




"""simple docstring"""







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A   :  Dict					=					False




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				pass





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


				"""simple docstring"""





				def       snake_case    (     self					:	List[Any]						):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Union[str, Any]												=				VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(A__						)
						pipe.set_progress_bar_config(disable=A__						)

						__lowercase				:		str												=				"""A painting of a squirrel eating a burger """
						__lowercase				:		int												=				torch.manual_seed(0						)
						__lowercase				:		int												=				pipe(
						    prompt=A__    ,   generator=A__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"						).images

						with tempfile.TemporaryDirectory() as tmpdirname:
								pipe.save_pretrained(A__						)
								__lowercase				:		List[str]												=				VersatileDiffusionTextToImagePipeline.from_pretrained(A__						)
						pipe.to(A__						)
						pipe.set_progress_bar_config(disable=A__						)

						__lowercase				:		List[str]												=				generator.manual_seed(0						)
						__lowercase				:		List[Any]												=				pipe(
						    prompt=A__    ,   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       snake_case    (     self					:	Dict						):
						__lowercase				:		List[Any]												=				VersatileDiffusionTextToImagePipeline.from_pretrained(
						    "shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa						)
						pipe.to(A__						)
						pipe.set_progress_bar_config(disable=A__						)

						__lowercase				:		List[str]												=				"""A painting of a squirrel eating a burger """
						__lowercase				:		List[str]												=				torch.manual_seed(0						)
						__lowercase				:		Optional[int]												=				pipe(
						    prompt=A__    ,   generator=A__    ,   guidance_scale=7.5    ,   num_inference_steps=5_0    ,   output_type="numpy"						).images

						__lowercase				:		Union[str, Any]												=				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)
						__lowercase				:		Optional[Any]												=				np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]						)

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






"""simple docstring"""







import argparse
from collections import defaultdict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				F'{file}_{class_name}_{test_name}'
		done_test[_id] += 1

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		Union[str, Any]												=				f.readlines()

		__lowercase				:		str												=				F'class {class_name}('
		__lowercase				:		Union[str, Any]												=				F'{4 * " "}def {test_name}('
		__lowercase				:		List[Any]												=				F'{8 * " "}{correct_line.split()[0]}'
		__lowercase				:		Optional[int]												=				F'{16 * " "}{correct_line.split()[0]}'
		__lowercase				:		List[Any]												=				False
		__lowercase				:		int												=				False
		__lowercase				:		int												=				False
		__lowercase				:		List[Any]												=				False
		__lowercase				:		str												=				0
		__lowercase				:		Dict												=				0

		__lowercase				:		Optional[int]												=				[]
		for line in lines:
				if line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and in_func and (line.startswith(_lowerCamelCase				) or line.startswith(_lowerCamelCase				)):
						__lowercase				:		int												=				len(line.split(correct_line.split()[0]				)[0]				)
						count += 1

						if count == done_test[_id]:
								__lowercase				:		Any												=				True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								__lowercase				:		int												=				True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F'{spaces * " "}{correct_line}'				)
						__lowercase				:		List[Any]												=				False
				else:
						new_lines.append(_lowerCamelCase				)

		with open(_lowerCamelCase,  "w"				) as f:
				for line in new_lines:
						f.write(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->List[Any]:






		"""simple docstring"""


		if fail is not None:
				with open(_lowerCamelCase,  "r"				) as f:
						__lowercase				:		str												=				{l.strip() for l in f.readlines()}
		else:
				__lowercase				:		List[Any]												=				None

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		str												=				f.readlines()

		__lowercase				:		List[str]												=				defaultdict(_lowerCamelCase				)
		for line in correct_lines:
				__lowercase						,__lowercase						,__lowercase						,__lowercase				:		str												=				line.split(";"				)
				if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
						overwrite_file(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)


if __name__ == "__main__":
						__A   :  str					=      argparse.ArgumentParser()
						parser.add_argument('--correct_filename', help='filename of tests with expected result')
						parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
						__A   :  Union[str, Any]					=      parser.parse_args()

						main(args.correct_filename, args.fail_filename)
