

'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def  a__       (    _SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]=0.999	,				_SCREAMING_SNAKE_CASE   :       str="cosine"	,				)     ->					Optional[Any]:


	"""simple docstring"""





	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

	UpperCAmelCase_     :      Optional[Any]						=  []
	for i in range(__a				):
		UpperCAmelCase_     :      Dict						=  i / num_diffusion_timesteps
		UpperCAmelCase_     :      Tuple						=  (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 					_snake_case		(_lowercase			,			_lowercase):
			__A  :	List[str]        =[e.name for e in KarrasDiffusionSchedulers]
			__A  :	Union[str, Any]        =2






			@register_to_config
			def __init__(  self			,_snake_case = 10_00			,_snake_case = 0.00085			,_snake_case = 0.012			,_snake_case = "linear"			,_snake_case = None			,_snake_case = "epsilon"			,_snake_case = "linspace"			,_snake_case = 0			,):
				if trained_betas is not None:
					UpperCAmelCase_     :      Dict						=  torch.tensor(_snake_case			,dtype=torch.floataa     )
				elif beta_schedule == "linear":
					UpperCAmelCase_     :      Union[str, Any]						=  torch.linspace(_snake_case			,_snake_case			,_snake_case			,dtype=torch.floataa     )
				elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					UpperCAmelCase_     :      Dict						=  (
					    torch.linspace(beta_start**0.5			,beta_end**0.5			,_snake_case			,dtype=torch.floataa     ) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					UpperCAmelCase_     :      Tuple						=  betas_for_alpha_bar(_snake_case     )
				else:
					raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''     )

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

				#  set all values
				self.set_timesteps(_snake_case			,_snake_case			,_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=None     ):
				if schedule_timesteps is None:
					UpperCAmelCase_     :      Optional[int]						=  self.timesteps

				UpperCAmelCase_     :      Optional[int]						=  (schedule_timesteps == timestep).nonzero()

				# The sigma index that is taken for the **very** first `step`
				# is always the second index (or the last index if there is only 1)
				# This way we can ensure we don't accidentally skip a sigma in
				# case we start in the middle of the denoising schedule (e.g. for image-to-image)
				if len(self._index_counter     ) == 0:
					UpperCAmelCase_     :      Optional[Any]						=  1 if len(_snake_case     ) > 1 else 0
				else:
					UpperCAmelCase_     :      Optional[int]						=  timestep.cpu().item() if torch.is_tensor(_snake_case     ) else timestep
					UpperCAmelCase_     :      str						=  self._index_counter[timestep_int]

				return indices[pos].item()






			@property
			def 		UpperCamelCase__							(  self     ):
				# standard deviation of the initial noise distribution
				if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,):
				UpperCAmelCase_     :      Tuple						=  self.index_for_timestep(_snake_case     )

				if self.state_in_first_order:
					UpperCAmelCase_     :      str						=  self.sigmas[step_index]
				else:
					UpperCAmelCase_     :      str						=  self.sigmas_interpol[step_index]

				UpperCAmelCase_     :      Optional[int]						=  sample / ((sigma**2 + 1) ** 0.5)
				return sample






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None			,_snake_case = None			,):
				UpperCAmelCase_     :      Optional[int]						=  num_inference_steps

				UpperCAmelCase_     :      Any						=  num_train_timesteps or self.config.num_train_timesteps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
					UpperCAmelCase_     :      int						=  np.linspace(0			,num_train_timesteps - 1			,_snake_case			,dtype=_snake_case     )[::-1].copy()
				elif self.config.timestep_spacing == "leading":
					UpperCAmelCase_     :      List[Any]						=  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
					UpperCAmelCase_     :      Any						=  (np.arange(0			,_snake_case     ) * step_ratio).round()[::-1].copy().astype(_snake_case     )
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					UpperCAmelCase_     :      Any						=  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
					UpperCAmelCase_     :      str						=  (np.arange(_snake_case			,0			,-step_ratio     )).round().copy().astype(_snake_case     )
					timesteps -= 1
				else:
					raise ValueError(
					    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''     )

				UpperCAmelCase_     :      Optional[int]						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
				UpperCAmelCase_     :      str						=  torch.from_numpy(np.log(_snake_case     )     ).to(_snake_case     )

				UpperCAmelCase_     :      str						=  np.interp(_snake_case			,np.arange(0			,len(_snake_case     )     )			,_snake_case     )
				UpperCAmelCase_     :      List[Any]						=  np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
				UpperCAmelCase_     :      Optional[Any]						=  torch.from_numpy(_snake_case     ).to(device=_snake_case     )

				# interpolate sigmas
				UpperCAmelCase_     :      Any						=  sigmas.log().lerp(sigmas.roll(1     ).log()			,0.5     ).exp()

				UpperCAmelCase_     :      Dict						=  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2     ), sigmas[-1:]]     )
				UpperCAmelCase_     :      Any						=  torch.cat(
				    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2     ), sigmas_interpol[-1:]]     )

				if str(_snake_case     ).startswith("mps"     ):
					# mps does not support float64
					UpperCAmelCase_     :      int						=  torch.from_numpy(_snake_case     ).to(_snake_case			,dtype=torch.floataa     )
				else:
					UpperCAmelCase_     :      Tuple						=  torch.from_numpy(_snake_case     ).to(_snake_case     )

				# interpolate timesteps
				UpperCAmelCase_     :      str						=  self.sigma_to_t(_snake_case     ).to(_snake_case			,dtype=timesteps.dtype     )
				UpperCAmelCase_     :      Any						=  torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])			,dim=-1     ).flatten()

				UpperCAmelCase_     :      Optional[Any]						=  torch.cat([timesteps[:1], interleaved_timesteps]     )

				UpperCAmelCase_     :      Any						=  None

				# for exp beta schedules, such as the one for `pipeline_shap_e.py`
				# we need an index counter
				UpperCAmelCase_     :      List[Any]						=  defaultdict(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      List[Any]						=  sigma.log()

				# get distribution
				UpperCAmelCase_     :      Dict						=  log_sigma - self.log_sigmas[:, None]

				# get sigmas range
				UpperCAmelCase_     :      Optional[Any]						=  dists.ge(0     ).cumsum(dim=0     ).argmax(dim=0     ).clamp(max=self.log_sigmas.shape[0] - 2     )
				UpperCAmelCase_     :      List[Any]						=  low_idx + 1

				UpperCAmelCase_     :      int						=  self.log_sigmas[low_idx]
				UpperCAmelCase_     :      List[str]						=  self.log_sigmas[high_idx]

				# interpolate sigmas
				UpperCAmelCase_     :      List[str]						=  (low - log_sigma) / (low - high)
				UpperCAmelCase_     :      Optional[Any]						=  w.clamp(0			,1     )

				# transform interpolation to time range
				UpperCAmelCase_     :      List[Any]						=  (1 - w) * low_idx + w * high_idx
				UpperCAmelCase_     :      Any						=  t.view(sigma.shape     )
				return t






			@property
			def 		UpperCamelCase__							(  self     ):
				return self.sample is None






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case = True			,):
				UpperCAmelCase_     :      str						=  self.index_for_timestep(_snake_case     )

				# advance index counter by 1
				UpperCAmelCase_     :      List[str]						=  timestep.cpu().item() if torch.is_tensor(_snake_case     ) else timestep
				self._index_counter[timestep_int] += 1

				if self.state_in_first_order:
					UpperCAmelCase_     :      Dict						=  self.sigmas[step_index]
					UpperCAmelCase_     :      Any						=  self.sigmas_interpol[step_index + 1]
					UpperCAmelCase_     :      Optional[int]						=  self.sigmas[step_index + 1]
				else:
					# 2nd order / KDPM2's method
					UpperCAmelCase_     :      Optional[int]						=  self.sigmas[step_index - 1]
					UpperCAmelCase_     :      List[Any]						=  self.sigmas_interpol[step_index]
					UpperCAmelCase_     :      Optional[int]						=  self.sigmas[step_index]

				# currently only gamma=0 is supported. This usually works best anyways.
				# We can support gamma in the future but then need to scale the timestep before
				# passing it to the model which requires a change in API
				UpperCAmelCase_     :      List[Any]						=  0
				UpperCAmelCase_     :      Union[str, Any]						=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
					UpperCAmelCase_     :      Union[str, Any]						=  sigma_hat if self.state_in_first_order else sigma_interpol
					UpperCAmelCase_     :      List[Any]						=  sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
					UpperCAmelCase_     :      Dict						=  sigma_hat if self.state_in_first_order else sigma_interpol
					UpperCAmelCase_     :      Optional[Any]						=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
					    sample / (sigma_input**2 + 1)
					)
				elif self.config.prediction_type == "sample":
					raise NotImplementedError("prediction_type not implemented yet: sample"     )
				else:
					raise ValueError(
					    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

				if self.state_in_first_order:
					# 2. Convert to an ODE derivative for 1st order
					UpperCAmelCase_     :      Optional[int]						=  (sample - pred_original_sample) / sigma_hat
					# 3. delta timestep
					UpperCAmelCase_     :      Union[str, Any]						=  sigma_interpol - sigma_hat

					# store for 2nd order step
					UpperCAmelCase_     :      str						=  sample
				else:
					# DPM-Solver-2
					# 2. Convert to an ODE derivative for 2nd order
					UpperCAmelCase_     :      int						=  (sample - pred_original_sample) / sigma_interpol

					# 3. delta timestep
					UpperCAmelCase_     :      Optional[Any]						=  sigma_next - sigma_hat

					UpperCAmelCase_     :      int						=  self.sample
					UpperCAmelCase_     :      Dict						=  None

				UpperCAmelCase_     :      Any						=  sample + derivative * dt

				if not return_dict:
					return (prev_sample,)

				return SchedulerOutput(prev_sample=_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,):
				UpperCAmelCase_     :      Optional[int]						=  self.sigmas.to(device=original_samples.device			,dtype=original_samples.dtype     )
				if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case     ):
					# mps does not support float64
					UpperCAmelCase_     :      Tuple						=  self.timesteps.to(original_samples.device			,dtype=torch.floataa     )
					UpperCAmelCase_     :      int						=  timesteps.to(original_samples.device			,dtype=torch.floataa     )
				else:
					UpperCAmelCase_     :      Optional[int]						=  self.timesteps.to(original_samples.device     )
					UpperCAmelCase_     :      int						=  timesteps.to(original_samples.device     )

				UpperCAmelCase_     :      Dict						=  [self.index_for_timestep(_snake_case			,_snake_case     ) for t in timesteps]

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

				UpperCAmelCase_     :      Tuple						=  original_samples + noise * sigma
				return noisy_samples






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






"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_       :    Any						      =			{'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    str						      =			[
		    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''VanForImageClassification''',
		    '''VanModel''',
		    '''VanPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_van import (
				    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VanForImageClassification,
				    VanModel,
				    VanPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_       :    Optional[Any]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




