





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
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')

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






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							Optional[str]     	=	field(
       default="cifar10"    ,       metadata={"help": "Name of a dataset from the datasets package"}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       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=__lowerCamelCase    ,       metadata={"help": "A folder containing the training data."}       )
   a		:							Optional[str]     	=	field(default=__lowerCamelCase    ,       metadata={"help": "A folder containing the validation data."}       )
   a		:							Optional[float]     	=	field(
       default=0.1_5    ,       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=__lowerCamelCase    ,       metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of training examples to this "
               "value if set."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }    ,       )




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   {}
       if self.train_dir is not None:
           UpperCamelCase__							:       Any      =   self.train_dir
       if self.validation_dir is not None:
           UpperCamelCase__							:       Optional[Any]      =   self.validation_dir
       UpperCamelCase__							:       List[Any]      =   data_files if data_files else None






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							str     	=	field(
       default=__lowerCamelCase    ,       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=__lowerCamelCase    ,       metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowerCamelCase       )}    ,       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       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=__lowerCamelCase    ,       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=__lowerCamelCase    ,       metadata={"help": "Name or path of preprocessor config."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       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=__lowerCamelCase    ,       metadata={
           "help": (
               "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Stride to use for the encoder."}    ,       )






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__=192,	__magic_name__=32,	__magic_name__=4,	__magic_name__=0.6							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   input_size
       UpperCamelCase__							:       str      =   mask_patch_size
       UpperCamelCase__							:       int      =   model_patch_size
       UpperCamelCase__							:       List[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'''							)

       UpperCamelCase__							:       Dict      =   self.input_size // self.mask_patch_size
       UpperCamelCase__							:       Dict      =   self.mask_patch_size // self.model_patch_size

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




   def __call__(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   np.random.permutation(self.token_count							)[: self.mask_count]
       UpperCamelCase__							:       Optional[int]      =   np.zeros(self.token_count,	dtype=__magic_name__							)
       UpperCamelCase__							:       Dict      =   1

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

       return torch.tensor(mask.flatten()							)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]   )   ->			Optional[Any]:
    UpperCamelCase__							:       Any      =   torch.stack([example['''pixel_values'''] for example in examples]   )
    UpperCamelCase__							:       Any      =   torch.stack([example['''mask'''] for example in examples]   )
    return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def 	lowerCAmelCase_					(						)   ->			Any:
    # 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__							:       str      =   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__							:       str      =   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_mim'''	,     __UpperCAmelCase	,     __UpperCAmelCase   )

    # 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__							:       Tuple      =   training_args.get_process_log_level()
    logger.setLevel(__UpperCAmelCase   )
    transformers.utils.logging.set_verbosity(__UpperCAmelCase   )
    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__							:       List[str]      =   None
    if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
        UpperCamelCase__							:       List[str]      =   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__							:       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.
    UpperCamelCase__							:       List[Any]      =   None if '''validation''' in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split	,     __UpperCAmelCase   ) and data_args.train_val_split > 0.0:
        UpperCamelCase__							:       int      =   ds['''train'''].train_test_split(data_args.train_val_split   )
        UpperCamelCase__							:       Union[str, Any]      =   split['''train''']
        UpperCamelCase__							:       List[Any]      =   split['''test''']

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    UpperCamelCase__							:       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:
        UpperCamelCase__							:       Any      =   AutoConfig.from_pretrained(model_args.config_name_or_path	,     **__UpperCAmelCase   )
    elif model_args.model_name_or_path:
        UpperCamelCase__							:       List[Any]      =   AutoConfig.from_pretrained(model_args.model_name_or_path	,     **__UpperCAmelCase   )
    else:
        UpperCamelCase__							:       Tuple      =   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(__UpperCAmelCase	,     '''decoder_type'''   ):
        UpperCamelCase__							:       int      =   '''simmim'''

    # adapt config
    UpperCamelCase__							:       List[str]      =   model_args.image_size if model_args.image_size is not None else config.image_size
    UpperCamelCase__							:       Dict      =   model_args.patch_size if model_args.patch_size is not None else config.patch_size
    UpperCamelCase__							:       int      =   (
        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__							:       Union[str, Any]      =   AutoImageProcessor.from_pretrained(model_args.image_processor_name	,     **__UpperCAmelCase   )
    elif model_args.model_name_or_path:
        UpperCamelCase__							:       List[str]      =   AutoImageProcessor.from_pretrained(model_args.model_name_or_path	,     **__UpperCAmelCase   )
    else:
        UpperCamelCase__							:       List[str]      =   {
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        UpperCamelCase__							:       List[Any]      =   IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    # create model
    if model_args.model_name_or_path:
        UpperCamelCase__							:       Optional[int]      =   AutoModelForMaskedImageModeling.from_pretrained(
            model_args.model_name_or_path	,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path   )	,     config=__UpperCAmelCase	,     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__							:       List[str]      =   AutoModelForMaskedImageModeling.from_config(__UpperCAmelCase   )

    if training_args.do_train:
        UpperCamelCase__							:       List[str]      =   ds['''train'''].column_names
    else:
        UpperCamelCase__							:       int      =   ds['''validation'''].column_names

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

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

    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__							:       List[Any]      =   (
                ds['''validation'''].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
            )
        # Set the validation transforms
        ds["validation"].set_transform(__UpperCAmelCase   )

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

    # Training
    if training_args.do_train:
        UpperCamelCase__							:       int      =   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__							:       Dict      =   trainer.train(resume_from_checkpoint=__UpperCAmelCase   )
        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__							:       str      =   trainer.evaluate()
        trainer.log_metrics('''eval'''	,     __UpperCAmelCase   )
        trainer.save_metrics('''eval'''	,     __UpperCAmelCase   )

    # Write model card and (optionally) push to hub
    UpperCamelCase__							:       List[str]      =   {
        '''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(**__UpperCAmelCase   )
    else:
        trainer.create_model_card(**__UpperCAmelCase   )


if __name__ == "__main__":
  main()








def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict   )   ->			set:
    UpperCamelCase__							:       int      =   set()
    # edges = list of graph's edges
    UpperCamelCase__							:       str      =   get_edges(__UpperCAmelCase   )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   edges.pop()
        chosen_vertices.add(__UpperCAmelCase   )
        chosen_vertices.add(__UpperCAmelCase   )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(__UpperCAmelCase   )
    return chosen_vertices




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict   )   ->			set:
    UpperCamelCase__							:       Optional[int]      =   set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)   )
    return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")

