

"""simple docstring"""




import warnings

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







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							["""image_processor""", """tokenizer"""]
  snake_case_												=							"""ChineseCLIPImageProcessor"""
  snake_case_												=							("""BertTokenizer""", """BertTokenizerFast""")

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






   '''simple docstring'''
   UpperCAmelCase__       :	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."""       ,A       ,)
    UpperCAmelCase__       :	Optional[Any]        =       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__(A       ,A     )
   UpperCAmelCase__       :	List[str]        =       self.image_processor

  def __call__(	self		:      int       ,A		:      Optional[int]=None       ,A		:      Union[str, Any]=None       ,A		:      str=None       ,**A		:      Union[str, Any]     ):






   '''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:
    UpperCAmelCase__       :	str        =       self.tokenizer(A       ,return_tensors=A       ,**A     )

   if images is not None:
    UpperCAmelCase__       :	Optional[int]        =       self.image_processor(A       ,return_tensors=A       ,**A     )

   if text is not None and images is not None:
    UpperCAmelCase__       :	List[str]        =       image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**A     )       ,tensor_type=A     )

  def  __lowercase							(	self		:      Tuple       ,*A		:      int       ,**A		:      List[str]     ):






   '''simple docstring'''
   return self.tokenizer.batch_decode(*A       ,**A     )

  def  __lowercase							(	self		:      str       ,*A		:      Optional[int]       ,**A		:      Optional[Any]     ):






   '''simple docstring'''
   return self.tokenizer.decode(*A       ,**A     )

  @property
  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       self.tokenizer.model_input_names
   UpperCAmelCase__       :	List[Any]        =       self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




  @property
  def  __lowercase							(	self		:      int     ):






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












import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a__      :     Optional[int]         =		False





class UpperCAmelCase_				( unittest.TestCase ):
  pass





@slow
@require_torch_gpu
class UpperCAmelCase_				( unittest.TestCase ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

       A_          =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
       A_          =       torch.manual_seed(0 )
       A_          =       pipe(
           image=__snake_case ,generator=__snake_case ,guidance_scale=7.5 ,num_inference_steps=5_0 ,output_type='''numpy''' ,).images

       A_          =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       A_          =       np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



