
'''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				(							__snake_case						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		None





class  A				(							__snake_case    ,			__snake_case						):
     __magic_name__						=		2





     @register_to_config
     def __init__(							self						,			SCREAMING_SNAKE_CASE = 0.02						,			SCREAMING_SNAKE_CASE = 100						,			SCREAMING_SNAKE_CASE = 1.007						,			SCREAMING_SNAKE_CASE = 80						,			SCREAMING_SNAKE_CASE = 0.05						,			SCREAMING_SNAKE_CASE = 50						,			) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						sigma_max

         # setable values
         A       :   int					=						None
         A       :   np.IntTensor					=						None
         A       :   torch.FloatTensor					=						None  # sigma(t_i)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				torch.FloatTensor:






         """simple docstring"""






         return sample





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				str:






         """simple docstring"""






         A       :   Optional[Any]					=						num_inference_steps
         A       :   Any					=						np.arange(0						,			self.num_inference_steps	)[::-1].copy()
         A       :   int					=						torch.from_numpy(SCREAMING_SNAKE_CASE	).to(SCREAMING_SNAKE_CASE	)
         A       :   List[Any]					=						[
             (
                 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       :   Dict					=						torch.tensor(SCREAMING_SNAKE_CASE						,			dtype=torch.floataa						,			device=SCREAMING_SNAKE_CASE	)





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






         """simple docstring"""






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

         # sample eps ~ N(0, S_noise^2 * I)
         A       :   Any					=						self.config.s_noise * randn_tensor(sample.shape						,			generator=SCREAMING_SNAKE_CASE	).to(sample.device	)
         A       :   Union[str, Any]					=						sigma + gamma * sigma
         A       :   Dict					=						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = True						,			) ->				Union[KarrasVeOutput, Tuple]:






         """simple docstring"""






         A       :   Optional[Any]					=						sample_hat + sigma_hat * model_output
         A       :   Optional[int]					=						(sample_hat - pred_original_sample) / sigma_hat
         A       :   Optional[Any]					=						sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
             return (sample_prev, derivative)

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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = True						,			) ->				Union[KarrasVeOutput, Tuple]:






         """simple docstring"""






         A       :   Tuple					=						sample_prev + sigma_prev * model_output
         A       :   Union[str, Any]					=						(sample_prev - pred_original_sample) / sigma_prev
         A       :   List[Any]					=						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=SCREAMING_SNAKE_CASE						,			derivative=SCREAMING_SNAKE_CASE						,			pred_original_sample=SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         raise NotImplementedError()











'''simple docstring'''




from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def        _lowerCAmelCase			(  __snake_case  : Tuple			)     ->     Dict:
     return {key.lstrip('-'			): value for key, value in zip(unknown_args[::2] ,							unknown_args[1::2]			)}






def        _lowerCAmelCase			(  )     ->     Tuple:
     __A						: int      =       ArgumentParser(
         'HuggingFace Datasets CLI tool' ,							usage='datasets-cli <command> [<args>]' ,							allow_abbrev=__snake_case			)
     __A						: Optional[Any]      =       parser.add_subparsers(help='datasets-cli command helpers'			)
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__snake_case			)
     EnvironmentCommand.register_subcommand(__snake_case			)
     TestCommand.register_subcommand(__snake_case			)
     RunBeamCommand.register_subcommand(__snake_case			)
     DummyDataCommand.register_subcommand(__snake_case			)

     # Parse args
     __A ,__A						: Optional[Any]      =       parser.parse_known_args()
     if not hasattr(__snake_case ,							'func'			):
          parser.print_help()
          exit(1			)
     __A						: Any      =       parse_unknown_args(__snake_case			)

     # Run
     __A						: List[Any]      =       args.func(__snake_case ,							**__snake_case			)
     service.run()


if __name__ == "__main__":
  main()