



"""simple docstring"""

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

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class  _UpperCAmelCase (    lowerCAmelCase__):
	_lowerCAmelCase     :    torch.FloatTensor
	_lowerCAmelCase     :    Optional[torch.FloatTensor]	      =	None





def __lowercase    (							_a	,    _a=0.999	,    _a="cosine"	,    ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(_a					):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(_a					):
												return math.exp(t * -12.0					)

				else:
								raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"					)

				snake_case_				:  Union[str, Any]       =			[]
				for i in range(_a					):
								snake_case_				:  Union[str, Any]       =			i / num_diffusion_timesteps
								snake_case_				:  int       =			(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(_a					) / alpha_bar_fn(_a					)	,    _a					)					)
				return torch.tensor(_a	,    dtype=torch.floataa					)






class  _UpperCAmelCase (    lowerCAmelCase__					,  lowerCAmelCase__):
	_lowerCAmelCase     :    Union[str, Any]	      =	1

	@register_to_config
	def __init__(  self	: Tuple						,  lowercase_	: int = 1000						,  lowercase_	: float = 0.00_01						,  lowercase_	: float = 0.02						,  lowercase_	: str = "linear"						,  lowercase_	: Optional[Union[np.ndarray, List[float]]] = None						,  lowercase_	: bool = True						,  lowercase_	: bool = True						,  lowercase_	: int = 0						,  lowercase_	: str = "epsilon"						,  lowercase_	: float = 1.0						,  **lowercase_	: int						,  ):
					if kwargs.get('''set_alpha_to_one'''						,  lowercase_       ) is not None:
									snake_case_				:  int       =			(
									    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
									)
									deprecate('''set_alpha_to_one'''						,  '''1.0.0'''						,  lowercase_						,  standard_warn=lowercase_       )
									snake_case_				:  Dict       =			kwargs['''set_alpha_to_one''']
					if trained_betas is not None:
									snake_case_				:  str       =			torch.tensor(lowercase_						,  dtype=torch.floataa       )
					elif beta_schedule == "linear":
									snake_case_				:  List[str]       =			torch.linspace(lowercase_						,  lowercase_						,  lowercase_						,  dtype=torch.floataa       )
					elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									snake_case_				:  Dict       =			(
									    torch.linspace(beta_start**0.5						,  beta_end**0.5						,  lowercase_						,  dtype=torch.floataa       ) ** 2
									)
					elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									snake_case_				:  Tuple       =			betas_for_alpha_bar(lowercase_       )
					else:
									raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"       )

					snake_case_				:  List[Any]       =			1.0 - self.betas
					snake_case_				:  Optional[Any]       =			torch.cumprod(self.alphas						,  dim=0       )

					# At every step in inverted ddim, we are looking into the next alphas_cumprod
					# For the final step, there is no next alphas_cumprod, and the index is out of bounds
					# `set_alpha_to_zero` decides whether we set this parameter simply to zero
					# in this case, self.step() just output the predicted noise
					# or whether we use the final alpha of the "non-previous" one.
					snake_case_				:  str       =			torch.tensor(0.0       ) if set_alpha_to_zero else self.alphas_cumprod[-1]

					# standard deviation of the initial noise distribution
					snake_case_				:  str       =			1.0

					# setable values
					snake_case_				:  Dict       =			None
					snake_case_				:  Tuple       =			torch.from_numpy(np.arange(0						,  lowercase_       ).copy().astype(np.intaa       )       )

	def 		_snake_case      (  self	: List[str]						,  lowercase_	: torch.FloatTensor						,  lowercase_	: Optional[int] = None       ):
					return sample

	def 		_snake_case      (  self	: List[str]						,  lowercase_	: int						,  lowercase_	: Union[str, torch.device] = None       ):

					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."       )

					snake_case_				:  Optional[Any]       =			num_inference_steps
					snake_case_				:  Union[str, Any]       =			self.config.num_train_timesteps // self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					snake_case_				:  str       =			(np.arange(0						,  lowercase_       ) * step_ratio).round().copy().astype(np.intaa       )
					snake_case_				:  List[Any]       =			torch.from_numpy(lowercase_       ).to(lowercase_       )
					self.timesteps += self.config.steps_offset

	def 		_snake_case      (  self	: Tuple						,  lowercase_	: torch.FloatTensor						,  lowercase_	: int						,  lowercase_	: torch.FloatTensor						,  lowercase_	: float = 0.0						,  lowercase_	: bool = False						,  lowercase_	: Optional[torch.FloatTensor] = None						,  lowercase_	: bool = True						,  ):
					# 1. get previous step value (=t+1)
					snake_case_				:  str       =			timestep + self.config.num_train_timesteps // self.num_inference_steps

					# 2. compute alphas, betas
					# change original implementation to exactly match noise levels for analogous forward process
					snake_case_				:  str       =			self.alphas_cumprod[timestep]
					snake_case_				:  Optional[Any]       =			(
					    self.alphas_cumprod[prev_timestep]
					    if prev_timestep < self.config.num_train_timesteps
					    else self.final_alpha_cumprod
					)

					snake_case_				:  Dict       =			1 - alpha_prod_t

					# 3. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					if self.config.prediction_type == "epsilon":
									snake_case_				:  Union[str, Any]       =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									snake_case_				:  List[Any]       =			model_output
					elif self.config.prediction_type == "sample":
									snake_case_				:  List[Any]       =			model_output
									snake_case_				:  Dict       =			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
					elif self.config.prediction_type == "v_prediction":
									snake_case_				:  Any       =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									snake_case_				:  Any       =			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
					else:
									raise ValueError(
									    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
									    ''' `v_prediction`'''       )

					# 4. Clip or threshold "predicted x_0"
					if self.config.clip_sample:
									snake_case_				:  List[Any]       =			pred_original_sample.clamp(
									    -self.config.clip_sample_range						,  self.config.clip_sample_range       )

					# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					snake_case_				:  Optional[int]       =			(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

					# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					snake_case_				:  int       =			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

					if not return_dict:
									return (prev_sample, pred_original_sample)
					return DDIMSchedulerOutput(prev_sample=lowercase_						,  pred_original_sample=lowercase_       )






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










"""simple docstring"""

def __lowercase    (							_a					):
    snake_case_				:  Optional[Any]       =			int(_a					)
    if decimal in (0, 1):  # Exit cases for the recursion
        return str(_a					)
    snake_case_,			snake_case_				:  Optional[int]       =			divmod(_a	,    2					)
    return binary_recursive(_a					) + str(_a					)




def __lowercase    (							_a					):
    snake_case_				:  Dict       =			str(_a					).strip()
    if not number:
        raise ValueError('''No input value was provided'''					)
    snake_case_				:  List[str]       =			'''-''' if number.startswith('''-'''					) else ''''''
    snake_case_				:  Optional[Any]       =			number.lstrip('''-'''					)
    if not number.isnumeric():
        raise ValueError('''Input value is not an integer'''					)
    return f"{negative}0b{binary_recursive(int(_a					)					)}"


if __name__ == "__main__":
     from doctest import testmod

     testmod()





