


"""simple docstring"""






import warnings

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


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				_lowerCAmelCase   :   str   				=       ["image_processor", "tokenizer"]
				_lowerCAmelCase   :   Any   				=       "AutoImageProcessor"
				_lowerCAmelCase   :   int   				=       "AutoTokenizer"



				def __init__(    self							,		UpperCAmelCase__=None							,		UpperCAmelCase__=None							,		**UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."							,		UpperCAmelCase__							,		)
												SCREAMING_SNAKE_CASE__  				=					kwargs.pop("feature_extractor"  )

								SCREAMING_SNAKE_CASE__  				=					image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError("You have to specify an image_processor."  )
								if tokenizer is None:
												raise ValueError("You have to specify a tokenizer."  )

								super().__init__(UpperCAmelCase__							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					self.image_processor



				def __call__(    self							,		UpperCAmelCase__=None							,		UpperCAmelCase__=None							,		UpperCAmelCase__=None							,		**UpperCAmelCase__  ):

								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:
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(UpperCAmelCase__							,		return_tensors=UpperCAmelCase__							,		**UpperCAmelCase__  )

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

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



				def 	lowerCAmelCase__		(    self							,		*UpperCAmelCase__							,		**UpperCAmelCase__  ):
								return self.tokenizer.batch_decode(*UpperCAmelCase__							,		**UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		*UpperCAmelCase__							,		**UpperCAmelCase__  ):
								return self.tokenizer.decode(*UpperCAmelCase__							,		**UpperCAmelCase__  )



				@property
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.tokenizer.model_input_names
								SCREAMING_SNAKE_CASE__  				=					self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )



				@property
				def 	lowerCAmelCase__		(    self  ):
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,		UpperCAmelCase__							,		)
								return self.image_processor_class






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




"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase  =	False


@nightly
@require_torch_gpu
class       lowerCamelCase_		(      unittest.TestCase ):





				"""simple docstring"""







				def 	lowerCAmelCase__		(    self  ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )

								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type="numpy"							,		).images

								with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase__  )

								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					generator.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type="numpy"							,		).images

								assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"






				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					"cyberpunk 2077"
								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )
								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt=UpperCAmelCase__							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=50							,		output_type="numpy"							,		).images

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

								assert image.shape == (1, 512, 512, 3)
								SCREAMING_SNAKE_CASE__  				=					np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]  )

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