


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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=0.9_9_9   ,UpperCAmelCase__="cosine"   ,):

				"""simple docstring"""






				if alpha_transform_type == "cosine":

								def alpha_bar_fn(UpperCAmelCase__							):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2							) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(UpperCAmelCase__							):
												return math.exp(t * -1_2.0							)

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

				_SCREAMING_SNAKE_CASE      = []
				for i in range(UpperCamelCase__							):
								_SCREAMING_SNAKE_CASE      = i / num_diffusion_timesteps
								_SCREAMING_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    __lowercase  (       UpperCamelCase      ,     UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						[e.name for e in KarrasDiffusionSchedulers]
							SCREAMING_SNAKE_CASE   :					List[str]        =						2





							@register_to_config
							def __init__(    self				, A_ = 1000				, A_ = 0.0_0085				, A_ = 0.012				, A_ = "linear"				, A_ = None				, A_ = "epsilon"				, A_ = "linspace"				, A_ = 0				, )->	int:
											if trained_betas is not None:
															_SCREAMING_SNAKE_CASE      = torch.tensor(__lowerCamelCase				, dtype=torch.floataa	)
											elif beta_schedule == "linear":
															_SCREAMING_SNAKE_CASE      = torch.linspace(__lowerCamelCase				, __lowerCamelCase				, __lowerCamelCase				, dtype=torch.floataa	)
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															_SCREAMING_SNAKE_CASE      = (
															    torch.linspace(beta_start**0.5				, beta_end**0.5				, __lowerCamelCase				, dtype=torch.floataa	) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															_SCREAMING_SNAKE_CASE      = betas_for_alpha_bar(__lowerCamelCase	)
											else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

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

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





							def 		__magic_name__			(    self				, A_				, A_=None	)->	Union[str, Any]:
											if schedule_timesteps is None:
															_SCREAMING_SNAKE_CASE      = self.timesteps

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

											return indices[pos].item()





							@property
							def 		__magic_name__			(    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 		__magic_name__			(    self				, A_				, A_				, )->	torch.FloatTensor:
											_SCREAMING_SNAKE_CASE      = self.index_for_timestep(__lowerCamelCase	)

											if self.state_in_first_order:
															_SCREAMING_SNAKE_CASE      = self.sigmas[step_index]
											else:
															_SCREAMING_SNAKE_CASE      = self.sigmas_interpol[step_index - 1]

											_SCREAMING_SNAKE_CASE      = sample / ((sigma**2 + 1) ** 0.5)
											return sample





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = None				, )->	List[str]:
											_SCREAMING_SNAKE_CASE      = num_inference_steps

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

											_SCREAMING_SNAKE_CASE      = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
											_SCREAMING_SNAKE_CASE      = torch.from_numpy(np.log(__lowerCamelCase	)	).to(__lowerCamelCase	)

											_SCREAMING_SNAKE_CASE      = np.interp(__lowerCamelCase				, np.arange(0				, len(__lowerCamelCase	)	)				, __lowerCamelCase	)
											_SCREAMING_SNAKE_CASE      = np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
											_SCREAMING_SNAKE_CASE      = torch.from_numpy(__lowerCamelCase	).to(device=__lowerCamelCase	)

											# compute up and down sigmas
											_SCREAMING_SNAKE_CASE      = sigmas.roll(-1	)
											_SCREAMING_SNAKE_CASE      = 0.0
											_SCREAMING_SNAKE_CASE      = (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
											_SCREAMING_SNAKE_CASE      = (sigmas_next**2 - sigmas_up**2) ** 0.5
											_SCREAMING_SNAKE_CASE      = 0.0

											# compute interpolated sigmas
											_SCREAMING_SNAKE_CASE      = sigmas.log().lerp(sigmas_down.log()				, 0.5	).exp()
											_SCREAMING_SNAKE_CASE      = 0.0

											# set sigmas
											_SCREAMING_SNAKE_CASE      = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
											_SCREAMING_SNAKE_CASE      = torch.cat(
											    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
											_SCREAMING_SNAKE_CASE      = torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
											_SCREAMING_SNAKE_CASE      = torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

											if str(__lowerCamelCase	).startswith('mps'	):
															# mps does not support float64
															_SCREAMING_SNAKE_CASE      = torch.from_numpy(__lowerCamelCase	).to(__lowerCamelCase				, dtype=torch.floataa	)
											else:
															_SCREAMING_SNAKE_CASE      = torch.from_numpy(__lowerCamelCase	).to(__lowerCamelCase	)

											_SCREAMING_SNAKE_CASE      = self.sigma_to_t(__lowerCamelCase	).to(__lowerCamelCase				, dtype=timesteps.dtype	)
											_SCREAMING_SNAKE_CASE      = torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])				, dim=-1	).flatten()

											_SCREAMING_SNAKE_CASE      = torch.cat([timesteps[:1], interleaved_timesteps]	)

											_SCREAMING_SNAKE_CASE      = None

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





							def 		__magic_name__			(    self				, A_	)->	Tuple:
											# get log sigma
											_SCREAMING_SNAKE_CASE      = sigma.log()

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

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

											_SCREAMING_SNAKE_CASE      = self.log_sigmas[low_idx]
											_SCREAMING_SNAKE_CASE      = self.log_sigmas[high_idx]

											# interpolate sigmas
											_SCREAMING_SNAKE_CASE      = (low - log_sigma) / (low - high)
											_SCREAMING_SNAKE_CASE      = w.clamp(0				, 1	)

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





							@property
							def 		__magic_name__			(    self	)->	str:
											return self.sample is None





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_ = None				, A_ = True				, )->	Union[SchedulerOutput, Tuple]:
											_SCREAMING_SNAKE_CASE      = self.index_for_timestep(__lowerCamelCase	)

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

											if self.state_in_first_order:
															_SCREAMING_SNAKE_CASE      = self.sigmas[step_index]
															_SCREAMING_SNAKE_CASE      = self.sigmas_interpol[step_index]
															_SCREAMING_SNAKE_CASE      = self.sigmas_up[step_index]
															_SCREAMING_SNAKE_CASE      = self.sigmas_down[step_index - 1]
											else:
															# 2nd order / KPDM2's method
															_SCREAMING_SNAKE_CASE      = self.sigmas[step_index - 1]
															_SCREAMING_SNAKE_CASE      = self.sigmas_interpol[step_index - 1]
															_SCREAMING_SNAKE_CASE      = self.sigmas_up[step_index - 1]
															_SCREAMING_SNAKE_CASE      = self.sigmas_down[step_index - 1]

											# 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
											_SCREAMING_SNAKE_CASE      = 0
											_SCREAMING_SNAKE_CASE      = sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

											_SCREAMING_SNAKE_CASE      = model_output.device
											_SCREAMING_SNAKE_CASE      = randn_tensor(model_output.shape				, dtype=model_output.dtype				, device=__lowerCamelCase				, generator=__lowerCamelCase	)

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
															_SCREAMING_SNAKE_CASE      = sigma_hat if self.state_in_first_order else sigma_interpol
															_SCREAMING_SNAKE_CASE      = sample - sigma_input * model_output
											elif self.config.prediction_type == "v_prediction":
															_SCREAMING_SNAKE_CASE      = sigma_hat if self.state_in_first_order else sigma_interpol
															_SCREAMING_SNAKE_CASE      = 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
															_SCREAMING_SNAKE_CASE      = (sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															_SCREAMING_SNAKE_CASE      = sigma_interpol - sigma_hat

															# store for 2nd order step
															_SCREAMING_SNAKE_CASE      = sample
															_SCREAMING_SNAKE_CASE      = dt
															_SCREAMING_SNAKE_CASE      = sample + derivative * dt
											else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															_SCREAMING_SNAKE_CASE      = (sample - pred_original_sample) / sigma_interpol
															# 3. delta timestep
															_SCREAMING_SNAKE_CASE      = sigma_down - sigma_hat

															_SCREAMING_SNAKE_CASE      = self.sample
															_SCREAMING_SNAKE_CASE      = None

															_SCREAMING_SNAKE_CASE      = sample + derivative * dt
															_SCREAMING_SNAKE_CASE      = prev_sample + noise * sigma_up

											if not return_dict:
															return (prev_sample,)

											return SchedulerOutput(prev_sample=__lowerCamelCase	)





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

											_SCREAMING_SNAKE_CASE      = [self.index_for_timestep(__lowerCamelCase				, __lowerCamelCase	) for t in timesteps]

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

											_SCREAMING_SNAKE_CASE      = original_samples + noise * sigma
											return noisy_samples





							def __len__(    self	)->	Union[str, Any]:
											return self.config.num_train_timesteps



from typing import TYPE_CHECKING

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


snake_case			:      Optional[int] 							=   {'configuration_vit_hybrid': ['VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTHybridConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case			:      int 							=   [
							    'VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ViTHybridForImageClassification',
							    'ViTHybridModel',
							    'ViTHybridPreTrainedModel',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case			:      Any 							=   ['ViTHybridImageProcessor']


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

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