


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
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    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 42
				lowerCAmelCase 		= None





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Dict=0.9_99							,					UpperCamelCase__:     str="cosine"							,					) ->   Tuple:





	"""simple docstring"""







	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase__:     str						):
			return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCamelCase__:     Union[str, Any]						):
			return math.exp(t * -12.0						)

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

	A								=				[]
	for i in range(UpperCamelCase__						):
		A								=				i / num_diffusion_timesteps
		A								=				(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCamelCase__						) / alpha_bar_fn(UpperCamelCase__						)							,					UpperCamelCase__						)						)
	return torch.tensor(UpperCamelCase__							,					dtype=torch.floataa						)






class       _UpperCamelCase    (					__snake_case    ,		__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 1

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

					A								=				1.0 - self.betas
					A								=				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.
					A								=				torch.tensor(0.0   ) if set_alpha_to_zero else self.alphas_cumprod[-1]

					# standard deviation of the initial noise distribution
					A								=				1.0

					# setable values
					A								=				None
					A								=				torch.from_numpy(np.arange(0				,   a__   ).copy().astype(np.intaa   )   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				torch.FloatTensor:
					return sample

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Dict:

					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								=				num_inference_steps
					A								=				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
					A								=				(np.arange(0				,   a__   ) * step_ratio).round().copy().astype(np.intaa   )
					A								=				torch.from_numpy(a__   ).to(a__   )
					self.timesteps += self.config.steps_offset

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__ = 0.0				,   a__ = False				,   a__ = None				,   a__ = True				,   )	->				Union[DDIMSchedulerOutput, Tuple]:
					# 1. get previous step value (=t+1)
					A								=				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
					A								=				self.alphas_cumprod[timestep]
					A								=				(
					    self.alphas_cumprod[prev_timestep]
					    if prev_timestep < self.config.num_train_timesteps
					    else self.final_alpha_cumprod
					)

					A								=				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":
						A								=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						A								=				model_output
					elif self.config.prediction_type == "sample":
						A								=				model_output
						A								=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
					elif self.config.prediction_type == "v_prediction":
						A								=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
						A								=				(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:
						A								=				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
					A								=				(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
					A								=				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=a__				,   pred_original_sample=a__   )

				def __len__(						self   )	->				List[Any]:
					return self.config.num_train_timesteps







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



