





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class        _lowerCamelCase      (       UpperCAmelCase__ ):
						UpperCAmelCase_ =			42
						UpperCAmelCase_ =			42
						UpperCAmelCase_ =			None





class        _lowerCamelCase      (       UpperCAmelCase__  ,			UpperCAmelCase__ ):
						UpperCAmelCase_ =			2


						@register_to_config
						def __init__(self   ,							__a = 0.02   ,							__a = 1_00   ,							__a = 1.007   ,							__a = 80   ,							__a = 0.05   ,							__a = 50   ,							) ->   List[Any]:
								UpperCamelCase   =	sigma_max

								# setable values
								UpperCamelCase   =	None
								UpperCamelCase   =	None
								UpperCamelCase   =	None  # sigma(t_i)


						def     snake_case_   (self   ,							__a   ,							__a = None		) ->   torch.FloatTensor:
								return sample


						def     snake_case_   (self   ,							__a   ,							__a = None		) ->   str:
								UpperCamelCase   =	num_inference_steps
								UpperCamelCase   =	np.arange(0   ,							self.num_inference_steps		)[::-1].copy()
								UpperCamelCase   =	torch.from_numpy(__a		).to(__a		)
								UpperCamelCase   =	[
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								UpperCamelCase   =	torch.tensor(__a   ,							dtype=torch.floataa   ,							device=__a		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a = None		) ->   Tuple[torch.FloatTensor, float]:




								if self.config.s_min <= sigma <= self.config.s_max:
										UpperCamelCase   =	min(self.config.s_churn / self.num_inference_steps   ,							2**0.5 - 1		)
								else:
										UpperCamelCase   =	0

								# sample eps ~ N(0, S_noise^2 * I)
								UpperCamelCase   =	self.config.s_noise * randn_tensor(sample.shape   ,							generator=__a		).to(sample.device		)
								UpperCamelCase   =	sigma + gamma * sigma
								UpperCamelCase   =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a = True   ,							) ->   Union[KarrasVeOutput, Tuple]:
								UpperCamelCase   =	sample_hat + sigma_hat * model_output
								UpperCamelCase   =	(sample_hat - pred_original_sample) / sigma_hat
								UpperCamelCase   =	sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=__a   ,							derivative=__a   ,							pred_original_sample=__a		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a = True   ,							) ->   Union[KarrasVeOutput, Tuple]:
								UpperCamelCase   =	sample_prev + sigma_prev * model_output
								UpperCamelCase   =	(sample_prev - pred_original_sample) / sigma_prev
								UpperCamelCase   =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
										return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=__a   ,							derivative=__a   ,							pred_original_sample=__a		)




						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   Any:
								raise NotImplementedError()













"""simple docstring"""





import argparse
from collections import defaultdict


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	F"{file}_{class_name}_{test_name}"
		done_test[_id] += 1

		with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
				UpperCamelCase   =	f.readlines()

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

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

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

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

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F"{spaces * ' '}{correct_line}"    )
						UpperCamelCase   =	UpperCamelCase   =	UpperCamelCase   =	UpperCamelCase   =	False
				else:
						new_lines.append(_SCREAMING_SNAKE_CASE    )

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


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		if fail is not None:
				with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
						UpperCamelCase   =	{l.strip() for l in f.readlines()}
		else:
				UpperCamelCase   =	None

		with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
				UpperCamelCase   =	f.readlines()

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


if __name__ == "__main__":
						lowerCAmelCase__     			=			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__     			=			parser.parse_args()

						main(args.correct_filename, args.fail_filename)






