


import argparse
from collections import defaultdict






def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						Dict    ,     __a				:						Union[str, Any]    ,     __a				:						Optional[int]    ,     __a				:						Optional[int]     ):
							a__      =f"""{file}_{class_name}_{test_name}"""
							done_test[_id] += 1

							with open(__a    ,     'r'     ) as f:
														a__      =f.readlines()

							a__      =f"""class {class_name}("""
							a__      =f"""{4 * ' '}def {test_name}("""
							a__      =f"""{8 * ' '}{correct_line.split()[0]}"""
							a__      =f"""{16 * ' '}{correct_line.split()[0]}"""
							a__      =False
							a__      =False
							a__      =False
							a__      =False
							a__      =0
							a__      =0

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

																					if count == done_test[_id]:
																												a__      =True

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

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(f"""{spaces * ' '}{correct_line}"""     )
																					a__      =a__      =a__      =a__      =False
														else:
																					new_lines.append(__a     )

							with open(__a    ,     'w'     ) as f:
														for line in new_lines:
																					f.write(__a     )






def   _lowercase(					__a				:						int    ,     __a				:						Union[str, Any]=None     ):
							if fail is not None:
														with open(__a    ,     'r'     ) as f:
																					a__      ={l.strip() for l in f.readlines()}
							else:
														a__      =None

							with open(__a    ,     'r'     ) as f:
														a__      =f.readlines()

							a__      =defaultdict(__a     )
							for line in correct_lines:
														a__    ,     a__    ,     a__    ,     a__      =line.split(';'     )
														if test_failures is None or "::".join([file, class_name, test_name]     ) in test_failures:
																					overwrite_file(__a    ,     __a    ,     __a    ,     __a    ,     __a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Tuple   				=       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)
	_lowerCAmelCase:						int   				=       parser.parse_args()

	main(args.correct_filename, args.fail_filename)









'''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      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





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





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

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

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






