





'''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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=0.999					,				UpperCAmelCase_="cosine"					,				)   ->Any:
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

	snake_case__	       =   []
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   i / num_diffusion_timesteps
		snake_case__	       =   (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 				__snake_case     (					__magic_name__       ,       __magic_name__							):
	__lowerCAmelCase       						=  [e.name for e in KarrasDiffusionSchedulers]
	__lowerCAmelCase       						=  2





	@register_to_config
	def __init__(		self      ,       UpperCamelCase_ = 1000      ,       UpperCamelCase_ = 0.0_0_0_8_5      ,       UpperCamelCase_ = 0.0_1_2      ,       UpperCamelCase_ = "linear"      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "epsilon"      ,       UpperCamelCase_ = False      ,       UpperCamelCase_ = False      ,       UpperCamelCase_ = 1.0      ,       UpperCamelCase_ = "linspace"      ,       UpperCamelCase_ = 0      ,       )      ->		Any:
		if trained_betas is not None:
			snake_case__	       =   torch.tensor(UpperCamelCase_      ,       dtype=torch.floataa		)
		elif beta_schedule == "linear":
			snake_case__	       =   torch.linspace(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       dtype=torch.floataa		)
		elif beta_schedule == "scaled_linear":
			# this schedule is very specific to the latent diffusion model.
			snake_case__	       =   (
			    torch.linspace(beta_start**0.5      ,       beta_end**0.5      ,       UpperCamelCase_      ,       dtype=torch.floataa		) ** 2
			)
		elif beta_schedule == "squaredcos_cap_v2":
			# Glide cosine schedule
			snake_case__	       =   betas_for_alpha_bar(UpperCamelCase_      ,       alpha_transform_type='cosine'		)
		elif beta_schedule == "exp":
			snake_case__	       =   betas_for_alpha_bar(UpperCamelCase_      ,       alpha_transform_type='exp'		)
		else:
			raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

		snake_case__	       =   1.0 - self.betas
		snake_case__	       =   torch.cumprod(self.alphas      ,       dim=0		)

		#  set all values
		self.set_timesteps(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)
		snake_case__	       =   use_karras_sigmas





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		str:
		if schedule_timesteps is None:
			snake_case__	       =   self.timesteps

		snake_case__	       =   (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:
			snake_case__	       =   1 if len(UpperCamelCase_		) > 1 else 0
		else:
			snake_case__	       =   timestep.cpu().item() if torch.is_tensor(UpperCamelCase_		) else timestep
			snake_case__	       =   self._index_counter[timestep_int]

		return indices[pos].item()





	@property
	def   _snake_case   (		self		)      ->		int:
		# 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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		torch.FloatTensor:
		snake_case__	       =   self.index_for_timestep(UpperCamelCase_		)

		snake_case__	       =   self.sigmas[step_index]
		snake_case__	       =   sample / ((sigma**2 + 1) ** 0.5)
		return sample





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       )      ->		List[Any]:
		snake_case__	       =   num_inference_steps

		snake_case__	       =   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":
			snake_case__	       =   np.linspace(0      ,       num_train_timesteps - 1      ,       UpperCamelCase_      ,       dtype=UpperCamelCase_		)[::-1].copy()
		elif self.config.timestep_spacing == "leading":
			snake_case__	       =   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__	       =   (np.arange(0      ,       UpperCamelCase_		) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_		)
			timesteps += self.config.steps_offset
		elif self.config.timestep_spacing == "trailing":
			snake_case__	       =   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__	       =   (np.arange(UpperCamelCase_      ,       0      ,       -step_ratio		)).round().copy().astype(UpperCamelCase_		)
			timesteps -= 1
		else:
			raise ValueError(
			    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

		snake_case__	       =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
		snake_case__	       =   np.log(UpperCamelCase_		)
		snake_case__	       =   np.interp(UpperCamelCase_      ,       np.arange(0      ,       len(UpperCamelCase_		)		)      ,       UpperCamelCase_		)

		if self.config.use_karras_sigmas:
			snake_case__	       =   self._convert_to_karras(in_sigmas=UpperCamelCase_      ,       num_inference_steps=self.num_inference_steps		)
			snake_case__	       =   np.array([self._sigma_to_t(UpperCamelCase_      ,       UpperCamelCase_		) for sigma in sigmas]		)

		snake_case__	       =   np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
		snake_case__	       =   torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_		)
		snake_case__	       =   torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

		snake_case__	       =   torch.from_numpy(UpperCamelCase_		)
		snake_case__	       =   torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

		if str(UpperCamelCase_		).startswith('mps'		):
			# mps does not support float64
			snake_case__	       =   timesteps.to(UpperCamelCase_      ,       dtype=torch.floataa		)
		else:
			snake_case__	       =   timesteps.to(device=UpperCamelCase_		)

		# empty dt and derivative
		snake_case__	       =   None
		snake_case__	       =   None

		# for exp beta schedules, such as the one for `pipeline_shap_e.py`
		# we need an index counter
		snake_case__	       =   defaultdict(UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Dict:
		# get log sigma
		snake_case__	       =   np.log(UpperCamelCase_		)

		# get distribution
		snake_case__	       =   log_sigma - log_sigmas[:, np.newaxis]

		# get sigmas range
		snake_case__	       =   np.cumsum((dists >= 0)      ,       axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
		snake_case__	       =   low_idx + 1

		snake_case__	       =   log_sigmas[low_idx]
		snake_case__	       =   log_sigmas[high_idx]

		# interpolate sigmas
		snake_case__	       =   (low - log_sigma) / (low - high)
		snake_case__	       =   np.clip(UpperCamelCase_      ,       0      ,       1		)

		# transform interpolation to time range
		snake_case__	       =   (1 - w) * low_idx + w * high_idx
		snake_case__	       =   t.reshape(sigma.shape		)
		return t





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		torch.FloatTensor:
		snake_case__	       =   in_sigmas[-1].item()
		snake_case__	       =   in_sigmas[0].item()

		snake_case__	       =   7.0  # 7.0 is the value used in the paper
		snake_case__	       =   np.linspace(0      ,       1      ,       UpperCamelCase_		)
		snake_case__	       =   sigma_min ** (1 / rho)
		snake_case__	       =   sigma_max ** (1 / rho)
		snake_case__	       =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
		return sigmas





	@property
	def   _snake_case   (		self		)      ->		Any:
		return self.dt is None





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = True      ,       )      ->		Union[SchedulerOutput, Tuple]:
		snake_case__	       =   self.index_for_timestep(UpperCamelCase_		)

		# advance index counter by 1
		snake_case__	       =   timestep.cpu().item() if torch.is_tensor(UpperCamelCase_		) else timestep
		self._index_counter[timestep_int] += 1

		if self.state_in_first_order:
			snake_case__	       =   self.sigmas[step_index]
			snake_case__	       =   self.sigmas[step_index + 1]
		else:
			# 2nd order / Heun's method
			snake_case__	       =   self.sigmas[step_index - 1]
			snake_case__	       =   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
		snake_case__	       =   0
		snake_case__	       =   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":
			snake_case__	       =   sigma_hat if self.state_in_first_order else sigma_next
			snake_case__	       =   sample - sigma_input * model_output
		elif self.config.prediction_type == "v_prediction":
			snake_case__	       =   sigma_hat if self.state_in_first_order else sigma_next
			snake_case__	       =   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
			    sample / (sigma_input**2 + 1)
			)
		elif self.config.prediction_type == "sample":
			snake_case__	       =   model_output
		else:
			raise ValueError(
			    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''		)

		if self.config.clip_sample:
			snake_case__	       =   pred_original_sample.clamp(
			    -self.config.clip_sample_range      ,       self.config.clip_sample_range		)

		if self.state_in_first_order:
			# 2. Convert to an ODE derivative for 1st order
			snake_case__	       =   (sample - pred_original_sample) / sigma_hat
			# 3. delta timestep
			snake_case__	       =   sigma_next - sigma_hat

			# store for 2nd order step
			snake_case__	       =   derivative
			snake_case__	       =   dt
			snake_case__	       =   sample
		else:
			# 2. 2nd order / Heun's method
			snake_case__	       =   (sample - pred_original_sample) / sigma_next
			snake_case__	       =   (self.prev_derivative + derivative) / 2

			# 3. take prev timestep & sample
			snake_case__	       =   self.dt
			snake_case__	       =   self.sample

			# free dt and derivative
			# Note, this puts the scheduler in "first order mode"
			snake_case__	       =   None
			snake_case__	       =   None
			snake_case__	       =   None

		snake_case__	       =   sample + derivative * dt

		if not return_dict:
			return (prev_sample,)

		return SchedulerOutput(prev_sample=UpperCamelCase_		)





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

		snake_case__	       =   [self.index_for_timestep(UpperCamelCase_      ,       UpperCamelCase_		) for t in timesteps]

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

		snake_case__	       =   original_samples + noise * sigma
		return noisy_samples





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










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



