





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_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = None





def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Dict=0.999					,				lowerCamelCase:   Optional[int]="cosine"					,				)	->						str:







      '''simple docstring'''






      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      __A          =				[]
      for i in range(lowerCamelCase ):
            __A          =				i / num_diffusion_timesteps
            __A          =				(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_					(			_lowerCamelCase ,      _lowerCamelCase   ):




   @register_to_config
   def __init__(self	:Tuple    ,   _UpperCamelCase	:int = 1000    ,   _UpperCamelCase	:str = "fixed_small_log"    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Optional[float] = 1.0    ,   _UpperCamelCase	:str = "epsilon"    ,   _UpperCamelCase	:str = "squaredcos_cap_v2"    ,   )->					List[Any]:
         if beta_schedule != "squaredcos_cap_v2":
               raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )

         __A          =				betas_for_alpha_bar(_UpperCamelCase )

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

         # standard deviation of the initial noise distribution
         __A          =				1.0

         # setable values
         __A          =				None
         __A          =				torch.from_numpy(np.arange(0    ,   _UpperCamelCase )[::-1].copy() )

         __A          =				variance_type




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:torch.FloatTensor    ,   _UpperCamelCase	:Optional[int] = None )->					torch.FloatTensor:
         return sample




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Union[str, torch.device] = None )->					Union[str, Any]:
         __A          =				num_inference_steps
         __A          =				(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         __A          =				(np.arange(0    ,   _UpperCamelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
         __A          =				torch.from_numpy(_UpperCamelCase ).to(_UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Tuple=None    ,   _UpperCamelCase	:Any=None    ,   _UpperCamelCase	:Any=None )->					Tuple:
         if prev_timestep is None:
               __A          =				t - 1

         __A          =				self.alphas_cumprod[t]
         __A          =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __A          =				1 - alpha_prod_t
         __A          =				1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               __A          =				self.betas[t]
         else:
               __A          =				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
         __A          =				beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
               __A          =				self.config.variance_type

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

               __A          =				(predicted_variance + 1) / 2
               __A          =				frac * max_log + (1 - frac) * min_log

         return variance




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:torch.FloatTensor    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:torch.FloatTensor    ,   _UpperCamelCase	:Optional[int] = None    ,   _UpperCamelCase	:Any=None    ,   _UpperCamelCase	:bool = True    ,   )->					Union[UnCLIPSchedulerOutput, Tuple]:
         __A          =				timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
               __A   ,						__A          =				torch.split(_UpperCamelCase    ,   sample.shape[1]    ,   dim=1 )
         else:
               __A          =				None

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

         __A          =				self.alphas_cumprod[t]
         __A          =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __A          =				1 - alpha_prod_t
         __A          =				1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               __A          =				self.betas[t]
               __A          =				self.alphas[t]
         else:
               __A          =				1 - alpha_prod_t / alpha_prod_t_prev
               __A          =				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":
               __A          =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               __A          =				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:
               __A          =				torch.clamp(
                   _UpperCamelCase    ,   -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
         __A          =				(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         __A          =				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
         __A          =				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         __A          =				0
         if t > 0:
               __A          =				randn_tensor(
                   model_output.shape    ,   dtype=model_output.dtype    ,   generator=_UpperCamelCase    ,   device=model_output.device )

               __A          =				self._get_variance(
                   _UpperCamelCase    ,   predicted_variance=_UpperCamelCase    ,   prev_timestep=_UpperCamelCase    ,   )

               if self.variance_type == "fixed_small_log":
                     __A          =				variance
               elif self.variance_type == "learned_range":
                     __A          =				(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.''' )

               __A          =				variance * variance_noise

         __A          =				pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=_UpperCamelCase    ,   pred_original_sample=_UpperCamelCase )






   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:torch.FloatTensor    ,   _UpperCamelCase	:torch.FloatTensor    ,   _UpperCamelCase	:torch.IntTensor    ,   )->					torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
         __A          =				self.alphas_cumprod.to(device=original_samples.device    ,   dtype=original_samples.dtype )
         __A          =				timesteps.to(original_samples.device )

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

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

         __A          =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case__					:							List[str]													=     {
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							Tuple													=     ['VivitImageProcessor']


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							Any													=     [
           'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VivitModel',
           'VivitPreTrainedModel',
           'VivitForVideoClassification',
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       snake_case__					:							List[str]													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





