




'''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
A									=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')

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


@dataclass
class     _a						:
       __a       :  Optional[str]            =      field(
           default="""cifar10"""							,							metadata={"""help""": """Name of a dataset from the datasets package"""}				)
       __a       :  Optional[str]            =      field(
           default=__a							,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}				)
       __a       :  Optional[str]            =      field(
           default=__a							,							metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}							,							)
       __a       :  Optional[str]            =      field(default=__a							,							metadata={"""help""": """A folder containing the training data."""}				)
       __a       :  Optional[str]            =      field(default=__a							,							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       :  int            =      field(default=32							,							metadata={"""help""": """The size of the square patches to use for masking."""}				)
       __a       :  float            =      field(
           default=0.6							,							metadata={"""help""": """Percentage of patches to mask."""}							,							)
       __a       :  Optional[int]            =      field(
           default=__a							,							metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of training examples to this """
                   """value if set."""
               )
           }							,							)
       __a       :  Optional[int]            =      field(
           default=__a							,							metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                   """value if set."""
               )
           }							,							)
       def 					A   (			self		:			int       ):



           '''simple docstring'''

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






@dataclass
class     _a						:
       __a       :  str            =      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."""
               )
           }							,							)
       __a       :  Optional[str]            =      field(
           default=__a							,							metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__a				)}							,							)
       __a       :  Optional[str]            =      field(
           default=__a							,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
       __a       :  Optional[str]            =      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"""
               )
           }							,							)
       __a       :  Optional[str]            =      field(
           default=__a							,							metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}							,							)
       __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=__a							,							metadata={"""help""": """Name or path of preprocessor config."""}				)
       __a       :  bool            =      field(
           default=__a							,							metadata={
               """help""": (
                   """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                   """with private models)."""
               )
           }							,							)
       __a       :  Optional[int]            =      field(
           default=__a							,							metadata={
               """help""": (
                   """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
               )
           }							,							)
       __a       :  Optional[int]            =      field(
           default=__a							,							metadata={
               """help""": (
                   """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
               )
           }							,							)
       __a       :  Optional[int]            =      field(
           default=__a							,							metadata={"""help""": """Stride to use for the encoder."""}							,							)






class     _a						:
       def __init__(			self		:			List[Any]     ,       lowercase		:			Union[str, Any]=192     ,       lowercase		:			Union[str, Any]=32     ,       lowercase		:			Optional[int]=4     ,       lowercase		:			Union[str, Any]=0.6       ):



           '''simple docstring'''

           UpperCAmelCase     =	input_size
           UpperCAmelCase     =	mask_patch_size
           UpperCAmelCase     =	model_patch_size
           UpperCAmelCase     =	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'''       )

           UpperCAmelCase     =	self.input_size // self.mask_patch_size
           UpperCAmelCase     =	self.mask_patch_size // self.model_patch_size

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







       def __call__(			self		:			Dict       ):



           '''simple docstring'''

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

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

           return torch.tensor(mask.flatten()       )






def 		snake_case_							(_a			:							Any  ):
    UpperCAmelCase     =	torch.stack([example['''pixel_values'''] for example in examples]  )
    UpperCAmelCase     =	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.

    UpperCAmelCase     =	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.
        UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
    else:
        UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	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'''					,     _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     =	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     =	None
    if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
        UpperCAmelCase     =	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     =	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     =	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     =	ds['''train'''].train_test_split(data_args.train_val_split  )
        UpperCAmelCase     =	split['''train''']
        UpperCAmelCase     =	split['''test''']

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

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

    # create model
    if model_args.model_name_or_path:
        UpperCAmelCase     =	AutoModelForMaskedImageModeling.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     =	AutoModelForMaskedImageModeling.from_config(_a  )

    if training_args.do_train:
        UpperCAmelCase     =	ds['''train'''].column_names
    else:
        UpperCAmelCase     =	ds['''validation'''].column_names

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

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

    # Initialize our trainer
    UpperCAmelCase     =	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     =	None
        if training_args.resume_from_checkpoint is not None:
            UpperCAmelCase     =	training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            UpperCAmelCase     =	last_checkpoint
        UpperCAmelCase     =	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     =	trainer.evaluate()
        trainer.log_metrics('''eval'''					,     _a  )
        trainer.save_metrics('''eval'''					,     _a  )

    # Write model card and (optionally) push to hub
    UpperCAmelCase     =	{
        '''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(**_a  )
    else:
        trainer.create_model_card(**_a  )


if __name__ == "__main__":
   main()










'''simple docstring'''







from __future__ import annotations





def 		snake_case_							(_a			:							str					,     _a			:							list[str] | None = None  ):
    UpperCAmelCase     =	word_bank or []
    # create a table
    UpperCAmelCase     =	len(_a  ) + 1

    UpperCAmelCase     =	[]
    for _ in range(_a  ):
        table.append([]  )
    # seed value
    UpperCAmelCase     =	[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(_a  ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(_a  )] == word:
                    UpperCAmelCase     =	[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(_a  )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(_a  )]:
        combination.reverse()

    return table[len(_a  )]


if __name__ == "__main__":
   print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
   print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
   print(
       all_construct(
           'hexagonosaurus',
           ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
       )
   )




