


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
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 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					torch.FloatTensor
      snake_case__    :					Optional[torch.FloatTensor]					       =						None







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      str=0.999  ,   __A			:      Dict="cosine"  ,   )     ->		Optional[int]:

       """simple docstring"""

       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(__A			:      List[str]    ):
                     return math.exp(t * -12.0    )

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

       a_ :		str							=  []
       for i in range(__A    ):
              a_ :		List[str]							=  i / num_diffusion_timesteps
              a_ :		Any							=  (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(__A    ) / alpha_bar_fn(__A    )  ,   __A    )    )
       return torch.tensor(__A  ,   dtype=torch.floataa    )
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					str					       =						1
      @register_to_config
      def __init__(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	int = 1_0_0_0					,							SCREAMING_SNAKE_CASE__    :	float = 0.0001					,							SCREAMING_SNAKE_CASE__    :	float = 0.02					,							SCREAMING_SNAKE_CASE__    :	str = "linear"					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[np.ndarray, List[float]]] = None					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	int = 0					,							SCREAMING_SNAKE_CASE__    :	str = "epsilon"					,							SCREAMING_SNAKE_CASE__    :	float = 1.0					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					,							)		->      List[str]:
             if kwargs.get('set_alpha_to_one'					,							SCREAMING_SNAKE_CASE__					) is not None:
                    a_ :		Tuple							=  (
                        'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
                    )
                    deprecate('set_alpha_to_one'					,							'1.0.0'					,							SCREAMING_SNAKE_CASE__					,							standard_warn=SCREAMING_SNAKE_CASE__					)
                    a_ :		Optional[Any]							=  kwargs['set_alpha_to_one']
             if trained_betas is not None:
                    a_ :		int							=  torch.tensor(SCREAMING_SNAKE_CASE__					,							dtype=torch.floataa					)
             elif beta_schedule == "linear":
                    a_ :		Tuple							=  torch.linspace(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							dtype=torch.floataa					)
             elif beta_schedule == "scaled_linear":
                    # this schedule is very specific to the latent diffusion model.
                    a_ :		Any							=  (
                        torch.linspace(beta_start**0.5					,							beta_end**0.5					,							SCREAMING_SNAKE_CASE__					,							dtype=torch.floataa					) ** 2
                    )
             elif beta_schedule == "squaredcos_cap_v2":
                    # Glide cosine schedule
                    a_ :		Any							=  betas_for_alpha_bar(SCREAMING_SNAKE_CASE__					)
             else:
                    raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""					)

             a_ :		Dict							=  1.0 - self.betas
             a_ :		Tuple							=  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.
             a_ :		List[str]							=  torch.tensor(0.0					) if set_alpha_to_zero else self.alphas_cumprod[-1]

             # standard deviation of the initial noise distribution
             a_ :		Dict							=  1.0

             # setable values
             a_ :		int							=  None
             a_ :		List[Any]							=  torch.from_numpy(np.arange(0					,							SCREAMING_SNAKE_CASE__					).copy().astype(np.intaa					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	torch.FloatTensor					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					)		->      torch.FloatTensor:
             return sample
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      Optional[Any]:

             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."""					)

             a_ :		List[str]							=  num_inference_steps
             a_ :		List[str]							=  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
             a_ :		Any							=  (np.arange(0					,							SCREAMING_SNAKE_CASE__					) * step_ratio).round().copy().astype(np.intaa					)
             a_ :		Optional[Any]							=  torch.from_numpy(SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
             self.timesteps += self.config.steps_offset
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	torch.FloatTensor					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	torch.FloatTensor					,							SCREAMING_SNAKE_CASE__    :	float = 0.0					,							SCREAMING_SNAKE_CASE__    :	bool = False					,							SCREAMING_SNAKE_CASE__    :	Optional[torch.FloatTensor] = None					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							)		->      Union[DDIMSchedulerOutput, Tuple]:
             # 1. get previous step value (=t+1)
             a_ :		Union[str, Any]							=  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
             a_ :		Any							=  self.alphas_cumprod[timestep]
             a_ :		str							=  (
                 self.alphas_cumprod[prev_timestep]
                 if prev_timestep < self.config.num_train_timesteps
                 else self.final_alpha_cumprod
             )

             a_ :		str							=  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":
                    a_ :		Optional[int]							=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                    a_ :		Tuple							=  model_output
             elif self.config.prediction_type == "sample":
                    a_ :		Dict							=  model_output
                    a_ :		str							=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
             elif self.config.prediction_type == "v_prediction":
                    a_ :		str							=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                    a_ :		Tuple							=  (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:
                    a_ :		Optional[int]							=  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
             a_ :		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
             a_ :		Optional[Any]							=  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=SCREAMING_SNAKE_CASE__					,							pred_original_sample=SCREAMING_SNAKE_CASE__					)

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_				:			Dict   =	{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			int   =	[
       'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMSNModel',
       'ViTMSNForImageClassification',
       'ViTMSNPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_msn import (
          VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMSNForImageClassification,
          ViTMSNModel,
          ViTMSNPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase_				:			Dict   =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


