


"""simple docstring"""







import re
from pathlib import Path
from unittest import TestCase

import pytest




@pytest.mark.integration
class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				str   )       ->	Optional[int]:

						with open(UpperCAmelCase__			,			encoding="utf-8"   ) as input_file:
											__SCREAMING_SNAKE_CASE						   =				re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"   )
											__SCREAMING_SNAKE_CASE						   =				input_file.read()
											__SCREAMING_SNAKE_CASE						   =				regexp.search(UpperCAmelCase__   )

						return match




	def   UpperCAmelCase_      ( self			:				int			,			UpperCAmelCase__			:				str   )       ->	Union[str, Any]:

						with open(UpperCAmelCase__			,			encoding="utf-8"   ) as input_file:
											__SCREAMING_SNAKE_CASE						   =				re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"			,			re.DOTALL   )
											__SCREAMING_SNAKE_CASE						   =				input_file.read()
											# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
											__SCREAMING_SNAKE_CASE						   =				regexp.finditer(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				[match for match in matches if match is not None and match.group(1   ) is not None]
						return matches[0] if matches else None




	def   UpperCAmelCase_      ( self			:				Dict   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				Path("./datasets"   )
						__SCREAMING_SNAKE_CASE						   =				list(dataset_paths.absolute().glob("**/*.py"   )   )

						for dataset in dataset_files:
											if self._no_encoding_on_file_open(str(UpperCAmelCase__   )   ):
																raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}"""   )




	def   UpperCAmelCase_      ( self			:				str   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				Path("./datasets"   )
						__SCREAMING_SNAKE_CASE						   =				list(dataset_paths.absolute().glob("**/*.py"   )   )

						for dataset in dataset_files:
											if self._no_print_statements(str(UpperCAmelCase__   )   ):
																raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead."""   )




"""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


a__				:							Tuple						=     False




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	pass




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
						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(
						    image=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type="numpy"			,			).images

						__SCREAMING_SNAKE_CASE						   =				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)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]   )

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