





'''simple docstring'''






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

import numpy as np
import torch

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


snake_case_     			=						logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class 	_lowercase    (				a					):
   _UpperCamelCase									= 42







class 	_lowercase    (				a		,       a					):
   _UpperCamelCase									= 1


   @register_to_config
   def __init__(			self     , _UpperCAmelCase = 40     , _UpperCAmelCase = 0.002     , _UpperCAmelCase = 80.0     , _UpperCAmelCase = 0.5     , _UpperCAmelCase = 1.0     , _UpperCAmelCase = 7.0     , _UpperCAmelCase = True     , ):
       # standard deviation of the initial noise distribution
       A							:							int				=    sigma_max

       A							:							List[str]				=    np.linspace(0     , 1     , _UpperCAmelCase       )
       A							:							int				=    self._convert_to_karras(_UpperCAmelCase       )
       A							:							Any				=    self.sigma_to_t(_UpperCAmelCase       )

       # setable values
       A							:							Optional[int]				=    None
       A							:							Optional[int]				=    torch.from_numpy(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    torch.from_numpy(_UpperCAmelCase       )
       A							:							int				=    False
       A							:							Optional[int]				=    False


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=None       ):
       if schedule_timesteps is None:
           A							:							Optional[Any]				=    self.timesteps

       A							:							Tuple				=    (schedule_timesteps == timestep).nonzero()
       return indices.item()


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # Get sigma corresponding to timestep
       if isinstance(_UpperCAmelCase     , torch.Tensor       ):
           A							:							Dict				=    timestep.to(self.timesteps.device       )
       A							:							List[Any]				=    self.index_for_timestep(_UpperCAmelCase       )
       A							:							Optional[int]				=    self.sigmas[step_idx]

       A							:							List[Any]				=    sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       A							:							Optional[int]				=    True
       return sample


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if not isinstance(_UpperCAmelCase     , np.ndarray       ):
           A							:							List[str]				=    np.array(_UpperCAmelCase     , dtype=np.floataa       )

       A							:							Optional[Any]				=    1_000 * 0.25 * np.log(sigmas + 1E-4_4       )

       return timesteps


   def        snake_case					(			self     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , ):
       if num_inference_steps is None and timesteps is None:
           raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''       )

       if num_inference_steps is not None and timesteps is not None:
           raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''       )

       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
           for i in range(1     , len(_UpperCAmelCase       )       ):
               if timesteps[i] >= timesteps[i - 1]:
                   raise ValueError('''`timesteps` must be in descending order.'''       )

           if timesteps[0] >= self.config.num_train_timesteps:
               raise ValueError(
                   f'''`timesteps` must start before `self.config.train_timesteps`:'''
                   f''' {self.config.num_train_timesteps}.'''       )

           A							:							Dict				=    np.array(_UpperCAmelCase     , dtype=np.intaa       )
           A							:							Dict				=    True
       else:
           if num_inference_steps > self.config.num_train_timesteps:
               raise ValueError(
                   f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                   f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                   f''' maximal {self.config.num_train_timesteps} timesteps.'''       )

           A							:							Tuple				=    num_inference_steps

           A							:							List[Any]				=    self.config.num_train_timesteps // self.num_inference_steps
           A							:							Any				=    (np.arange(0     , _UpperCAmelCase       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
           A							:							str				=    False

       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       A							:							int				=    self.config.num_train_timesteps
       A							:							Any				=    timesteps[::-1].copy()
       A							:							List[str]				=    ramp / (num_train_timesteps - 1)
       A							:							List[str]				=    self._convert_to_karras(_UpperCAmelCase       )
       A							:							List[Any]				=    self.sigma_to_t(_UpperCAmelCase       )

       A							:							str				=    np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
       A							:							str				=    torch.from_numpy(_UpperCAmelCase       ).to(device=_UpperCAmelCase       )

       if str(_UpperCAmelCase       ).startswith('''mps'''       ):
           # mps does not support float64
           A							:							Tuple				=    torch.from_numpy(_UpperCAmelCase       ).to(_UpperCAmelCase     , dtype=torch.floataa       )
       else:
           A							:							Any				=    torch.from_numpy(_UpperCAmelCase       ).to(device=_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							float				=    self.config.sigma_min
       A							:							float				=    self.config.sigma_max

       A							:							List[str]				=    self.config.rho
       A							:							int				=    sigma_min ** (1 / rho)
       A							:							Dict				=    sigma_max ** (1 / rho)
       A							:							Tuple				=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							List[str]				=    self.config.sigma_data

       A							:							Optional[Any]				=    sigma_data**2 / (sigma**2 + sigma_data**2)
       A							:							str				=    sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							List[Any]				=    self.config.sigma_min
       A							:							Dict				=    self.config.sigma_data

       A							:							List[str]				=    sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       A							:							str				=    (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = True     , ):

       if (
           isinstance(_UpperCAmelCase     , _UpperCAmelCase       )
           or isinstance(_UpperCAmelCase     , torch.IntTensor       )
           or isinstance(_UpperCAmelCase     , torch.LongTensor       )
       ):
           raise ValueError(
               (
                   '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                   f''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                   ''' one of the `scheduler.timesteps` as a timestep.'''
               )     , )

       if not self.is_scale_input_called:
           logger.warning(
               '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
               '''See `StableDiffusionPipeline` for a usage example.'''       )

       if isinstance(_UpperCAmelCase     , torch.Tensor       ):
           A							:							Dict				=    timestep.to(self.timesteps.device       )

       A							:							int				=    self.config.sigma_min
       A							:							int				=    self.config.sigma_max

       A							:							List[Any]				=    self.index_for_timestep(_UpperCAmelCase       )

       # sigma_next corresponds to next_t in original implementation
       A							:							Dict				=    self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
           A							:							List[str]				=    self.sigmas[step_index + 1]
       else:
           # Set sigma_next to sigma_min
           A							:							Optional[Any]				=    self.sigmas[-1]

       # Get scalings for boundary conditions
       A,						A							:							str				=    self.get_scalings_for_boundary_condition(_UpperCAmelCase       )

       # 1. Denoise model output using boundary conditions
       A							:							Optional[int]				=    c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
           A							:							Optional[int]				=    denoised.clamp(-1     , 1       )

       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps       ) > 1:
           A							:							Optional[int]				=    randn_tensor(
               model_output.shape     , dtype=model_output.dtype     , device=model_output.device     , generator=_UpperCAmelCase       )
       else:
           A							:							List[Any]				=    torch.zeros_like(_UpperCAmelCase       )
       A							:							int				=    noise * self.config.s_noise

       A							:							Dict				=    sigma_next.clamp(min=_UpperCAmelCase     , max=_UpperCAmelCase       )

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       A							:							List[Any]				=    denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
           return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       A							:							int				=    self.sigmas.to(device=original_samples.device     , dtype=original_samples.dtype       )
       if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase       ):
           # mps does not support float64
           A							:							str				=    self.timesteps.to(original_samples.device     , dtype=torch.floataa       )
           A							:							str				=    timesteps.to(original_samples.device     , dtype=torch.floataa       )
       else:
           A							:							str				=    self.timesteps.to(original_samples.device       )
           A							:							Any				=    timesteps.to(original_samples.device       )

       A							:							List[str]				=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

       A							:							Tuple				=    sigmas[step_indices].flatten()
       while len(sigma.shape       ) < len(original_samples.shape       ):
           A							:							List[Any]				=    sigma.unsqueeze(-1       )

       A							:							Optional[int]				=    original_samples + noise * sigma
       return noisy_samples


   def __len__(			self       ):
       return self.config.num_train_timesteps







'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def 				_lowerCamelCase(	UpperCamelCase__     :			Any		)		->	List[str]:
    return EnvironmentCommand()





class 	_lowercase    (				a					):


   @staticmethod
   def        snake_case					(			_UpperCAmelCase       ):
       A							:							List[str]				=    parser.add_parser('''env'''     , help='''Print relevant system environment info.'''       )
       download_parser.set_defaults(func=_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''`datasets` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''PyArrow version''': pyarrow.__version__,
           '''Pandas version''': pandas.__version__,
       }

       print('''\nCopy-and-paste the text below in your GitHub issue.\n'''       )
       print(self.format_dict(_UpperCAmelCase       )       )

       return info


   @staticmethod
   def        snake_case					(			_UpperCAmelCase       ):
       return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]       ) + "\n"
