





'''simple docstring'''


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 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       List[Any]=0.999		,							SCREAMING_SNAKE_CASE_	:       List[str]="cosine"		,							):



    '''simple docstring'''





    if alpha_transform_type == "cosine":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE_	:       List[Any]     ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

    elif alpha_transform_type == "exp":

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

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

    _lowerCAmelCase						      =   []
    for i in range(SCREAMING_SNAKE_CASE_     ):
        _lowerCAmelCase						      =   i / num_diffusion_timesteps
        _lowerCAmelCase						      =   (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_     ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_     )		,							SCREAMING_SNAKE_CASE_     )     )
    return torch.tensor(SCREAMING_SNAKE_CASE_		,							dtype=torch.floataa     )





class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__		):
 __lowerCamelCase    :				Optional[Any]								=       [e.name for e in KarrasDiffusionSchedulers]
 __lowerCamelCase    :				Any								=       2




 @register_to_config
 def __init__(						self      ,  _lowerCAmelCase = 1000      ,  _lowerCAmelCase = 0.00085      ,  _lowerCAmelCase = 0.012      ,  _lowerCAmelCase = "linear"      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = "epsilon"      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 1.0      ,  _lowerCAmelCase = "linspace"      ,  _lowerCAmelCase = 0      ,  )     ->				Union[str, Any]:
     if trained_betas is not None:
         _lowerCAmelCase						      =   torch.tensor(_lowerCAmelCase      ,  dtype=torch.floataa		)
     elif beta_schedule == "linear":
         _lowerCAmelCase						      =   torch.linspace(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  dtype=torch.floataa		)
     elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         _lowerCAmelCase						      =   (
             torch.linspace(beta_start**0.5      ,  beta_end**0.5      ,  _lowerCAmelCase      ,  dtype=torch.floataa		) ** 2
         )
     elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _lowerCAmelCase						      =   betas_for_alpha_bar(_lowerCAmelCase      ,  alpha_transform_type="cosine"		)
     elif beta_schedule == "exp":
         _lowerCAmelCase						      =   betas_for_alpha_bar(_lowerCAmelCase      ,  alpha_transform_type="exp"		)
     else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''		)

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

     #  set all values
     self.set_timesteps(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   use_karras_sigmas




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None		)     ->				List[str]:
     if schedule_timesteps is None:
         _lowerCAmelCase						      =   self.timesteps

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

     return indices[pos].item()




 @property
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     # 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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )     ->				torch.FloatTensor:
     _lowerCAmelCase						      =   self.index_for_timestep(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.sigmas[step_index]
     _lowerCAmelCase						      =   sample / ((sigma**2 + 1) ** 0.5)
     return sample




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  )     ->				Any:
     _lowerCAmelCase						      =   num_inference_steps

     _lowerCAmelCase						      =   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":
         _lowerCAmelCase						      =   np.linspace(0      ,  num_train_timesteps - 1      ,  _lowerCAmelCase      ,  dtype=_lowerCAmelCase		)[::-1].copy()
     elif self.config.timestep_spacing == "leading":
         _lowerCAmelCase						      =   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
         _lowerCAmelCase						      =   (np.arange(0      ,  _lowerCAmelCase		) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase		)
         timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
         _lowerCAmelCase						      =   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
         _lowerCAmelCase						      =   (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\'.'''		)

     _lowerCAmelCase						      =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
     _lowerCAmelCase						      =   np.log(_lowerCAmelCase		)
     _lowerCAmelCase						      =   np.interp(_lowerCAmelCase      ,  np.arange(0      ,  len(_lowerCAmelCase		)		)      ,  _lowerCAmelCase		)

     if self.config.use_karras_sigmas:
         _lowerCAmelCase						      =   self._convert_to_karras(in_sigmas=_lowerCAmelCase      ,  num_inference_steps=self.num_inference_steps		)
         _lowerCAmelCase						      =   np.array([self._sigma_to_t(_lowerCAmelCase      ,  _lowerCAmelCase		) for sigma in sigmas]		)

     _lowerCAmelCase						      =   np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
     _lowerCAmelCase						      =   torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

     _lowerCAmelCase						      =   torch.from_numpy(_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

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

     # empty dt and derivative
     _lowerCAmelCase						      =   None
     _lowerCAmelCase						      =   None

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[Any]:
     # get log sigma
     _lowerCAmelCase						      =   np.log(_lowerCAmelCase		)

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

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

     _lowerCAmelCase						      =   log_sigmas[low_idx]
     _lowerCAmelCase						      =   log_sigmas[high_idx]

     # interpolate sigmas
     _lowerCAmelCase						      =   (low - log_sigma) / (low - high)
     _lowerCAmelCase						      =   np.clip(_lowerCAmelCase      ,  0      ,  1		)

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				torch.FloatTensor:
     _lowerCAmelCase						      =   in_sigmas[-1].item()
     _lowerCAmelCase						      =   in_sigmas[0].item()

     _lowerCAmelCase						      =   7.0  # 7.0 is the value used in the paper
     _lowerCAmelCase						      =   np.linspace(0      ,  1      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   sigma_min ** (1 / rho)
     _lowerCAmelCase						      =   sigma_max ** (1 / rho)
     _lowerCAmelCase						      =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas




 @property
 def 				_snake_case (						self		)     ->				Tuple:
     return self.dt is None




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = True      ,  )     ->				Union[SchedulerOutput, Tuple]:
     _lowerCAmelCase						      =   self.index_for_timestep(_lowerCAmelCase		)

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

     if self.state_in_first_order:
         _lowerCAmelCase						      =   self.sigmas[step_index]
         _lowerCAmelCase						      =   self.sigmas[step_index + 1]
     else:
         # 2nd order / Heun's method
         _lowerCAmelCase						      =   self.sigmas[step_index - 1]
         _lowerCAmelCase						      =   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
     _lowerCAmelCase						      =   0
     _lowerCAmelCase						      =   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":
         _lowerCAmelCase						      =   sigma_hat if self.state_in_first_order else sigma_next
         _lowerCAmelCase						      =   sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
         _lowerCAmelCase						      =   sigma_hat if self.state_in_first_order else sigma_next
         _lowerCAmelCase						      =   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
     elif self.config.prediction_type == "sample":
         _lowerCAmelCase						      =   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:
         _lowerCAmelCase						      =   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
         _lowerCAmelCase						      =   (sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         _lowerCAmelCase						      =   sigma_next - sigma_hat

         # store for 2nd order step
         _lowerCAmelCase						      =   derivative
         _lowerCAmelCase						      =   dt
         _lowerCAmelCase						      =   sample
     else:
         # 2. 2nd order / Heun's method
         _lowerCAmelCase						      =   (sample - pred_original_sample) / sigma_next
         _lowerCAmelCase						      =   (self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         _lowerCAmelCase						      =   self.dt
         _lowerCAmelCase						      =   self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         _lowerCAmelCase						      =   None
         _lowerCAmelCase						      =   None
         _lowerCAmelCase						      =   None

     _lowerCAmelCase						      =   sample + derivative * dt

     if not return_dict:
         return (prev_sample,)

     return SchedulerOutput(prev_sample=_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )     ->				torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     _lowerCAmelCase						      =   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
         _lowerCAmelCase						      =   self.timesteps.to(original_samples.device      ,  dtype=torch.floataa		)
         _lowerCAmelCase						      =   timesteps.to(original_samples.device      ,  dtype=torch.floataa		)
     else:
         _lowerCAmelCase						      =   self.timesteps.to(original_samples.device		)
         _lowerCAmelCase						      =   timesteps.to(original_samples.device		)

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

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

     _lowerCAmelCase						      =   original_samples + noise * sigma
     return noisy_samples





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








"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''vit_mae'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]=768       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			Optional[int]=3_072       ,				_UpperCAmelCase			:			Any="gelu"       ,				_UpperCAmelCase			:			Optional[Any]=0.0       ,				_UpperCAmelCase			:			Optional[int]=0.0       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			List[Any]=1E-1_2       ,				_UpperCAmelCase			:			Optional[Any]=224       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=3       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=512       ,				_UpperCAmelCase			:			int=8       ,				_UpperCAmelCase			:			List[Any]=2_048       ,				_UpperCAmelCase			:			Optional[Any]=0.75       ,				_UpperCAmelCase			:			List[str]=False       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       super().__init__(**_UpperCAmelCase						)

       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  image_size
       _A     =  patch_size
       _A     =  num_channels
       _A     =  qkv_bias
       _A     =  decoder_num_attention_heads
       _A     =  decoder_hidden_size
       _A     =  decoder_num_hidden_layers
       _A     =  decoder_intermediate_size
       _A     =  mask_ratio
       _A     =  norm_pix_loss

