






'''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


_A      :     Optional[int]       	=logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class _lowercase      (							__a						):
							a							 =			42



class _lowercase      (							__a							, __a						):
							a							 =			1




							@register_to_config
							def __init__(				self:							Union[str, Any]     ,				UpperCamelCase__:							int = 40     ,				UpperCamelCase__:							float = 0.002     ,				UpperCamelCase__:							float = 80.0     ,				UpperCamelCase__:							float = 0.5     ,				UpperCamelCase__:							float = 1.0     ,				UpperCamelCase__:							float = 7.0     ,				UpperCamelCase__:							bool = True     ,				):
														lowerCamelCase__   :      str			    =			sigma_max

														lowerCamelCase__   :      int			    =			np.linspace(0     ,				1     ,				UpperCamelCase__	)
														lowerCamelCase__   :      int			    =			self._convert_to_karras(UpperCamelCase__	)
														lowerCamelCase__   :      int			    =			self.sigma_to_t(UpperCamelCase__	)

														# setable values
														lowerCamelCase__   :      Tuple			    =			None
														lowerCamelCase__   :      Tuple			    =			torch.from_numpy(UpperCamelCase__	)
														lowerCamelCase__   :      Tuple			    =			torch.from_numpy(UpperCamelCase__	)
														lowerCamelCase__   :      Dict			    =			False
														lowerCamelCase__   :      str			    =			False




							def 						lowerCamelCase_							(				self:							int     ,				UpperCamelCase__:							Union[str, Any]     ,				UpperCamelCase__:							int=None	):

														if schedule_timesteps is None:
																					lowerCamelCase__   :      Optional[Any]			    =			self.timesteps

														lowerCamelCase__   :      int			    =			(schedule_timesteps == timestep).nonzero()
														return indices.item()




							def 						lowerCamelCase_							(				self:							str     ,				UpperCamelCase__:							torch.FloatTensor     ,				UpperCamelCase__:							Union[float, torch.FloatTensor]	):

														if isinstance(UpperCamelCase__     ,				torch.Tensor	):
																					lowerCamelCase__   :      str			    =			timestep.to(self.timesteps.device	)
														lowerCamelCase__   :      str			    =			self.index_for_timestep(UpperCamelCase__	)
														lowerCamelCase__   :      Union[str, Any]			    =			self.sigmas[step_idx]

														lowerCamelCase__   :      Union[str, Any]			    =			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

														lowerCamelCase__   :      Dict			    =			True
														return sample




							def 						lowerCamelCase_							(				self:							str     ,				UpperCamelCase__:							Union[float, np.ndarray]	):

														if not isinstance(UpperCamelCase__     ,				np.ndarray	):
																					lowerCamelCase__   :      List[Any]			    =			np.array(UpperCamelCase__     ,				dtype=np.floataa	)

														lowerCamelCase__   :      List[Any]			    =			1_000 * 0.25 * np.log(sigmas + 1e-44	)

														return timesteps




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							Optional[int] = None     ,				UpperCamelCase__:							Union[str, torch.device] = None     ,				UpperCamelCase__:							Optional[List[int]] = 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}.'''	)

																					lowerCamelCase__   :      str			    =			np.array(UpperCamelCase__     ,				dtype=np.intaa	)
																					lowerCamelCase__   :      str			    =			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.'''	)

																					lowerCamelCase__   :      List[Any]			    =			num_inference_steps

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

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

														lowerCamelCase__   :      Tuple			    =			np.concatenate([sigmas, [self.sigma_min]]	).astype(np.floataa	)
														lowerCamelCase__   :      Optional[Any]			    =			torch.from_numpy(UpperCamelCase__	).to(device=UpperCamelCase__	)

														if str(UpperCamelCase__	).startswith("""mps"""	):
																					# mps does not support float64
																					lowerCamelCase__   :      List[Any]			    =			torch.from_numpy(UpperCamelCase__	).to(UpperCamelCase__     ,				dtype=torch.floataa	)
														else:
																					lowerCamelCase__   :      int			    =			torch.from_numpy(UpperCamelCase__	).to(device=UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							int	):
														lowerCamelCase__   :      Union[str, Any]			    =			self.config.sigma_min
														lowerCamelCase__   :      Dict			    =			self.config.sigma_max

														lowerCamelCase__   :      str			    =			self.config.rho
														lowerCamelCase__   :      int			    =			sigma_min ** (1 / rho)
														lowerCamelCase__   :      Tuple			    =			sigma_max ** (1 / rho)
														lowerCamelCase__   :      int			    =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
														return sigmas




							def 						lowerCamelCase_							(				self:							int     ,				UpperCamelCase__:							Tuple	):
														lowerCamelCase__   :      Any			    =			self.config.sigma_data

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




							def 						lowerCamelCase_							(				self:							str     ,				UpperCamelCase__:							Dict	):
														lowerCamelCase__   :      Any			    =			self.config.sigma_min
														lowerCamelCase__   :      Union[str, Any]			    =			self.config.sigma_data

														lowerCamelCase__   :      Any			    =			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
														lowerCamelCase__   :      int			    =			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
														return c_skip, c_out




							def 						lowerCamelCase_							(				self:							int     ,				UpperCamelCase__:							torch.FloatTensor     ,				UpperCamelCase__:							Union[float, torch.FloatTensor]     ,				UpperCamelCase__:							torch.FloatTensor     ,				UpperCamelCase__:							Optional[torch.Generator] = None     ,				UpperCamelCase__:							bool = 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	):
																					lowerCamelCase__   :      List[Any]			    =			timestep.to(self.timesteps.device	)

														lowerCamelCase__   :      List[Any]			    =			self.config.sigma_min
														lowerCamelCase__   :      Optional[int]			    =			self.config.sigma_max

														lowerCamelCase__   :      Union[str, Any]			    =			self.index_for_timestep(UpperCamelCase__	)

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

														# Get scalings for boundary conditions
														lowerCamelCase__    ,						lowerCamelCase__   :      Tuple			    =			self.get_scalings_for_boundary_condition(UpperCamelCase__	)

														# 1. Denoise model output using boundary conditions
														lowerCamelCase__   :      List[str]			    =			c_out * model_output + c_skip * sample
														if self.config.clip_denoised:
																					lowerCamelCase__   :      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:
																					lowerCamelCase__   :      Tuple			    =			randn_tensor(
																					    model_output.shape     ,				dtype=model_output.dtype     ,				device=model_output.device     ,				generator=UpperCamelCase__	)
														else:
																					lowerCamelCase__   :      Optional[Any]			    =			torch.zeros_like(UpperCamelCase__	)
														lowerCamelCase__   :      Union[str, Any]			    =			noise * self.config.s_noise

														lowerCamelCase__   :      Any			    =			sigma_next.clamp(min=UpperCamelCase__     ,				max=UpperCamelCase__	)

														# 3. Return noisy sample
														# tau = sigma_hat, eps = sigma_min
														lowerCamelCase__   :      int			    =			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

														if not return_dict:
																					return (prev_sample,)

														return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							int     ,				UpperCamelCase__:							torch.FloatTensor     ,				UpperCamelCase__:							torch.FloatTensor     ,				UpperCamelCase__:							torch.FloatTensor     ,				):
														lowerCamelCase__   :      Union[str, Any]			    =			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
																					lowerCamelCase__   :      Any			    =			self.timesteps.to(original_samples.device     ,				dtype=torch.floataa	)
																					lowerCamelCase__   :      Union[str, Any]			    =			timesteps.to(original_samples.device     ,				dtype=torch.floataa	)
														else:
																					lowerCamelCase__   :      str			    =			self.timesteps.to(original_samples.device	)
																					lowerCamelCase__   :      Union[str, Any]			    =			timesteps.to(original_samples.device	)

														lowerCamelCase__   :      Optional[int]			    =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

														lowerCamelCase__   :      str			    =			original_samples + noise * sigma
														return noisy_samples




							def __len__(				self:							Optional[int]	):
														return self.config.num_train_timesteps







'''simple docstring'''







def 		snake_case_				(_a      :				Any       ,  _a      :				Union[str, Any]	):
    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    UpperCAmelCase     			=						[0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    UpperCAmelCase     			=						1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(_a       ,  n + 1	):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()




