





'''simple docstring'''






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  _A (					__lowercase		):
		lowercase__:   torch.FloatTensor
		lowercase__:   Optional[torch.FloatTensor]				     =   None







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase=0.9_99    ,	_lowerCamelCase="cosine"    ,	) ->				int:



			"""simple docstring"""


			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			__snake_case					:      Dict							      =     []
			for i in range(_lowerCamelCase ):
						__snake_case					:      List[Any]							      =     i / num_diffusion_timesteps
						__snake_case					:      str							      =     (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 (					__lowercase						,					__lowercase		):

		@register_to_config
		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   int = 10_00  ,      __magic_name__					:   str = "fixed_small_log"  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[float] = 1.0  ,      __magic_name__					:   str = "epsilon"  ,      __magic_name__					:   str = "squaredcos_cap_v2"  ,      )					->    List[Any]:


					"""simple docstring"""

					if beta_schedule != "squaredcos_cap_v2":
								raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""			)

					__snake_case					:      Optional[int]							      =     betas_for_alpha_bar(__magic_name__			)

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

					# standard deviation of the initial noise distribution
					__snake_case					:      int							      =     1.0

					# setable values
					__snake_case					:      Union[str, Any]							      =     None
					__snake_case					:      Any							      =     torch.from_numpy(np.arange(0  ,      __magic_name__			)[::-1].copy()			)

					__snake_case					:      Optional[Any]							      =     variance_type

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   Optional[int] = None			)					->    torch.FloatTensor:


					"""simple docstring"""

					return sample

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   Union[str, torch.device] = None			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     num_inference_steps
					__snake_case					:      Optional[int]							      =     (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
					__snake_case					:      int							      =     (np.arange(0  ,      __magic_name__			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
					__snake_case					:      str							      =     torch.from_numpy(__magic_name__			).to(__magic_name__			)

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   str=None  ,      __magic_name__					:   int=None  ,      __magic_name__					:   List[Any]=None			)					->    List[Any]:


					"""simple docstring"""

					if prev_timestep is None:
								__snake_case					:      Union[str, Any]							      =     t - 1

					__snake_case					:      Optional[int]							      =     self.alphas_cumprod[t]
					__snake_case					:      Optional[Any]							      =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					__snake_case					:      Union[str, Any]							      =     1 - alpha_prod_t
					__snake_case					:      int							      =     1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
								__snake_case					:      Union[str, Any]							      =     self.betas[t]
					else:
								__snake_case					:      Tuple							      =     1 - alpha_prod_t / alpha_prod_t_prev

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					__snake_case					:      Union[str, Any]							      =     beta_prod_t_prev / beta_prod_t * beta

					if variance_type is None:
								__snake_case					:      Optional[int]							      =     self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small_log":
								__snake_case					:      int							      =     torch.log(torch.clamp(__magic_name__  ,      min=1E-20			)			)
								__snake_case					:      List[Any]							      =     torch.exp(0.5 * variance			)
					elif variance_type == "learned_range":
								# NOTE difference with DDPM scheduler
								__snake_case					:      Tuple							      =     variance.log()
								__snake_case					:      str							      =     beta.log()

								__snake_case					:      int							      =     (predicted_variance + 1) / 2
								__snake_case					:      Tuple							      =     frac * max_log + (1 - frac) * min_log

					return variance

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   int  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   bool = True  ,      )					->    Union[UnCLIPSchedulerOutput, Tuple]:


					"""simple docstring"""

					__snake_case					:      Any							      =     timestep

					if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
								__snake_case      ,     __snake_case					:      Optional[Any]							      =     torch.split(__magic_name__  ,      sample.shape[1]  ,      dim=1			)
					else:
								__snake_case					:      Tuple							      =     None

					# 1. compute alphas, betas
					if prev_timestep is None:
								__snake_case					:      Tuple							      =     t - 1

					__snake_case					:      Union[str, Any]							      =     self.alphas_cumprod[t]
					__snake_case					:      List[Any]							      =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					__snake_case					:      Optional[int]							      =     1 - alpha_prod_t
					__snake_case					:      Tuple							      =     1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
								__snake_case					:      List[Any]							      =     self.betas[t]
								__snake_case					:      List[Any]							      =     self.alphas[t]
					else:
								__snake_case					:      int							      =     1 - alpha_prod_t / alpha_prod_t_prev
								__snake_case					:      Optional[Any]							      =     1 - beta

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
								__snake_case					:      List[str]							      =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
								__snake_case					:      Optional[Any]							      =     model_output
					else:
								raise ValueError(
								    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
								    """ for the UnCLIPScheduler."""			)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
								__snake_case					:      Tuple							      =     torch.clamp(
								    __magic_name__  ,      -self.config.clip_sample_range  ,      self.config.clip_sample_range			)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					__snake_case					:      Union[str, Any]							      =     (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
					__snake_case					:      List[Any]							      =     alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					__snake_case					:      List[Any]							      =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					__snake_case					:      List[str]							      =     0
					if t > 0:
								__snake_case					:      Dict							      =     randn_tensor(
								    model_output.shape  ,      dtype=model_output.dtype  ,      generator=__magic_name__  ,      device=model_output.device			)

								__snake_case					:      Union[str, Any]							      =     self._get_variance(
								    __magic_name__  ,      predicted_variance=__magic_name__  ,      prev_timestep=__magic_name__  ,      )

								if self.variance_type == "fixed_small_log":
											__snake_case					:      Tuple							      =     variance
								elif self.variance_type == "learned_range":
											__snake_case					:      int							      =     (0.5 * variance).exp()
								else:
											raise ValueError(
											    f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
											    """ for the UnCLIPScheduler."""			)

								__snake_case					:      List[Any]							      =     variance * variance_noise

					__snake_case					:      int							      =     pred_prev_sample + variance

					if not return_dict:
								return (pred_prev_sample,)

					return UnCLIPSchedulerOutput(prev_sample=__magic_name__  ,      pred_original_sample=__magic_name__			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   torch.IntTensor  ,      )					->    torch.FloatTensor:


					"""simple docstring"""

					__snake_case					:      str							      =     self.alphas_cumprod.to(device=original_samples.device  ,      dtype=original_samples.dtype			)
					__snake_case					:      Tuple							      =     timesteps.to(original_samples.device			)

					__snake_case					:      List[str]							      =     alphas_cumprod[timesteps] ** 0.5
					__snake_case					:      Any							      =     sqrt_alpha_prod.flatten()
					while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
								__snake_case					:      Union[str, Any]							      =     sqrt_alpha_prod.unsqueeze(-1			)

					__snake_case					:      List[str]							      =     (1 - alphas_cumprod[timesteps]) ** 0.5
					__snake_case					:      str							      =     sqrt_one_minus_alpha_prod.flatten()
					while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
								__snake_case					:      Optional[Any]							      =     sqrt_one_minus_alpha_prod.unsqueeze(-1			)

					__snake_case					:      List[str]							      =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples









'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):

		def __init__(   self					:   int  ,      *__magic_name__					:   Optional[Any]  ,      **__magic_name__					:   Any			)					->    None:


					"""simple docstring"""

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


