"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["image_processor", "tokenizer"]
					snake_case__       					=				"ChineseCLIPImageProcessor"
					snake_case__       					=				("BertTokenizer", "BertTokenizerFast")







					def __init__(       self     :			str					,lowerCamelCase__     :			Optional[Any]=None					,lowerCamelCase__     :			Optional[Any]=None					,**lowerCamelCase__     :			Tuple    ):
									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__    )
									UpperCAmelCase__    =							self.image_processor







					def __call__(       self     :			int					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			Any=None					,**lowerCamelCase__     :			str    ):

									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__    =							self.tokenizer(lowerCamelCase__					,return_tensors=lowerCamelCase__					,**lowerCamelCase__    )

									if images is not None:
													UpperCAmelCase__    =							self.image_processor(lowerCamelCase__					,return_tensors=lowerCamelCase__					,**lowerCamelCase__    )

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







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,*lowerCamelCase__     :			int					,**lowerCamelCase__     :			Any    ):
									return self.tokenizer.batch_decode(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]					,*lowerCamelCase__     :			Optional[int]					,**lowerCamelCase__     :			List[str]    ):
									return self.tokenizer.decode(*lowerCamelCase__					,**lowerCamelCase__    )







					@property
					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							self.tokenizer.model_input_names
									UpperCAmelCase__    =							self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







					@property
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,lowerCamelCase__					,)
									return self.image_processor_class




"""simple docstring"""






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


lowerCAmelCase__						:						Optional[int]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






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




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe(
									    image=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]    )

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



