


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


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



@dataclass
class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  torch.FloatTensor




class  UpperCAmelCase_						(				_a,  _a):
       '''simple docstring'''
       __UpperCamelCase   :  int				=       1






       @register_to_config
       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 40			,       __SCREAMING_SNAKE_CASE = 0.002			,       __SCREAMING_SNAKE_CASE = 80.0			,       __SCREAMING_SNAKE_CASE = 0.5			,       __SCREAMING_SNAKE_CASE = 1.0			,       __SCREAMING_SNAKE_CASE = 7.0			,       __SCREAMING_SNAKE_CASE = True			,       ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		sigma_max

              UpperCamelCase     :						List[str] 						=		np.linspace(0			,       1			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						str 						=		self._convert_to_karras(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		self.sigma_to_t(__SCREAMING_SNAKE_CASE    )

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              if schedule_timesteps is None:
                     UpperCamelCase     :						Dict 						=		self.timesteps

              UpperCamelCase     :						Optional[Any] 						=		(schedule_timesteps == timestep).nonzero()
              return indices.item()






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE			,       torch.Tensor    ):
                     UpperCamelCase     :						int 						=		timestep.to(self.timesteps.device    )
              UpperCamelCase     :						Union[str, Any] 						=		self.index_for_timestep(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Tuple 						=		self.sigmas[step_idx]

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

              UpperCamelCase     :						str 						=		True
              return sample






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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

              UpperCamelCase     :						int 						=		1_000 * 0.25 * np.log(sigmas + 1e-44    )

              return timesteps






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       ):




              """simple docstring"""




              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(__SCREAMING_SNAKE_CASE    )    ):
                            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}."""    )

                     UpperCamelCase     :						Tuple 						=		np.array(__SCREAMING_SNAKE_CASE			,       dtype=np.intaa    )
                     UpperCamelCase     :						Union[str, Any] 						=		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."""    )

                     UpperCamelCase     :						Optional[Any] 						=		num_inference_steps

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

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

              UpperCamelCase     :						Optional[Any] 						=		np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
              UpperCamelCase     :						int 						=		torch.from_numpy(__SCREAMING_SNAKE_CASE    ).to(device=__SCREAMING_SNAKE_CASE    )

              if str(__SCREAMING_SNAKE_CASE    ).startswith('''mps'''    ):
                     # mps does not support float64
                     UpperCamelCase     :						Optional[int] 						=		torch.from_numpy(__SCREAMING_SNAKE_CASE    ).to(__SCREAMING_SNAKE_CASE			,       dtype=torch.floataa    )
              else:
                     UpperCamelCase     :						Any 						=		torch.from_numpy(__SCREAMING_SNAKE_CASE    ).to(device=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						float 						=		self.config.sigma_min
              UpperCamelCase     :						float 						=		self.config.sigma_max

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		self.config.sigma_data

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		self.config.sigma_min
              UpperCamelCase     :						int 						=		self.config.sigma_data

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = True			,       ):




              """simple docstring"""




              if (
                  isinstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
                  or isinstance(__SCREAMING_SNAKE_CASE			,       torch.IntTensor    )
                  or isinstance(__SCREAMING_SNAKE_CASE			,       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(__SCREAMING_SNAKE_CASE			,       torch.Tensor    ):
                     UpperCamelCase     :						List[Any] 						=		timestep.to(self.timesteps.device    )

              UpperCamelCase     :						List[str] 						=		self.config.sigma_min
              UpperCamelCase     :						Any 						=		self.config.sigma_max

              UpperCamelCase     :						Dict 						=		self.index_for_timestep(__SCREAMING_SNAKE_CASE    )

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

              # Get scalings for boundary conditions
              UpperCamelCase						,			UpperCamelCase     :						List[Any] 						=		self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE    )

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

              UpperCamelCase     :						Any 						=		sigma_next.clamp(min=__SCREAMING_SNAKE_CASE			,       max=__SCREAMING_SNAKE_CASE    )

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

              if not return_dict:
                     return (prev_sample,)

              return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		self.sigmas.to(device=original_samples.device			,       dtype=original_samples.dtype    )
              if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE    ):
                     # mps does not support float64
                     UpperCamelCase     :						Tuple 						=		self.timesteps.to(original_samples.device			,       dtype=torch.floataa    )
                     UpperCamelCase     :						Optional[Any] 						=		timesteps.to(original_samples.device			,       dtype=torch.floataa    )
              else:
                     UpperCamelCase     :						Dict 						=		self.timesteps.to(original_samples.device    )
                     UpperCamelCase     :						Tuple 						=		timesteps.to(original_samples.device    )

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

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

              UpperCamelCase     :						Any 						=		original_samples + noise * sigma
              return noisy_samples






       def __len__(  self    ):




              """simple docstring"""




              return self.config.num_train_timesteps





import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

