





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							(						__lowercase    ):
      _snake_case				  =42
      _snake_case				  =42
      _snake_case				  =None

class   A							(						__lowercase		,   __lowercase    ):
      _snake_case				  =2



      @register_to_config
      def __init__(						self:    List[str]		,	_lowerCAmelCase:    float = 0.02		,	_lowerCAmelCase:    float = 100		,	_lowerCAmelCase:    float = 1.0_07		,	_lowerCAmelCase:    float = 80		,	_lowerCAmelCase:    float = 0.05		,	_lowerCAmelCase:    float = 50		,	)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =sigma_max

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



      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    Optional[int] = None							)    ->     torch.FloatTensor:
         '''simple docstring'''

         return sample



      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    Union[str, torch.device] = None							)    ->     Optional[Any]:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    List[str]		,	_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:
            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=_lowerCAmelCase							).to(sample.device							)
         UpperCAmelCase_           =sigma + gamma * sigma
         UpperCAmelCase_           =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    bool = True		,	)    ->     Union[KarrasVeOutput, Tuple]:
         '''simple docstring'''

         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=_lowerCAmelCase		,	derivative=_lowerCAmelCase		,	pred_original_sample=_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    float		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    bool = True		,	)    ->     Union[KarrasVeOutput, Tuple]:
         '''simple docstring'''

         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=_lowerCAmelCase		,	derivative=_lowerCAmelCase		,	pred_original_sample=_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    Dict		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    Any							)    ->     List[Any]:
         '''simple docstring'''

         raise NotImplementedError()











"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Union[str, Any]       =inspect.getfile(accelerate.test_utils      )
											__lowerCamelCase   :   Union[str, Any]       =os.path.sep.join(
											    mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']      )

											from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

											__lowerCamelCase   :   List[str]       =test_metrics




				@require_cpu
				def        __lowercase	(  self	:Optional[Any]      ):
											debug_launcher(self.test_metrics.main    ,     num_processes=1      )




				@require_cpu
				def        __lowercase	(  self	:int      ):
											debug_launcher(self.test_metrics.main      )




				@require_single_gpu
				def        __lowercase	(  self	:int      ):
											self.test_metrics.main()




				@require_multi_gpu
				def        __lowercase	(  self	:Dict      ):
											print(f'Found {torch.cuda.device_count()} devices.'      )
											__lowerCamelCase   :   Union[str, Any]       =['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
											with patch_environment(omp_num_threads=1      ):
																		execute_subprocess_async(__lowercase    ,     env=os.environ.copy()      )




