






'''simple docstring'''

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
UpperCamelCase      					=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      A     :






 """simple docstring"""





 __a      :    Optional[str]			  =    field(
     default='''cifar10''', metadata={'''help''': '''Name of a dataset from the datasets package'''}    )
 __a      :    Optional[str]			  =    field(
     default=SCREAMING_SNAKE_CASE__, metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}    )
 __a      :    Optional[str]			  =    field(
     default=SCREAMING_SNAKE_CASE__, metadata={'''help''': '''The column name of the images in the files.'''}    )
 __a      :    Optional[str]			  =    field(default=SCREAMING_SNAKE_CASE__, metadata={'''help''': '''A folder containing the training data.'''}    )
 __a      :    Optional[str]			  =    field(default=SCREAMING_SNAKE_CASE__, metadata={'''help''': '''A folder containing the validation data.'''}    )
 __a      :    Optional[float]			  =    field(
     default=0.15, metadata={'''help''': '''Percent to split off of train for validation.'''}    )
 __a      :    Optional[int]			  =    field(
     default=SCREAMING_SNAKE_CASE__, metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }, )
 __a      :    Optional[int]			  =    field(
     default=SCREAMING_SNAKE_CASE__, metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     }, )
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: int            =				{}
       if self.train_dir is not None:
             UpperCamelCase_					: int            =				self.train_dir
       if self.validation_dir is not None:
             UpperCamelCase_					: Optional[int]            =				self.validation_dir
       UpperCamelCase_					: str            =				data_files if data_files else None






@dataclass
class      A     :






 """simple docstring"""





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






@dataclass
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





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






def 				snake_case       (  a_				:			Optional[Any]						)							->					Optional[Any]:





      """simple docstring"""

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



def 				snake_case       (  )							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				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.
            UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
      else:
            UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				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"""     , a_     , a_						)

      # 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()

      UpperCamelCase_					: List[Any]            =				training_args.get_process_log_level()
      logger.setLevel(a_						)
      transformers.utils.logging.set_verbosity(a_						)
      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.
      UpperCamelCase_					: Dict            =				None
      if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
            UpperCamelCase_					: Optional[int]            =				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.
      UpperCamelCase_					: List[str]            =				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.
      UpperCamelCase_					: Union[str, Any]            =				None if """validation""" in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split     , a_						) and data_args.train_val_split > 0.0:
            UpperCamelCase_					: Tuple            =				ds["""train"""].train_test_split(data_args.train_val_split						)
            UpperCamelCase_					: Tuple            =				split["""train"""]
            UpperCamelCase_					: List[Any]            =				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.
      UpperCamelCase_					: Union[str, 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:
            UpperCamelCase_					: int            =				ViTMAEConfig.from_pretrained(model_args.config_name     , **a_						)
      elif model_args.model_name_or_path:
            UpperCamelCase_					: Tuple            =				ViTMAEConfig.from_pretrained(model_args.model_name_or_path     , **a_						)
      else:
            UpperCamelCase_					: int            =				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:
            UpperCamelCase_					: Optional[Any]            =				ViTImageProcessor.from_pretrained(model_args.image_processor_name     , **a_						)
      elif model_args.model_name_or_path:
            UpperCamelCase_					: Tuple            =				ViTImageProcessor.from_pretrained(model_args.model_name_or_path     , **a_						)
      else:
            UpperCamelCase_					: List[Any]            =				ViTImageProcessor()

      # create model
      if model_args.model_name_or_path:
            UpperCamelCase_					: Optional[Any]            =				ViTMAEForPreTraining.from_pretrained(
                model_args.model_name_or_path     , from_tf=bool(""".ckpt""" in model_args.model_name_or_path						)     , config=a_     , 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"""						)
            UpperCamelCase_					: Dict            =				ViTMAEForPreTraining(a_						)

      if training_args.do_train:
            UpperCamelCase_					: List[Any]            =				ds["""train"""].column_names
      else:
            UpperCamelCase_					: List[Any]            =				ds["""validation"""].column_names

      if data_args.image_column_name is not None:
            UpperCamelCase_					: List[str]            =				data_args.image_column_name
      elif "image" in column_names:
            UpperCamelCase_					: List[Any]            =				"""image"""
      elif "img" in column_names:
            UpperCamelCase_					: Optional[Any]            =				"""img"""
      else:
            UpperCamelCase_					: str            =				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:
            UpperCamelCase_					: Dict            =				image_processor.size["""shortest_edge"""]
      else:
            UpperCamelCase_					: int            =				(image_processor.size["""height"""], image_processor.size["""width"""])
      UpperCamelCase_					: Tuple            =				Compose(
          [
              Lambda(lambda a_						: img.convert("""RGB"""						) if img.mode != "RGB" else img						),
              RandomResizedCrop(a_     , scale=(0.2, 1.0)     , interpolation=InterpolationMode.BICUBIC						),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean     , std=image_processor.image_std						),
          ]						)

      def preprocess_images(a_				:			Any						):
            UpperCamelCase_					: Tuple            =				[transforms(a_						) 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:
                  UpperCamelCase_					: List[str]            =				ds["""train"""].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
            # Set the training transforms
            ds["train"].set_transform(a_						)

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

      # Compute absolute learning rate
      UpperCamelCase_					: List[Any]            =				(
          training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
      )
      if training_args.base_learning_rate is not None:
            UpperCamelCase_					: Union[str, Any]            =				training_args.base_learning_rate * total_train_batch_size / 256

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

      # Training
      if training_args.do_train:
            UpperCamelCase_					: List[Any]            =				None
            if training_args.resume_from_checkpoint is not None:
                  UpperCamelCase_					: Tuple            =				training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  UpperCamelCase_					: str            =				last_checkpoint
            UpperCamelCase_					: str            =				trainer.train(resume_from_checkpoint=a_						)
            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:
            UpperCamelCase_					: int            =				trainer.evaluate()
            trainer.log_metrics("""eval"""     , a_						)
            trainer.save_metrics("""eval"""     , a_						)

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



def 				snake_case       (  a_				:			Optional[int]						)							->					List[Any]:





      """simple docstring"""

      main()


if __name__ == "__main__":
      main()














'''simple docstring'''

def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]						)							->					tuple[float, float]:





      """simple docstring"""

      if not len(a_						) == len(a_						) == 3:
            raise ValueError("""Please enter a valid equation."""						)
      if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
            raise ValueError("""Both a & b of two equations can't be zero."""						)

      # Extract the coefficients
      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Optional[int]            =				equationa
      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				equationa

      # Calculate the determinants of the matrices
      UpperCamelCase_					: Union[str, Any]            =				aa * ba - aa * ba
      UpperCamelCase_					: Dict            =				ca * ba - ca * ba
      UpperCamelCase_					: Dict            =				aa * ca - aa * ca

      # Check if the system of linear equations has a solution (using Cramer's rule)
      if determinant == 0:
            if determinant_x == determinant_y == 0:
                  raise ValueError("""Infinite solutions. (Consistent system)"""						)
            else:
                  raise ValueError("""No solution. (Inconsistent system)"""						)
      else:
            if determinant_x == determinant_y == 0:
                  # Trivial solution (Inconsistent system)
                  return (0.0, 0.0)
            else:
                  UpperCamelCase_					: List[Any]            =				determinant_x / determinant
                  UpperCamelCase_					: List[str]            =				determinant_y / determinant
                  # Non-Trivial Solution (Consistent system)
                  return (x, y)






