






'''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
lowerCamelCase_												=					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''')

lowerCamelCase_												=					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCamelCase_												=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class    _UpperCAmelCase			:




   """simple docstring"""

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

   def 						lowerCAmelCase (		self :	Any		):





         '''simple docstring'''





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





@dataclass
class    _UpperCAmelCase			:




   """simple docstring"""

   snake_case    				=						field(
       default=snake_case_							,    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.'''
           )
       }							,    )
   snake_case    				=						field(
       default=snake_case_							,    metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_						)}							,    )
   snake_case    				=						field(
       default=snake_case_							,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
   snake_case    				=						field(
       default=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'''
           )
       }							,    )
   snake_case    				=						field(
       default=snake_case_							,    metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}							,    )
   snake_case    				=						field(
       default='''main'''							,    metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}							,    )
   snake_case    				=						field(default=snake_case_							,    metadata={'''help''': '''Name or path of preprocessor config.'''}						)
   snake_case    				=						field(
       default=snake_case_							,    metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       }							,    )
   snake_case    				=						field(
       default=snake_case_							,    metadata={
           '''help''': (
               '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
           )
       }							,    )
   snake_case    				=						field(
       default=snake_case_							,    metadata={
           '''help''': (
               '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
           )
       }							,    )
   snake_case    				=						field(
       default=snake_case_							,    metadata={'''help''': '''Stride to use for the encoder.'''}							,    )





class    _UpperCAmelCase			:




   """simple docstring"""

   def __init__(		self :	Tuple      ,      __UpperCAmelCase :	Optional[int]=192      ,      __UpperCAmelCase :	Dict=32      ,      __UpperCAmelCase :	int=4      ,      __UpperCAmelCase :	int=0.6		):





         '''simple docstring'''





         _A			     =	input_size
         _A			     =	mask_patch_size
         _A			     =	model_patch_size
         _A			     =	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"		)

         _A			     =	self.input_size // self.mask_patch_size
         _A			     =	self.mask_patch_size // self.model_patch_size

         _A			     =	self.rand_size**2
         _A			     =	int(np.ceil(self.token_count * self.mask_ratio		)		)

   def __call__(		self :	Any		):





         '''simple docstring'''





         _A			     =	np.random.permutation(self.token_count		)[: self.mask_count]
         _A			     =	np.zeros(self.token_count      ,      dtype=__UpperCAmelCase		)
         _A			     =	1

         _A			     =	mask.reshape((self.rand_size, self.rand_size)		)
         _A			     =	mask.repeat(self.scale      ,      axis=0		).repeat(self.scale      ,      axis=1		)

         return torch.tensor(mask.flatten()		)





def 					__lowercase						(			__lowercase       )       ->						str:

      '''simple docstring'''


      _A			     =	torch.stack([example["pixel_values"] for example in examples]       )
      _A			     =	torch.stack([example["mask"] for example in examples]       )
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def 					__lowercase						(			)       ->						Dict:

      '''simple docstring'''


      _A			     =	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.
            _A     ,	_A     ,	_A			     =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
      else:
            _A     ,	_A     ,	_A			     =	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"   ,  __lowercase   ,  __lowercase       )

      # 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()

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

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      _A			     =	{
          "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:
            _A			     =	AutoConfig.from_pretrained(model_args.config_name_or_path   ,  **__lowercase       )
      elif model_args.model_name_or_path:
            _A			     =	AutoConfig.from_pretrained(model_args.model_name_or_path   ,  **__lowercase       )
      else:
            _A			     =	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(__lowercase   ,  "decoder_type"       ):
            _A			     =	"simmim"

      # adapt config
      _A			     =	model_args.image_size if model_args.image_size is not None else config.image_size
      _A			     =	model_args.patch_size if model_args.patch_size is not None else config.patch_size
      _A			     =	(
          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:
            _A			     =	AutoImageProcessor.from_pretrained(model_args.image_processor_name   ,  **__lowercase       )
      elif model_args.model_name_or_path:
            _A			     =	AutoImageProcessor.from_pretrained(model_args.model_name_or_path   ,  **__lowercase       )
      else:
            _A			     =	{
                conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
            }
            _A			     =	IMAGE_PROCESSOR_TYPES[model_args.model_type]()

      # create model
      if model_args.model_name_or_path:
            _A			     =	AutoModelForMaskedImageModeling.from_pretrained(
                model_args.model_name_or_path   ,  from_tf=bool(".ckpt" in model_args.model_name_or_path       )   ,  config=__lowercase   ,  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"       )
            _A			     =	AutoModelForMaskedImageModeling.from_config(__lowercase       )

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

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

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      _A			     =	Compose(
          [
              Lambda(lambda __lowercase       : 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
      _A			     =	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(__lowercase       ):
            _A			     =	[transforms(__lowercase       ) for image in examples[image_column_name]]
            _A			     =	[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:
                  _A			     =	ds["train"].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
            # Set the training transforms
            ds["train"].set_transform(__lowercase       )

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

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

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

      # Write model card and (optionally) push to hub
      _A			     =	{
          "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(**__lowercase       )
      else:
            trainer.create_model_card(**__lowercase       )


if __name__ == "__main__":
    main()







import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""simple docstring"""



				assert np.shape(a_						)[0] == np.shape(a_						)[1]
				# Ensure proper dimensionality.
				assert np.shape(a_						)[0] == np.shape(a_						)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(a_						) == np.iscomplexobj(a_						)
				__A   			= np.iscomplexobj(a_						)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(a_		,   input_matrix.conj().T						)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				__A   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

				while not convergence:
								# Multiple matrix by the vector.
								__A   			= np.dot(a_		,   a_						)
								# Normalize the resulting output vector.
								__A   			= w / np.linalg.norm(a_						)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								__A   			= vector.conj().T if is_complex else vector.T
								__A   			= np.dot(a_		,   np.dot(a_		,   a_						)						)

								# Check convergence.
								__A   			= np.abs(lambda_ - lambda_previous						) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__A   			= True

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__A   			= real_input_matrix
												__A   			= real_vector
								elif problem_type == "complex":
												__A   			= complex_input_matrix
												__A   			= complex_vector

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__A			,							__A   			= np.linalg.eigh(a_						)
								# Last eigenvalue is the maximum one.
								__A   			= eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__A   			= eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(a_						) - np.abs(a_						)						) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()
