






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   :							int						= 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   :							Optional[int]						= list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__a   :							List[Any]						= tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class    __lowercase   :







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




     def 		lowerCAmelCase_  (					self							:      str					):


         """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    __lowercase   :







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


class    __lowercase   :







     '''simple docstring'''




     def __init__(					self							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]=192			,						UpperCamelCase_							:      Any=32			,						UpperCamelCase_							:      int=4			,						UpperCamelCase_							:      List[Any]=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							:      Optional[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       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   Optional[int]:





    """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       _SCREAMING_SNAKE_CASE    (			)    ->   Union[str, Any]:





    """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 :      Optional[Any]     ):
        __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()












from ....configuration_utils import PretrainedConfig
from ....utils import logging


__a   :							List[Any]						= logging.get_logger(__name__)

# TODO: upload to AWS
__a   :							Union[str, Any]						= {
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"retribert"




     def __init__(					self							:      Any			,						UpperCamelCase_							:      int=30_522			,						UpperCamelCase_							:      Optional[int]=768			,						UpperCamelCase_							:      Union[str, Any]=8			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      List[Any]=3_072			,						UpperCamelCase_							:      Tuple="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      Dict=0.1			,						UpperCamelCase_							:      List[str]=512			,						UpperCamelCase_							:      Optional[int]=2			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      List[str]=1e-12			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=128			,						UpperCamelCase_							:      Union[str, Any]=0			,						**UpperCamelCase_							:      List[str]			,						):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_			,						**UpperCamelCase_					)

         __A       =						vocab_size
         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						hidden_act
         __A       =						intermediate_size
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						max_position_embeddings
         __A       =						type_vocab_size
         __A       =						initializer_range
         __A       =						layer_norm_eps
         __A       =						share_encoders
         __A       =						projection_dim




