




'''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      A       (						_a       ):
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							None



class      A       (						_a	,_a       ):
							lowercase_							     =							2







							@register_to_config
							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    float = 0.0_2				,     lowerCAmelCase_			:    float = 1_00				,     lowerCAmelCase_			:    float = 1.0_0_7				,     lowerCAmelCase_			:    float = 80				,     lowerCAmelCase_			:    float = 0.0_5				,     lowerCAmelCase_			:    float = 50				,     )     ->				Tuple:


														"""simple docstring"""


														_a      	=							sigma_max

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







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    Optional[int] = None   )     ->				torch.FloatTensor:


														"""simple docstring"""


														return sample







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Union[str, torch.device] = None   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							num_inference_steps
														_a      	=							np.arange(0				,     self.num_inference_steps   )[::-1].copy()
														_a      	=							torch.from_numpy(lowerCAmelCase_   ).to(lowerCAmelCase_   )
														_a      	=							[
														    (
														        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
														]
														_a      	=							torch.tensor(lowerCAmelCase_				,     dtype=torch.floataa				,     device=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[torch.Generator] = None   )     ->				Tuple[torch.FloatTensor, float]:


														"""simple docstring"""


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

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

														return sample_hat, sigma_hat







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    bool = True				,     )     ->				Union[KarrasVeOutput, Tuple]:


														"""simple docstring"""


														_a      	=							sample_hat + sigma_hat * model_output
														_a      	=							(sample_hat - pred_original_sample) / sigma_hat
														_a      	=							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   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    torch.FloatTensor				,     lowerCAmelCase_			:    bool = True				,     )     ->				Union[KarrasVeOutput, Tuple]:


														"""simple docstring"""


														_a      	=							sample_prev + sigma_prev * model_output
														_a      	=							(sample_prev - pred_original_sample) / sigma_prev
														_a      	=							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   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[Any]   )     ->				List[str]:


														"""simple docstring"""


														raise NotImplementedError()







'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case : str  				=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_snake_case : List[str]  				=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							tempfile.mkdtemp()
														os.makedirs(os.path.join(self.diffusers_dir				,     '''schedulers/'''   )   )
														_a      	=							self.diffusers_dir
														shutil.copy(
														    os.path.join(lowerCAmelCase_				,     '''src/diffusers/schedulers/scheduling_ddpm.py'''   )				,     os.path.join(self.diffusers_dir				,     '''schedulers/scheduling_ddpm.py'''   )				,     )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							'''src/diffusers'''
														shutil.rmtree(self.diffusers_dir   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    str=None   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + class_code
														if overwrite_result is not None:
																					_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
														_a      	=							black.Mode(target_versions={black.TargetVersion.PYaa}				,     line_length=1_19   )
														_a      	=							black.format_str(lowerCAmelCase_				,     mode=lowerCAmelCase_   )
														_a      	=							os.path.join(self.diffusers_dir				,     '''new_code.py'''   )
														with open(lowerCAmelCase_				,     '''w'''				,     newline='''\n'''   ) as f:
																					f.write(lowerCAmelCase_   )
														if overwrite_result is None:
																					self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_   )   ) == 0   )
														else:
																					check_copies.is_copy_consistent(f.name				,     overwrite=lowerCAmelCase_   )
																					with open(lowerCAmelCase_				,     '''r'''   ) as f:
																												self.assertTrue(f.read()				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   )
														self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Union[str, Any]:


														"""simple docstring"""


														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     REFERENCE_CODE + '''\n'''				,     )

														# With no empty line at the end
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     lowerCAmelCase_				,     )

														# Copy consistency with rename
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

														# Copy consistency with a really long name
														_a      	=							'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
														self.check_copy_consistency(
														    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'				,     F'{long_class_name}SchedulerOutput'				,     re.sub('''Bert'''				,     lowerCAmelCase_				,     lowerCAmelCase_   )				,     )

														# Copy consistency with overwrite
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     lowerCAmelCase_				,     overwrite_result=re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

