






import gc
import tempfile
import unittest

import numpy as np
import torch

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


lowerCamelCase : List[str]						   =       False

class 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					pass







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

					'''simple docstring'''

					def       lowerCAmelCase				(  self				:	int							)			->  str:


												"""simple docstring"""

												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""		,     torch_dtype=torch.floataa							)
												pipe.to(__a							)
												pipe.set_progress_bar_config(disable=__a							)

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

												__lowercase      :							List[Any]				=     torch.manual_seed(0							)
												__lowercase      :							List[str]				=     pipe.dual_guided(
												    prompt="""first prompt"""		,     image=__a		,     text_to_image_strength=0.75		,     generator=__a		,     guidance_scale=7.5		,     num_inference_steps=2		,     output_type="""numpy"""		,     ).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(__a							)
																			__lowercase      :							Any				=     VersatileDiffusionPipeline.from_pretrained(__a		,     torch_dtype=torch.floataa							)
												pipe.to(__a							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase      :							Tuple				=     generator.manual_seed(0							)
												__lowercase      :							Optional[int]				=     pipe.dual_guided(
												    prompt="""first prompt"""		,     image=__a		,     text_to_image_strength=0.75		,     generator=__a		,     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				:	Dict							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Tuple				=     VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""		,     torch_dtype=torch.floataa							)
												pipe.to(__a							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase      :							int				=     """cyberpunk 2077"""
												__lowercase      :							Union[str, Any]				=     load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
												__lowercase      :							Tuple				=     torch.manual_seed(0							)
												__lowercase      :							Dict				=     pipe.dual_guided(
												    prompt=__a		,     image=__a		,     text_to_image_strength=0.75		,     generator=__a		,     guidance_scale=7.5		,     num_inference_steps=50		,     output_type="""numpy"""		,     ).images

												__lowercase      :							Optional[Any]				=     image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												__lowercase      :							str				=     np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]							)

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

												__lowercase      :							Tuple				=     """A painting of a squirrel eating a burger """
												__lowercase      :							Optional[int]				=     torch.manual_seed(0							)
												__lowercase      :							str				=     pipe.text_to_image(
												    prompt=__a		,     generator=__a		,     guidance_scale=7.5		,     num_inference_steps=50		,     output_type="""numpy"""							).images

												__lowercase      :							Optional[int]				=     image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												__lowercase      :							List[str]				=     np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]							)

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

												__lowercase      :							Tuple				=     pipe.image_variation(__a		,     generator=__a		,     output_type="""numpy"""							).images

												__lowercase      :							List[str]				=     image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												__lowercase      :							Optional[int]				=     np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]							)

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







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase : Union[str, Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	int		,     *__a				:	Dict		,     **__a				:	Optional[Any]							)			->  None:


												"""simple docstring"""

												warnings.warn(
												    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use BeitImageProcessor instead."""		,     __a		,     )
												super().__init__(*__a		,     **__a							)