



"""simple docstring"""
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_(   A__    ):





					'''simple docstring'''



					lowercase__   :       torch.FloatTensor
					lowercase__   :       torch.FloatTensor
					lowercase__   :       Optional[torch.FloatTensor]										= None


class 							lowerCamelCase_(   A__,				A__    ):





					'''simple docstring'''



					lowercase__   :       Optional[Any]										= 2

					@register_to_config
					def __init__(    self				,       lowerCamelCase__ = 0.0_2				,       lowerCamelCase__ = 1_0_0				,       lowerCamelCase__ = 1.0_0_7				,       lowerCamelCase__ = 8_0				,       lowerCamelCase__ = 0.0_5				,       lowerCamelCase__ = 5_0				,       ):
									# standard deviation of the initial noise distribution
									_lowerCamelCase        =				sigma_max

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

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
									return sample

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
									_lowerCamelCase        =				num_inference_steps
									_lowerCamelCase        =				np.arange(0				,       self.num_inference_steps   )[::-1].copy()
									_lowerCamelCase        =				torch.from_numpy(lowerCamelCase__   ).to(lowerCamelCase__   )
									_lowerCamelCase        =				[
									    (
									        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
									]
									_lowerCamelCase        =				torch.tensor(lowerCamelCase__				,       dtype=torch.floataa				,       device=lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
									if self.config.s_min <= sigma <= self.config.s_max:
													_lowerCamelCase        =				min(self.config.s_churn / self.num_inference_steps				,       2**0.5 - 1   )
									else:
													_lowerCamelCase        =				0

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

									return sample_hat, sigma_hat

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = True				,       ):
									_lowerCamelCase        =				sample_hat + sigma_hat * model_output
									_lowerCamelCase        =				(sample_hat - pred_original_sample) / sigma_hat
									_lowerCamelCase        =				sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
													return (sample_prev, derivative)

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

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = True				,       ):
									_lowerCamelCase        =				sample_prev + sigma_prev * model_output
									_lowerCamelCase        =				(sample_prev - pred_original_sample) / sigma_prev
									_lowerCamelCase        =				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=lowerCamelCase__				,       derivative=lowerCamelCase__				,       pred_original_sample=lowerCamelCase__   )





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									raise NotImplementedError()





"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def        lowerCAmelCase_(   lowercase_     :							List[Any]							)	->       Optional[Any]:
				from diffusers.utils.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(lowercase_							)




def        lowerCAmelCase_(   lowercase_     :							List[str]							)	->       List[str]:
				from diffusers.utils.testing_utils import pytest_terminal_summary_main

				_lowerCamelCase        =				terminalreporter.config.getoption('''--make-reports'''							)
				if make_reports:
								pytest_terminal_summary_main(lowercase_						,   id=lowercase_							)
