





"""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  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				torch.FloatTensor
       SCREAMING_SNAKE_CASE_       :				Optional[torch.FloatTensor]     						=  None




def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Union[str, Any]=0.999			,       a_					:		int="cosine"			,       )      ->				Dict:
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(a_					:		Optional[int]							):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(a_					:		Optional[Any]							):
                  return math.exp(t * -12.0							)

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

      __SCREAMING_SNAKE_CASE       :int		    =  []
      for i in range(a_							):
            __SCREAMING_SNAKE_CASE       :Any		    =  i / num_diffusion_timesteps
            __SCREAMING_SNAKE_CASE       :str		    =  (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(       A   ,     A  ):




       @register_to_config
       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 10_00		,SCREAMING_SNAKE_CASE__ = "fixed_small_log"		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 1.0		,SCREAMING_SNAKE_CASE__ = "epsilon"		,SCREAMING_SNAKE_CASE__ = "squaredcos_cap_v2"		,)      ->				str:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  betas_for_alpha_bar(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Any		    =  1.0 - self.betas
             __SCREAMING_SNAKE_CASE       :Any		    =  torch.cumprod(self.alphas		,dim=0							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  torch.tensor(1.0							)

             # standard deviation of the initial noise distribution
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  1.0

             # setable values
             __SCREAMING_SNAKE_CASE       :int		    =  None
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.from_numpy(np.arange(0		,SCREAMING_SNAKE_CASE__							)[::-1].copy()							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  variance_type




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)      ->				torch.FloatTensor:





             """simple docstring"""






             return sample




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_inference_steps
             __SCREAMING_SNAKE_CASE       :Tuple		    =  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  (np.arange(0		,SCREAMING_SNAKE_CASE__							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
             __SCREAMING_SNAKE_CASE       :str		    =  torch.from_numpy(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None							)      ->				Union[str, Any]:





             """simple docstring"""






             if prev_timestep is None:
                   __SCREAMING_SNAKE_CASE       :str		    =  t - 1

             __SCREAMING_SNAKE_CASE       :Dict		    =  self.alphas_cumprod[t]
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
             __SCREAMING_SNAKE_CASE       :Any		    =  1 - alpha_prod_t
             __SCREAMING_SNAKE_CASE       :List[str]		    =  1 - alpha_prod_t_prev

             if prev_timestep == t - 1:
                   __SCREAMING_SNAKE_CASE       :Any		    =  self.betas[t]
             else:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  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
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  beta_prod_t_prev / beta_prod_t * beta

             if variance_type is None:
                   __SCREAMING_SNAKE_CASE       :Any		    =  self.config.variance_type

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

                   __SCREAMING_SNAKE_CASE       :List[str]		    =  (predicted_variance + 1) / 2
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  frac * max_log + (1 - frac) * min_log

             return variance




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__ = True		,)      ->				Union[UnCLIPSchedulerOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  timestep

             if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.split(SCREAMING_SNAKE_CASE__		,sample.shape[1]		,dim=1							)
             else:
                   __SCREAMING_SNAKE_CASE       :Any		    =  None

             # 1. compute alphas, betas
             if prev_timestep is None:
                   __SCREAMING_SNAKE_CASE       :Any		    =  t - 1

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.alphas_cumprod[t]
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
             __SCREAMING_SNAKE_CASE       :int		    =  1 - alpha_prod_t
             __SCREAMING_SNAKE_CASE       :Dict		    =  1 - alpha_prod_t_prev

             if prev_timestep == t - 1:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  self.betas[t]
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  self.alphas[t]
             else:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  1 - alpha_prod_t / alpha_prod_t_prev
                   __SCREAMING_SNAKE_CASE       :Union[str, 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":
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             elif self.config.prediction_type == "sample":
                   __SCREAMING_SNAKE_CASE       :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:
                   __SCREAMING_SNAKE_CASE       :int		    =  torch.clamp(
                       SCREAMING_SNAKE_CASE__		,-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
             __SCREAMING_SNAKE_CASE       :Dict		    =  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
             __SCREAMING_SNAKE_CASE       :Union[str, 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
             __SCREAMING_SNAKE_CASE       :Dict		    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

                   __SCREAMING_SNAKE_CASE       :Tuple		    =  self._get_variance(
                       SCREAMING_SNAKE_CASE__		,predicted_variance=SCREAMING_SNAKE_CASE__		,prev_timestep=SCREAMING_SNAKE_CASE__		,)

                   if self.variance_type == "fixed_small_log":
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  variance
                   elif self.variance_type == "learned_range":
                         __SCREAMING_SNAKE_CASE       :str		    =  (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.'''							)

                   __SCREAMING_SNAKE_CASE       :int		    =  variance * variance_noise

             __SCREAMING_SNAKE_CASE       :List[str]		    =  pred_prev_sample + variance

             if not return_dict:
                   return (pred_prev_sample,)

             return UnCLIPSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,)      ->				torch.FloatTensor:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.alphas_cumprod.to(device=original_samples.device		,dtype=original_samples.dtype							)
             __SCREAMING_SNAKE_CASE       :Any		    =  timesteps.to(original_samples.device							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  alphas_cumprod[timesteps] ** 0.5
             __SCREAMING_SNAKE_CASE       :List[str]		    =  sqrt_alpha_prod.flatten()
             while len(sqrt_alpha_prod.shape							) < len(original_samples.shape							):
                   __SCREAMING_SNAKE_CASE       :str		    =  sqrt_alpha_prod.unsqueeze(-1							)

             __SCREAMING_SNAKE_CASE       :Dict		    =  (1 - alphas_cumprod[timesteps]) ** 0.5
             __SCREAMING_SNAKE_CASE       :int		    =  sqrt_one_minus_alpha_prod.flatten()
             while len(sqrt_one_minus_alpha_prod.shape							) < len(original_samples.shape							):
                   __SCREAMING_SNAKE_CASE       :str		    =  sqrt_one_minus_alpha_prod.unsqueeze(-1							)

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
             return noisy_samples






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_hidden_layers
             __SCREAMING_SNAKE_CASE       :List[str]		    =  num_attention_heads
             __SCREAMING_SNAKE_CASE       :Any		    =  intermediate_size
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :int		    =  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Dict		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Dict		    =  layer_norm_eps

             __SCREAMING_SNAKE_CASE       :Any		    =  image_size
             __SCREAMING_SNAKE_CASE       :Any		    =  num_frames
             __SCREAMING_SNAKE_CASE       :Any		    =  tubelet_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_channels
             __SCREAMING_SNAKE_CASE       :List[str]		    =  qkv_bias

             super().__init__(**SCREAMING_SNAKE_CASE__							)