

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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any=0.999  ,      lowerCAmelCase:  List[Any]="cosine"  ,      )   ->       Any:
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				_UpperCAmelCase       :		Optional[Any] 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		List[Any] 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCAmelCase ) / alpha_bar_fn(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return torch.tensor(lowerCAmelCase  ,      dtype=torch.floataa )





class 				a  (	UpperCAmelCase							, UpperCAmelCase				):
						_lowercase    						=			[e.name for e in KarrasDiffusionSchedulers]
						_lowercase    						=			2


						@register_to_config
						def __init__(       self						,					A_ = 1000						,					A_ = 0.0_00_85						,					A_ = 0.0_12						,					A_ = "linear"						,					A_ = None						,					A_ = "epsilon"						,					A_ = False						,					A_ = False						,					A_ = 1.0						,					A_ = "linspace"						,					A_ = 0						,					):







										'''simple docstring'''


										if trained_betas is not None:
														_UpperCAmelCase       :		str 		=    torch.tensor(A_						,					dtype=torch.floataa	)
										elif beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    torch.linspace(A_						,					A_						,					A_						,					dtype=torch.floataa	)
										elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		str 		=    (
														    torch.linspace(beta_start**0.5						,					beta_end**0.5						,					A_						,					dtype=torch.floataa	) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		Dict 		=    betas_for_alpha_bar(A_						,					alpha_transform_type="cosine"	)
										elif beta_schedule == "exp":
														_UpperCAmelCase       :		Tuple 		=    betas_for_alpha_bar(A_						,					alpha_transform_type="exp"	)
										else:
														raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'	)

										_UpperCAmelCase       :		Union[str, Any] 		=    1.0 - self.betas
										_UpperCAmelCase       :		Dict 		=    torch.cumprod(self.alphas						,					dim=0	)

										#  set all values
										self.set_timesteps(A_						,					A_						,					A_	)
										_UpperCAmelCase       :		Optional[int] 		=    use_karras_sigmas


						def        _UpperCAmelCase				(       self						,					A_						,					A_=None	):







										'''simple docstring'''


										if schedule_timesteps is None:
														_UpperCAmelCase       :		Dict 		=    self.timesteps

										_UpperCAmelCase       :		Tuple 		=    (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(A_	) > 1 else 0
										else:
														_UpperCAmelCase       :		List[Any] 		=    timestep.cpu().item() if torch.is_tensor(A_	) else timestep
														_UpperCAmelCase       :		int 		=    self._index_counter[timestep_int]

										return indices[pos].item()


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

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


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    self.index_for_timestep(A_	)

										_UpperCAmelCase       :		str 		=    self.sigmas[step_index]
										_UpperCAmelCase       :		Tuple 		=    sample / ((sigma**2 + 1) ** 0.5)
										return sample


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    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       :		List[Any] 		=    np.linspace(0						,					num_train_timesteps - 1						,					A_						,					dtype=A_	)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
														_UpperCAmelCase       :		int 		=    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						,					A_	) * step_ratio).round()[::-1].copy().astype(A_	)
														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       :		Any 		=    (np.arange(A_						,					0						,					-step_ratio	)).round().copy().astype(A_	)
														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       :		Optional[Any] 		=    np.log(A_	)
										_UpperCAmelCase       :		Dict 		=    np.interp(A_						,					np.arange(0						,					len(A_	)	)						,					A_	)

										if self.config.use_karras_sigmas:
														_UpperCAmelCase       :		Union[str, Any] 		=    self._convert_to_karras(in_sigmas=A_						,					num_inference_steps=self.num_inference_steps	)
														_UpperCAmelCase       :		List[str] 		=    np.array([self._sigma_to_t(A_						,					A_	) for sigma in sigmas]	)

										_UpperCAmelCase       :		Tuple 		=    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
										_UpperCAmelCase       :		Dict 		=    torch.from_numpy(A_	).to(device=A_	)
										_UpperCAmelCase       :		str 		=    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2	), sigmas[-1:]]	)

										_UpperCAmelCase       :		Tuple 		=    torch.from_numpy(A_	)
										_UpperCAmelCase       :		int 		=    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2	)]	)

										if str(A_	).startswith("mps"	):
														# mps does not support float64
														_UpperCAmelCase       :		int 		=    timesteps.to(A_						,					dtype=torch.floataa	)
										else:
														_UpperCAmelCase       :		str 		=    timesteps.to(device=A_	)

										# empty dt and derivative
										_UpperCAmelCase       :		Optional[int] 		=    None
										_UpperCAmelCase       :		Optional[int] 		=    None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										_UpperCAmelCase       :		str 		=    defaultdict(A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    np.log(A_	)

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

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

										_UpperCAmelCase       :		Union[str, Any] 		=    log_sigmas[low_idx]
										_UpperCAmelCase       :		Union[str, Any] 		=    log_sigmas[high_idx]

										# interpolate sigmas
										_UpperCAmelCase       :		Dict 		=    (low - log_sigma) / (low - high)
										_UpperCAmelCase       :		List[str] 		=    np.clip(A_						,					0						,					1	)

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


						def        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		float 		=    in_sigmas[-1].item()
										_UpperCAmelCase       :		float 		=    in_sigmas[0].item()

										_UpperCAmelCase       :		str 		=    7.0  # 7.0 is the value used in the paper
										_UpperCAmelCase       :		Union[str, Any] 		=    np.linspace(0						,					1						,					A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    sigma_min ** (1 / rho)
										_UpperCAmelCase       :		Union[str, Any] 		=    sigma_max ** (1 / rho)
										_UpperCAmelCase       :		Dict 		=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self.dt is None


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_ = True						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.index_for_timestep(A_	)

										# advance index counter by 1
										_UpperCAmelCase       :		Tuple 		=    timestep.cpu().item() if torch.is_tensor(A_	) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
														_UpperCAmelCase       :		Optional[int] 		=    self.sigmas[step_index]
														_UpperCAmelCase       :		List[Any] 		=    self.sigmas[step_index + 1]
										else:
														# 2nd order / Heun's method
														_UpperCAmelCase       :		Dict 		=    self.sigmas[step_index - 1]
														_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       :		str 		=    0
										_UpperCAmelCase       :		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       :		int 		=    sigma_hat if self.state_in_first_order else sigma_next
														_UpperCAmelCase       :		Optional[Any] 		=    sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
														_UpperCAmelCase       :		Any 		=    sigma_hat if self.state_in_first_order else sigma_next
														_UpperCAmelCase       :		Optional[int] 		=    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
										elif self.config.prediction_type == "sample":
														_UpperCAmelCase       :		Union[str, Any] 		=    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:
														_UpperCAmelCase       :		Tuple 		=    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
														_UpperCAmelCase       :		Union[str, Any] 		=    (sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														_UpperCAmelCase       :		Optional[Any] 		=    sigma_next - sigma_hat

														# store for 2nd order step
														_UpperCAmelCase       :		Optional[Any] 		=    derivative
														_UpperCAmelCase       :		int 		=    dt
														_UpperCAmelCase       :		List[Any] 		=    sample
										else:
														# 2. 2nd order / Heun's method
														_UpperCAmelCase       :		str 		=    (sample - pred_original_sample) / sigma_next
														_UpperCAmelCase       :		Any 		=    (self.prev_derivative + derivative) / 2

														# 3. take prev timestep & sample
														_UpperCAmelCase       :		Union[str, Any] 		=    self.dt
														_UpperCAmelCase       :		Optional[int] 		=    self.sample

														# free dt and derivative
														# Note, this puts the scheduler in "first order mode"
														_UpperCAmelCase       :		Union[str, Any] 		=    None
														_UpperCAmelCase       :		int 		=    None
														_UpperCAmelCase       :		Union[str, Any] 		=    None

										_UpperCAmelCase       :		Tuple 		=    sample + derivative * dt

										if not return_dict:
														return (prev_sample,)

										return SchedulerOutput(prev_sample=A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					):







										'''simple docstring'''


										_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(A_	):
														# mps does not support float64
														_UpperCAmelCase       :		List[str] 		=    self.timesteps.to(original_samples.device						,					dtype=torch.floataa	)
														_UpperCAmelCase       :		List[Any] 		=    timesteps.to(original_samples.device						,					dtype=torch.floataa	)
										else:
														_UpperCAmelCase       :		Dict 		=    self.timesteps.to(original_samples.device	)
														_UpperCAmelCase       :		Optional[int] 		=    timesteps.to(original_samples.device	)

										_UpperCAmelCase       :		List[Any] 		=    [self.index_for_timestep(A_						,					A_	) for t in timesteps]

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

										_UpperCAmelCase       :		Union[str, Any] 		=    original_samples + noise * sigma
										return noisy_samples





						def __len__(       self	):







										'''simple docstring'''


										return self.config.num_train_timesteps





import warnings

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use PerceiverImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


