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


@dataclass
class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			torch.FloatTensor






class 							lowercase   (						_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Union[str, Any]      	=							1



		@register_to_config
		def __init__(					self							, A_ = 40							, A_ = 0.002							, A_ = 80.0							, A_ = 0.5							, A_ = 1.0							, A_ = 7.0							, A_ = True							, )    ->   List[Any]:
				"""simple docstring"""
				# standard deviation of the initial noise distribution
				UpperCamelCase            =	sigma_max

				UpperCamelCase            =	np.linspace(0							, 1							, A_			)
				UpperCamelCase            =	self._convert_to_karras(A_			)
				UpperCamelCase            =	self.sigma_to_t(A_			)

				# setable values
				UpperCamelCase            =	None
				UpperCamelCase            =	torch.from_numpy(A_			)
				UpperCamelCase            =	torch.from_numpy(A_			)
				UpperCamelCase            =	False
				UpperCamelCase            =	False



		def __UpperCamelCase					(					self							, A_							, A_=None			)    ->   Optional[Any]:
				"""simple docstring"""
				if schedule_timesteps is None:
						UpperCamelCase            =	self.timesteps

				UpperCamelCase            =	(schedule_timesteps == timestep).nonzero()
				return indices.item()



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   torch.FloatTensor:
				"""simple docstring"""
				# Get sigma corresponding to timestep
				if isinstance(A_							, torch.Tensor			):
						UpperCamelCase            =	timestep.to(self.timesteps.device			)
				UpperCamelCase            =	self.index_for_timestep(A_			)
				UpperCamelCase            =	self.sigmas[step_idx]

				UpperCamelCase            =	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

				UpperCamelCase            =	True
				return sample



		def __UpperCamelCase					(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				if not isinstance(A_							, np.ndarray			):
						UpperCamelCase            =	np.array(A_							, dtype=np.floataa			)

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

				return timesteps



		def __UpperCamelCase					(					self							, A_ = None							, A_ = None							, A_ = None							, )    ->   Optional[Any]:
				"""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(A_			)			):
								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            =	np.array(A_							, dtype=np.intaa			)
						UpperCamelCase            =	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            =	num_inference_steps

						UpperCamelCase            =	self.config.num_train_timesteps // self.num_inference_steps
						UpperCamelCase            =	(np.arange(0							, A_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
						UpperCamelCase            =	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            =	self.config.num_train_timesteps
				UpperCamelCase            =	timesteps[::-1].copy()
				UpperCamelCase            =	ramp / (num_train_timesteps - 1)
				UpperCamelCase            =	self._convert_to_karras(A_			)
				UpperCamelCase            =	self.sigma_to_t(A_			)

				UpperCamelCase            =	np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
				UpperCamelCase            =	torch.from_numpy(A_			).to(device=A_			)

				if str(A_			).startswith('mps'			):
						# mps does not support float64
						UpperCamelCase            =	torch.from_numpy(A_			).to(A_							, dtype=torch.floataa			)
				else:
						UpperCamelCase            =	torch.from_numpy(A_			).to(device=A_			)



		def __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.config.sigma_min
				UpperCamelCase            =	self.config.sigma_max

				UpperCamelCase            =	self.config.rho
				UpperCamelCase            =	sigma_min ** (1 / rho)
				UpperCamelCase            =	sigma_max ** (1 / rho)
				UpperCamelCase            =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas



		def __UpperCamelCase					(					self							, A_			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	self.config.sigma_data

				UpperCamelCase            =	sigma_data**2 / (sigma**2 + sigma_data**2)
				UpperCamelCase            =	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
				return c_skip, c_out



		def __UpperCamelCase					(					self							, A_			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	self.config.sigma_min
				UpperCamelCase            =	self.config.sigma_data

				UpperCamelCase            =	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
				UpperCamelCase            =	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
				return c_skip, c_out



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_ = None							, A_ = True							, )    ->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:
				"""simple docstring"""

				if (
				    isinstance(A_							, A_			)
				    or isinstance(A_							, torch.IntTensor			)
				    or isinstance(A_							, 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(A_							, torch.Tensor			):
						UpperCamelCase            =	timestep.to(self.timesteps.device			)

				UpperCamelCase            =	self.config.sigma_min
				UpperCamelCase            =	self.config.sigma_max

				UpperCamelCase            =	self.index_for_timestep(A_			)

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

				# Get scalings for boundary conditions
				UpperCamelCase      ,    UpperCamelCase            =	self.get_scalings_for_boundary_condition(A_			)

				# 1. Denoise model output using boundary conditions
				UpperCamelCase            =	c_out * model_output + c_skip * sample
				if self.config.clip_denoised:
						UpperCamelCase            =	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            =	randn_tensor(
						    model_output.shape							, dtype=model_output.dtype							, device=model_output.device							, generator=A_			)
				else:
						UpperCamelCase            =	torch.zeros_like(A_			)
				UpperCamelCase            =	noise * self.config.s_noise

				UpperCamelCase            =	sigma_next.clamp(min=A_							, max=A_			)

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

				if not return_dict:
						return (prev_sample,)

				return CMStochasticIterativeSchedulerOutput(prev_sample=A_			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, )    ->   torch.FloatTensor:
				"""simple docstring"""
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				UpperCamelCase            =	self.sigmas.to(device=original_samples.device							, dtype=original_samples.dtype			)
				if original_samples.device.type == "mps" and torch.is_floating_point(A_			):
						# mps does not support float64
						UpperCamelCase            =	self.timesteps.to(original_samples.device							, dtype=torch.floataa			)
						UpperCamelCase            =	timesteps.to(original_samples.device							, dtype=torch.floataa			)
				else:
						UpperCamelCase            =	self.timesteps.to(original_samples.device			)
						UpperCamelCase            =	timesteps.to(original_samples.device			)

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

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

				UpperCamelCase            =	original_samples + noise * sigma
				return noisy_samples



		def __len__(					self			)    ->   Dict:
				"""simple docstring"""
				return self.config.num_train_timesteps


from __future__ import annotations


def  A			(   lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase      ,    UpperCamelCase            =	0, len(lowercase					) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
					print(double_linear_search(list(range(100)), 40))
