




"""simple docstring"""

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

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 	lowerCamelCase__			(     __lowercase	):
			a__       :					torch.FloatTensor
			a__       :					Optional[torch.FloatTensor]	    =   None







def      UpperCamelCase__   (						lowercase__     :   Any				,   lowercase__     :   List[Any]=0.9_99				,   lowercase__     :   Union[str, Any]="cosine"				,   ):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowercase__     :   Optional[int]      ):
						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

		elif alpha_transform_type == "exp":

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

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

		snake_case						:  int    	= []
		for i in range(lowercase__      ):
				snake_case						:  str    	= i / num_diffusion_timesteps
				snake_case						:  List[str]    	= (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(lowercase__      ) / alpha_bar_fn(lowercase__      )				,   lowercase__      )      )
		return torch.tensor(lowercase__				,   dtype=torch.floataa      )
class 	lowerCamelCase__			(     __lowercase ,     __lowercase	):
			a__       :					Union[str, Any]	    =   1


			@register_to_config
			def __init__(   self ,       SCREAMING_SNAKE_CASE = 1_000 ,       SCREAMING_SNAKE_CASE = 0.00_01 ,       SCREAMING_SNAKE_CASE = 0.02 ,       SCREAMING_SNAKE_CASE = "linear" ,       SCREAMING_SNAKE_CASE = None ,       SCREAMING_SNAKE_CASE = True ,       SCREAMING_SNAKE_CASE = True ,       SCREAMING_SNAKE_CASE = 0 ,       SCREAMING_SNAKE_CASE = "epsilon" ,       SCREAMING_SNAKE_CASE = 1.0 ,       **SCREAMING_SNAKE_CASE ,       ):



					"""simple docstring"""







					if kwargs.get("set_alpha_to_one" ,       UpperCAmelCase__							) is not None:
							snake_case						:  List[str]    	= (
							    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
							)
							deprecate("set_alpha_to_one" ,       "1.0.0" ,       UpperCAmelCase__ ,       standard_warn=UpperCAmelCase__							)
							snake_case						:  Dict    	= kwargs["set_alpha_to_one"]
					if trained_betas is not None:
							snake_case						:  str    	= torch.tensor(UpperCAmelCase__ ,       dtype=torch.floataa							)
					elif beta_schedule == "linear":
							snake_case						:  Optional[int]    	= 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						:  Dict    	= (
							    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						:  Any    	= betas_for_alpha_bar(UpperCAmelCase__							)
					else:
							raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''							)

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

					# At every step in inverted ddim, we are looking into the next alphas_cumprod
					# For the final step, there is no next alphas_cumprod, and the index is out of bounds
					# `set_alpha_to_zero` decides whether we set this parameter simply to zero
					# in this case, self.step() just output the predicted noise
					# or whether we use the final alpha of the "non-previous" one.
					snake_case						:  Dict    	= torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

					# standard deviation of the initial noise distribution
					snake_case						:  Optional[int]    	= 1.0

					# setable values
					snake_case						:  Optional[Any]    	= None
					snake_case						:  Optional[Any]    	= torch.from_numpy(np.arange(0 ,       UpperCAmelCase__							).copy().astype(np.intaa							)							)


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					return sample


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					if num_inference_steps > self.config.num_train_timesteps:
							raise ValueError(
							    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
							    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
							    F''' maximal {self.config.num_train_timesteps} timesteps.'''							)

					snake_case						:  str    	= num_inference_steps
					snake_case						:  Any    	= self.config.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						:  Dict    	= (np.arange(0 ,       UpperCAmelCase__							) * step_ratio).round().copy().astype(np.intaa							)
					snake_case						:  List[str]    	= torch.from_numpy(UpperCAmelCase__							).to(UpperCAmelCase__							)
					self.timesteps += self.config.steps_offset


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE = 0.0 ,       SCREAMING_SNAKE_CASE = False ,       SCREAMING_SNAKE_CASE = None ,       SCREAMING_SNAKE_CASE = True ,       ):



					"""simple docstring"""







					snake_case						:  int    	= timestep + self.config.num_train_timesteps // self.num_inference_steps

					# 2. compute alphas, betas
					# change original implementation to exactly match noise levels for analogous forward process
					snake_case						:  Dict    	= self.alphas_cumprod[timestep]
					snake_case						:  Dict    	= (
					    self.alphas_cumprod[prev_timestep]
					    if prev_timestep < self.config.num_train_timesteps
					    else self.final_alpha_cumprod
					)

					snake_case						:  Tuple    	= 1 - alpha_prod_t

					# 3. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					if self.config.prediction_type == "epsilon":
							snake_case						:  Optional[Any]    	= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							snake_case						:  Tuple    	= model_output
					elif self.config.prediction_type == "sample":
							snake_case						:  str    	= model_output
							snake_case						:  Optional[Any]    	= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
					elif self.config.prediction_type == "v_prediction":
							snake_case						:  int    	= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							snake_case						:  Any    	= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
					else:
							raise ValueError(
							    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
							    " `v_prediction`"							)

					# 4. Clip or threshold "predicted x_0"
					if self.config.clip_sample:
							snake_case						:  Dict    	= pred_original_sample.clamp(
							    -self.config.clip_sample_range ,       self.config.clip_sample_range							)

					# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					snake_case						:  int    	= (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

					# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					snake_case						:  Optional[int]    	= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

					if not return_dict:
							return (prev_sample, pred_original_sample)
					return DDIMSchedulerOutput(prev_sample=UpperCAmelCase__ ,       pred_original_sample=UpperCAmelCase__							)



			def __len__(   self							):



					"""simple docstring"""







					return self.config.num_train_timesteps





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


__snake_case    						=logging.get_logger(__name__)

class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Dict    ,      *UpperCAmelCase__       :      Dict    ,      **UpperCAmelCase__       :      List[str]    )				->  None:
        warnings.warn(
            'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use FlavaImageProcessor instead.'    ,      UpperCAmelCase__    ,      )
        super().__init__(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





