




"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class       lowerCAmelCase__       (			__lowercase    ):


				"""simple docstring"""





				__UpperCAmelCase :     UNetaDModel
				__UpperCAmelCase :     ScoreSdeVeScheduler



				def __init__(     self					:	Any    ,   lowercase__					:	UNetaDModel    ,   lowercase__					:	ScoreSdeVeScheduler						):
						super().__init__()
						self.register_modules(unet=__a    ,   scheduler=__a						)



				@torch.no_grad()
				def __call__(     self					:	List[str]    ,   lowercase__					:	int = 1    ,   lowercase__					:	int = 2_0_0_0    ,   lowercase__					:	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   lowercase__					:	Optional[str] = "pil"    ,   lowercase__					:	bool = True    ,   **lowercase__					:	Any    ,   ):
						__lowercase				:		str												=				self.unet.config.sample_size
						__lowercase				:		List[Any]												=				(batch_size, 3, img_size, img_size)

						__lowercase				:		str												=				self.unet

						__lowercase				:		Any												=				randn_tensor(__a    ,   generator=__a						) * self.scheduler.init_noise_sigma
						__lowercase				:		Any												=				sample.to(self.device						)

						self.scheduler.set_timesteps(__a						)
						self.scheduler.set_sigmas(__a						)

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
								__lowercase				:		int												=				self.scheduler.sigmas[i] * torch.ones(shape[0]    ,   device=self.device						)

								# correction step
								for _ in range(self.scheduler.config.correct_steps						):
										__lowercase				:		Optional[Any]												=				self.unet(__a    ,   __a						).sample
										__lowercase				:		Any												=				self.scheduler.step_correct(__a    ,   __a    ,   generator=__a						).prev_sample

								# prediction step
								__lowercase				:		Any												=				model(__a    ,   __a						).sample
								__lowercase				:		int												=				self.scheduler.step_pred(__a    ,   __a    ,   __a    ,   generator=__a						)

								__lowercase				:		List[str]												=				output.prev_sample, output.prev_sample_mean

						__lowercase				:		Tuple												=				sample_mean.clamp(0    ,   1						)
						__lowercase				:		Dict												=				sample.cpu().permute(0    ,   2    ,   3    ,   1						).numpy()
						if output_type == "pil":
								__lowercase				:		List[Any]												=				self.numpy_to_pil(__a						)

						if not return_dict:
								return (sample,)

						return ImagePipelineOutput(images=__a						)






"""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)
