


"""simple docstring"""





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
snake_case__		:     Optional[int]    				=				logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

snake_case__		:     Union[str, Any]    				=				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
snake_case__		:     Optional[Any]    				=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 		snake_case_:
     __UpperCamelCase  =					field(
         default='''cifar10'''							,					metadata={'''help''': '''Name of a dataset from the datasets package'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}							,					)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''A folder containing the training data.'''}			)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''A folder containing the validation data.'''}			)
     __UpperCamelCase  =					field(
         default=0.15							,					metadata={'''help''': '''Percent to split off of train for validation.'''}			)
     __UpperCamelCase  =					field(default=32							,					metadata={'''help''': '''The size of the square patches to use for masking.'''}			)
     __UpperCamelCase  =					field(
         default=0.6							,					metadata={'''help''': '''Percentage of patches to mask.'''}							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                 '''value if set.'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                 '''value if set.'''
             )
         }							,					)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Tuple       =   {}
           if self.train_dir is not None:
                 lowerCAmelCase					:	List[Any]       =   self.train_dir
           if self.validation_dir is not None:
                 lowerCAmelCase					:	Tuple       =   self.validation_dir
           lowerCAmelCase					:	Dict       =   data_files if data_files else None



@dataclass
class 		snake_case_:
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
                 '''checkpoint identifier on the hub. '''
                 '''Don\'t set if you want to train a model from scratch.'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(a__			)}							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''Override some existing default config settings when a model is trained from scratch. Example: '''
                 '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}							,					)
     __UpperCamelCase  =					field(
         default='''main'''							,					metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}							,					)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''Name or path of preprocessor config.'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                 '''with private models).'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={
             '''help''': (
                 '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
             )
         }							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''Stride to use for the encoder.'''}							,					)



class 		snake_case_:






     def __init__(	self  :					Dict					,				UpperCamelCase_  :					List[Any]=1_9_2					,				UpperCamelCase_  :					Union[str, Any]=3_2					,				UpperCamelCase_  :					str=4					,				UpperCamelCase_  :					Optional[int]=0.6				):
           lowerCAmelCase					:	Optional[int]       =   input_size
           lowerCAmelCase					:	List[Any]       =   mask_patch_size
           lowerCAmelCase					:	Any       =   model_patch_size
           lowerCAmelCase					:	Any       =   mask_ratio

           if self.input_size % self.mask_patch_size != 0:
                 raise ValueError('''Input size must be divisible by mask patch size'''				)
           if self.mask_patch_size % self.model_patch_size != 0:
                 raise ValueError('''Mask patch size must be divisible by model patch size'''				)

           lowerCAmelCase					:	int       =   self.input_size // self.mask_patch_size
           lowerCAmelCase					:	List[Any]       =   self.mask_patch_size // self.model_patch_size

           lowerCAmelCase					:	Tuple       =   self.rand_size**2
           lowerCAmelCase					:	Optional[Any]       =   int(np.ceil(self.token_count * self.mask_ratio				)				)






     def __call__(	self  :					str				):
           lowerCAmelCase					:	Any       =   np.random.permutation(self.token_count				)[: self.mask_count]
           lowerCAmelCase					:	Optional[Any]       =   np.zeros(self.token_count					,				dtype=UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   1

           lowerCAmelCase					:	Optional[int]       =   mask.reshape((self.rand_size, self.rand_size)				)
           lowerCAmelCase					:	List[str]       =   mask.repeat(self.scale					,				axis=0				).repeat(self.scale					,				axis=1				)

           return torch.tensor(mask.flatten()				)



def      _snake_case						(						_snake_case				:			str			):
      lowerCAmelCase					:	int       =   torch.stack([example['''pixel_values'''] for example in examples]			)
      lowerCAmelCase					:	Any       =   torch.stack([example['''mask'''] for example in examples]			)
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def      _snake_case						(						):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      lowerCAmelCase					:	int       =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
      if len(sys.argv			) == 2 and sys.argv[1].endswith('''.json'''			):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            lowerCAmelCase					:	Union[str, Any]       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            lowerCAmelCase					:	int       =   parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry('''run_mim''' ,   _snake_case ,   _snake_case			)

      # Setup logging
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,   datefmt='''%m/%d/%Y %H:%M:%S''' ,   handlers=[logging.StreamHandler(sys.stdout			)] ,   )

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      lowerCAmelCase					:	List[str]       =   training_args.get_process_log_level()
      logger.setLevel(_snake_case			)
      transformers.utils.logging.set_verbosity(_snake_case			)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # Log on each process the small summary:
      logger.warning(
          f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
          + f'''distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'''			)
      logger.info(f'''Training/evaluation parameters {training_args}'''			)

      # Detecting last checkpoint.
      lowerCAmelCase					:	str       =   None
      if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
            lowerCAmelCase					:	List[Any]       =   get_last_checkpoint(training_args.output_dir			)
            if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
                  raise ValueError(
                      f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                      '''Use --overwrite_output_dir to overcome.'''			)
            elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                  logger.info(
                      f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                      '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''			)

    # Initialize our dataset.
      lowerCAmelCase					:	Optional[Any]       =   load_dataset(
          data_args.dataset_name ,   data_args.dataset_config_name ,   data_files=data_args.data_files ,   cache_dir=model_args.cache_dir ,   use_auth_token=True if model_args.use_auth_token else None ,   )

      # If we don't have a validation split, split off a percentage of train as validation.
      lowerCAmelCase					:	Optional[int]       =   None if '''validation''' in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split ,   _snake_case			) and data_args.train_val_split > 0.0:
            lowerCAmelCase					:	List[str]       =   ds['''train'''].train_test_split(data_args.train_val_split			)
            lowerCAmelCase					:	List[str]       =   split['''train''']
            lowerCAmelCase					:	Dict       =   split['''test''']

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      lowerCAmelCase					:	List[Any]       =   {
          '''cache_dir''': model_args.cache_dir,
          '''revision''': model_args.model_revision,
          '''use_auth_token''': True if model_args.use_auth_token else None,
      }
      if model_args.config_name_or_path:
            lowerCAmelCase					:	Optional[Any]       =   AutoConfig.from_pretrained(model_args.config_name_or_path ,   **_snake_case			)
      elif model_args.model_name_or_path:
            lowerCAmelCase					:	Union[str, Any]       =   AutoConfig.from_pretrained(model_args.model_name_or_path ,   **_snake_case			)
      else:
            lowerCAmelCase					:	Union[str, Any]       =   CONFIG_MAPPING[model_args.model_type]()
            logger.warning('''You are instantiating a new config instance from scratch.'''			)
            if model_args.config_overrides is not None:
                  logger.info(f'''Overriding config: {model_args.config_overrides}'''			)
                  config.update_from_string(model_args.config_overrides			)
                  logger.info(f'''New config: {config}'''			)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
      if hasattr(_snake_case ,   '''decoder_type'''			):
            lowerCAmelCase					:	Tuple       =   '''simmim'''

      # adapt config
      lowerCAmelCase					:	List[str]       =   model_args.image_size if model_args.image_size is not None else config.image_size
      lowerCAmelCase					:	int       =   model_args.patch_size if model_args.patch_size is not None else config.patch_size
      lowerCAmelCase					:	str       =   (
          model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
      )

      config.update(
          {
              '''image_size''': model_args.image_size,
              '''patch_size''': model_args.patch_size,
              '''encoder_stride''': model_args.encoder_stride,
          }			)

      # create image processor
      if model_args.image_processor_name:
            lowerCAmelCase					:	Dict       =   AutoImageProcessor.from_pretrained(model_args.image_processor_name ,   **_snake_case			)
      elif model_args.model_name_or_path:
            lowerCAmelCase					:	Optional[int]       =   AutoImageProcessor.from_pretrained(model_args.model_name_or_path ,   **_snake_case			)
      else:
            lowerCAmelCase					:	List[str]       =   {
                conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
            }
            lowerCAmelCase					:	List[Any]       =   IMAGE_PROCESSOR_TYPES[model_args.model_type]()

      # create model
      if model_args.model_name_or_path:
            lowerCAmelCase					:	Any       =   AutoModelForMaskedImageModeling.from_pretrained(
                model_args.model_name_or_path ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path			) ,   config=_snake_case ,   cache_dir=model_args.cache_dir ,   revision=model_args.model_revision ,   use_auth_token=True if model_args.use_auth_token else None ,   )
      else:
            logger.info('''Training new model from scratch'''			)
            lowerCAmelCase					:	int       =   AutoModelForMaskedImageModeling.from_config(_snake_case			)

      if training_args.do_train:
            lowerCAmelCase					:	Union[str, Any]       =   ds['''train'''].column_names
      else:
            lowerCAmelCase					:	str       =   ds['''validation'''].column_names

      if data_args.image_column_name is not None:
            lowerCAmelCase					:	Dict       =   data_args.image_column_name
      elif "image" in column_names:
            lowerCAmelCase					:	Union[str, Any]       =   '''image'''
      elif "img" in column_names:
            lowerCAmelCase					:	Optional[int]       =   '''img'''
      else:
            lowerCAmelCase					:	str       =   column_names[0]

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      lowerCAmelCase					:	str       =   Compose(
          [
              Lambda(lambda _snake_case			: img.convert('''RGB'''			) if img.mode != "RGB" else img			),
              RandomResizedCrop(model_args.image_size ,   scale=(0.67, 1.0) ,   ratio=(3.0 / 4.0, 4.0 / 3.0)			),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean ,   std=image_processor.image_std			),
          ]			)

      # create mask generator
      lowerCAmelCase					:	Union[str, Any]       =   MaskGenerator(
          input_size=model_args.image_size ,   mask_patch_size=data_args.mask_patch_size ,   model_patch_size=model_args.patch_size ,   mask_ratio=data_args.mask_ratio ,   )

      def preprocess_images(_snake_case				:			Union[str, Any]			):
            lowerCAmelCase					:	Union[str, Any]       =   [transforms(_snake_case			) for image in examples[image_column_name]]
            lowerCAmelCase					:	Optional[int]       =   [mask_generator() for i in range(len(examples[image_column_name]			)			)]

            return examples

      if training_args.do_train:
            if "train" not in ds:
                  raise ValueError('''--do_train requires a train dataset'''			)
            if data_args.max_train_samples is not None:
                  lowerCAmelCase					:	Optional[int]       =   ds['''train'''].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
            # Set the training transforms
            ds["train"].set_transform(_snake_case			)

      if training_args.do_eval:
            if "validation" not in ds:
                  raise ValueError('''--do_eval requires a validation dataset'''			)
            if data_args.max_eval_samples is not None:
                  lowerCAmelCase					:	Dict       =   (
                      ds['''validation'''].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(_snake_case			)

      # Initialize our trainer
      lowerCAmelCase					:	Any       =   Trainer(
          model=_snake_case ,   args=_snake_case ,   train_dataset=ds['''train'''] if training_args.do_train else None ,   eval_dataset=ds['''validation'''] if training_args.do_eval else None ,   tokenizer=_snake_case ,   data_collator=_snake_case ,   )

      # Training
      if training_args.do_train:
            lowerCAmelCase					:	List[Any]       =   None
            if training_args.resume_from_checkpoint is not None:
                  lowerCAmelCase					:	Any       =   training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowerCAmelCase					:	Dict       =   last_checkpoint
            lowerCAmelCase					:	Union[str, Any]       =   trainer.train(resume_from_checkpoint=_snake_case			)
            trainer.save_model()
            trainer.log_metrics('''train''' ,   train_result.metrics			)
            trainer.save_metrics('''train''' ,   train_result.metrics			)
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            lowerCAmelCase					:	Optional[int]       =   trainer.evaluate()
            trainer.log_metrics('''eval''' ,   _snake_case			)
            trainer.save_metrics('''eval''' ,   _snake_case			)

      # Write model card and (optionally) push to hub
      lowerCAmelCase					:	List[Any]       =   {
          '''finetuned_from''': model_args.model_name_or_path,
          '''tasks''': '''masked-image-modeling''',
          '''dataset''': data_args.dataset_name,
          '''tags''': ['''masked-image-modeling'''],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**_snake_case			)
      else:
            trainer.create_model_card(**_snake_case			)


if __name__ == "__main__":
      main()






"""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 		snake_case_(       a__			):
     __UpperCamelCase  =					42
     __UpperCamelCase  =					None



def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			List[str]=0.999 ,   _snake_case				:			Dict="cosine" ,   ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_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(_snake_case				:			Optional[int]			):
                  return math.exp(t * -12.0			)

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

      lowerCAmelCase					:	List[Any]       =   []
      for i in range(_snake_case			):
            lowerCAmelCase					:	int       =   i / num_diffusion_timesteps
            lowerCAmelCase					:	Tuple       =   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_snake_case			) / alpha_bar_fn(_snake_case			) ,   _snake_case			)			)
      return torch.tensor(_snake_case ,   dtype=torch.floataa			)






class 		snake_case_(       a__							,					a__			):






     @register_to_config
     def __init__(	self  :					Any					,				UpperCamelCase_  :					int = 1_0_0_0					,				UpperCamelCase_  :					str = "fixed_small_log"					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Optional[float] = 1.0					,				UpperCamelCase_  :					str = "epsilon"					,				UpperCamelCase_  :					str = "squaredcos_cap_v2"					,				):
           if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''				)

           lowerCAmelCase					:	Any       =   betas_for_alpha_bar(UpperCamelCase_				)

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

           # standard deviation of the initial noise distribution
           lowerCAmelCase					:	Any       =   1.0

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

           lowerCAmelCase					:	List[str]       =   variance_type






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None				):
           return sample






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, torch.device] = None				):
           lowerCAmelCase					:	Any       =   num_inference_steps
           lowerCAmelCase					:	str       =   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           lowerCAmelCase					:	Tuple       =   (np.arange(0					,				UpperCamelCase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
           lowerCAmelCase					:	Any       =   torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					Any=None				):
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	int       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	Dict       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	Tuple       =   self.betas[t]
           else:
                 lowerCAmelCase					:	Union[str, Any]       =   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
           lowerCAmelCase					:	Optional[Any]       =   beta_prod_t_prev / beta_prod_t * beta

           if variance_type is None:
                 lowerCAmelCase					:	List[str]       =   self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
                 lowerCAmelCase					:	Any       =   torch.log(torch.clamp(UpperCamelCase_					,				min=1E-20				)				)
                 lowerCAmelCase					:	Union[str, Any]       =   torch.exp(0.5 * variance				)
           elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 lowerCAmelCase					:	Optional[Any]       =   variance.log()
                 lowerCAmelCase					:	Union[str, Any]       =   beta.log()

                 lowerCAmelCase					:	Dict       =   (predicted_variance + 1) / 2
                 lowerCAmelCase					:	Union[str, Any]       =   frac * max_log + (1 - frac) * min_log

           return variance






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					List[Any]=None					,				UpperCamelCase_  :					bool = True					,				):
           lowerCAmelCase					:	Optional[Any]       =   timestep

           if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   torch.split(UpperCamelCase_					,				sample.shape[1]					,				dim=1				)
           else:
                 lowerCAmelCase					:	Optional[int]       =   None

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

           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Optional[int]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	int       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

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

           # 6. Add noise
           lowerCAmelCase					:	int       =   0
           if t > 0:
                 lowerCAmelCase					:	Union[str, Any]       =   randn_tensor(
                     model_output.shape					,				dtype=model_output.dtype					,				generator=UpperCamelCase_					,				device=model_output.device				)

                 lowerCAmelCase					:	Any       =   self._get_variance(
                     UpperCamelCase_					,				predicted_variance=UpperCamelCase_					,				prev_timestep=UpperCamelCase_					,				)

                 if self.variance_type == "fixed_small_log":
                       lowerCAmelCase					:	str       =   variance
                 elif self.variance_type == "learned_range":
                       lowerCAmelCase					:	Optional[Any]       =   (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.'''				)

                 lowerCAmelCase					:	List[Any]       =   variance * variance_noise

           lowerCAmelCase					:	int       =   pred_prev_sample + variance

           if not return_dict:
                 return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase_					,				pred_original_sample=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.IntTensor					,				):
           # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
           lowerCAmelCase					:	Tuple       =   self.alphas_cumprod.to(device=original_samples.device					,				dtype=original_samples.dtype				)
           lowerCAmelCase					:	int       =   timesteps.to(original_samples.device				)

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

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

           lowerCAmelCase					:	Dict       =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples


