





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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
SCREAMING_SNAKE_CASE_						   =       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""")

@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	Optional[str]			=			field(
         default="cifar10"							,      metadata={"help": "Name of a dataset from the datasets package"}   )
     __snake_case    :	Optional[str]			=			field(
         default=lowercase__							,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
     __snake_case    :	Optional[str]			=			field(
         default=lowercase__							,      metadata={"help": "The column name of the images in the files."}   )
     __snake_case    :	Optional[str]			=			field(default=lowercase__							,      metadata={"help": "A folder containing the training data."}   )
     __snake_case    :	Optional[str]			=			field(default=lowercase__							,      metadata={"help": "A folder containing the validation data."}   )
     __snake_case    :	Optional[float]			=			field(
         default=0.15							,      metadata={"help": "Percent to split off of train for validation."}   )
     __snake_case    :	Optional[int]			=			field(
         default=lowercase__							,      metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }							,      )
     __snake_case    :	Optional[int]			=			field(
         default=lowercase__							,      metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }							,      )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{}
           if self.train_dir is not None:
                 SCREAMING_SNAKE_CASE		=						self.train_dir
           if self.validation_dir is not None:
                 SCREAMING_SNAKE_CASE		=						self.validation_dir
           SCREAMING_SNAKE_CASE		=						data_files if data_files else None

@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	str			=			field(
         default=lowercase__							,      metadata={
             "help": (
                 "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
             )
         }							,      )
     __snake_case    :	Optional[str]			=			field(
         default=lowercase__							,      metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}   )
     __snake_case    :	Optional[str]			=			field(
         default=lowercase__							,      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"
             )
         }							,      )
     __snake_case    :	Optional[str]			=			field(
         default=lowercase__							,      metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}   )
     __snake_case    :	str			=			field(
         default="main"							,      metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							,      )
     __snake_case    :	str			=			field(default=lowercase__							,      metadata={"help": "Name or path of preprocessor config."}   )
     __snake_case    :	bool			=			field(
         default=lowercase__							,      metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }							,      )
     __snake_case    :	float			=			field(
         default=0.75							,      metadata={"help": "The ratio of the number of masked tokens in the input sequence."}   )
     __snake_case    :	bool			=			field(
         default=lowercase__							,      metadata={"help": "Whether or not to train with normalized pixel values as target."}   )

@dataclass
class 		UpperCamelCase__ (      lowercase__   ):



     '''simple docstring'''

     __snake_case    :	float			=			field(
         default=1E-3							,      metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}   )



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						torch.stack([example["""pixel_values"""] for example in examples]	)
      return {"pixel_values": pixel_values}



def    __lowercase   (      ) ->   Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)	)
      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.
            SCREAMING_SNAKE_CASE		=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
      else:
            SCREAMING_SNAKE_CASE		=						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_mae""" ,						__lowerCamelCase ,						__lowerCamelCase	)

      # 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()

      SCREAMING_SNAKE_CASE		=						training_args.get_process_log_level()
      logger.setLevel(__lowerCamelCase	)
      transformers.utils.logging.set_verbosity(__lowerCamelCase	)
      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.
      SCREAMING_SNAKE_CASE		=						None
      if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
            SCREAMING_SNAKE_CASE		=						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.
      SCREAMING_SNAKE_CASE		=						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.
      SCREAMING_SNAKE_CASE		=						None if "validation" in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split ,						__lowerCamelCase	) and data_args.train_val_split > 0.0:
            SCREAMING_SNAKE_CASE		=						ds["train"].train_test_split(data_args.train_val_split	)
            SCREAMING_SNAKE_CASE		=						split["train"]
            SCREAMING_SNAKE_CASE		=						split["test"]

      # Load pretrained model and image processor
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      SCREAMING_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,
      }
      if model_args.config_name:
            SCREAMING_SNAKE_CASE		=						ViTMAEConfig.from_pretrained(model_args.config_name ,						**__lowerCamelCase	)
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE		=						ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,						**__lowerCamelCase	)
      else:
            SCREAMING_SNAKE_CASE		=						ViTMAEConfig()
            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}"""	)

    # adapt config
      config.update(
          {
              """mask_ratio""": model_args.mask_ratio,
              """norm_pix_loss""": model_args.norm_pix_loss,
          }	)

      # create image processor
      if model_args.image_processor_name:
            SCREAMING_SNAKE_CASE		=						ViTImageProcessor.from_pretrained(model_args.image_processor_name ,						**__lowerCamelCase	)
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE		=						ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,						**__lowerCamelCase	)
      else:
            SCREAMING_SNAKE_CASE		=						ViTImageProcessor()

      # create model
      if model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE		=						ViTMAEForPreTraining.from_pretrained(
                model_args.model_name_or_path ,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path	) ,						config=__lowerCamelCase ,						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"""	)
            SCREAMING_SNAKE_CASE		=						ViTMAEForPreTraining(__lowerCamelCase	)

      if training_args.do_train:
            SCREAMING_SNAKE_CASE		=						ds["train"].column_names
      else:
            SCREAMING_SNAKE_CASE		=						ds["validation"].column_names

      if data_args.image_column_name is not None:
            SCREAMING_SNAKE_CASE		=						data_args.image_column_name
      elif "image" in column_names:
            SCREAMING_SNAKE_CASE		=						"image"
      elif "img" in column_names:
            SCREAMING_SNAKE_CASE		=						"img"
      else:
            SCREAMING_SNAKE_CASE		=						column_names[0]

      # transformations as done in original MAE paper
      # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
      if "shortest_edge" in image_processor.size:
            SCREAMING_SNAKE_CASE		=						image_processor.size["shortest_edge"]
      else:
            SCREAMING_SNAKE_CASE		=						(image_processor.size["height"], image_processor.size["width"])
      SCREAMING_SNAKE_CASE		=						Compose(
          [
              Lambda(lambda _SCREAMING_SNAKE_CASE	: img.convert("""RGB"""	) if img.mode != "RGB" else img	),
              RandomResizedCrop(__lowerCamelCase ,						scale=(0.2, 1.0) ,						interpolation=InterpolationMode.BICUBIC	),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean ,						std=image_processor.image_std	),
          ]	)

      def preprocess_images(_SCREAMING_SNAKE_CASE	):
            SCREAMING_SNAKE_CASE		=						[transforms(__lowerCamelCase	) for image in 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:
                  SCREAMING_SNAKE_CASE		=						ds["train"].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
            # Set the training transforms
            ds["train"].set_transform(__lowerCamelCase	)

      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:
                  SCREAMING_SNAKE_CASE		=						(
                      ds["validation"].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(__lowerCamelCase	)

      # Compute absolute learning rate
      SCREAMING_SNAKE_CASE		=						(
          training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
      )
      if training_args.base_learning_rate is not None:
            SCREAMING_SNAKE_CASE		=						training_args.base_learning_rate * total_train_batch_size / 2_56

      # Initialize our trainer
      SCREAMING_SNAKE_CASE		=						Trainer(
          model=__lowerCamelCase ,						args=__lowerCamelCase ,						train_dataset=ds["""train"""] if training_args.do_train else None ,						eval_dataset=ds["""validation"""] if training_args.do_eval else None ,						tokenizer=__lowerCamelCase ,						data_collator=__lowerCamelCase ,						)

      # Training
      if training_args.do_train:
            SCREAMING_SNAKE_CASE		=						None
            if training_args.resume_from_checkpoint is not None:
                  SCREAMING_SNAKE_CASE		=						training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE		=						last_checkpoint
            SCREAMING_SNAKE_CASE		=						trainer.train(resume_from_checkpoint=__lowerCamelCase	)
            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:
            SCREAMING_SNAKE_CASE		=						trainer.evaluate()
            trainer.log_metrics("""eval""" ,						__lowerCamelCase	)
            trainer.save_metrics("""eval""" ,						__lowerCamelCase	)

      # Write model card and (optionally) push to hub
      SCREAMING_SNAKE_CASE		=						{
          "tasks": "masked-auto-encoding",
          "dataset": data_args.dataset_name,
          "tags": ["masked-auto-encoding"],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**__lowerCamelCase	)
      else:
            trainer.create_model_card(**__lowerCamelCase	)



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()




"""simple docstring"""





def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    int(__lowerCamelCase  )
   if n_element < 1:
      UpperCAmelCase_					:					List[Any]			     =    ValueError("a should be a positive number"  )
      raise my_error

   UpperCAmelCase_					:					List[Any]			     =    [1]
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    (0, 0, 0)
   UpperCAmelCase_					:					Dict			     =    1
   while index < n_element:
      while hamming_list[i] * 2 <= hamming_list[-1]:
         i += 1
      while hamming_list[j] * 3 <= hamming_list[-1]:
         j += 1
      while hamming_list[k] * 5 <= hamming_list[-1]:
         k += 1
      hamming_list.append(
          min(hamming_list[i] * 2,		hamming_list[j] * 3,		hamming_list[k] * 5  )  )
      index += 1
   return hamming_list


if __name__ == "__main__":
    _a		       =							input('Enter the last number (nth term) of the Hamming Number Series: ')
    print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
    _a		       =							hamming(int(n))
    print('-----------------------------------------------------')
    print(f"""The list with nth numbers is: {hamming_numbers}""")
    print('-----------------------------------------------------')


