 * (images / 2_5_5 - 0.5)			,						min=-1.0			,						max=1.0       )
										return images





