


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
_snake_case					:		Any      =    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")

_snake_case					:		Dict      =    list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_snake_case					:		Optional[int]      =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[str]					  =  field(
							    default="cifar10"					,    metadata={"help": "Name of a dataset from the datasets package"}				)
							__UpperCAmelCase   :							Optional[str]					  =  field(
							    default=_lowerCAmelCase					,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
							__UpperCAmelCase   :							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'."}					,    )
							__UpperCAmelCase   :							Optional[str]					  =  field(default=_lowerCAmelCase					,    metadata={"help": "A folder containing the training data."}				)
							__UpperCAmelCase   :							Optional[str]					  =  field(default=_lowerCAmelCase					,    metadata={"help": "A folder containing the validation data."}				)
							__UpperCAmelCase   :							Optional[float]					  =  field(
							    default=0.15					,    metadata={"help": "Percent to split off of train for validation."}				)
							__UpperCAmelCase   :							int					  =  field(default=32					,    metadata={"help": "The size of the square patches to use for masking."}				)
							__UpperCAmelCase   :							float					  =  field(
							    default=0.6					,    metadata={"help": "Percentage of patches to mask."}					,    )
							__UpperCAmelCase   :							Optional[int]					  =  field(
							    default=_lowerCAmelCase					,    metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }					,    )
							__UpperCAmelCase   :							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    __snake_case (							self	:  List[str]    )     ->   Optional[Any]:
														__snake_case		:      str										=       {}
														if self.train_dir is not None:
																					__snake_case		:      List[Any]										=       self.train_dir
														if self.validation_dir is not None:
																					__snake_case		:      Union[str, Any]										=       self.validation_dir
														__snake_case		:      str										=       data_files if data_files else None

@dataclass
class     a		:




							"""simple docstring"""


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

class     a		:




							"""simple docstring"""


							def __init__(							self	:  Optional[Any]   ,     lowerCamelCase	:  Union[str, Any]=192   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[Any]=4   ,     lowerCamelCase	:  str=0.6    )     ->   Union[str, Any]:
														__snake_case		:      Dict										=       input_size
														__snake_case		:      Any										=       mask_patch_size
														__snake_case		:      Optional[int]										=       model_patch_size
														__snake_case		:      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"    )

														__snake_case		:      Optional[Any]										=       self.input_size // self.mask_patch_size
														__snake_case		:      Optional[Any]										=       self.mask_patch_size // self.model_patch_size

														__snake_case		:      Any										=       self.rand_size**2
														__snake_case		:      List[Any]										=       int(np.ceil(self.token_count * self.mask_ratio    )    )






							def __call__(							self	:  Optional[int]    )     ->   Dict:
														__snake_case		:      Optional[int]										=       np.random.permutation(self.token_count    )[: self.mask_count]
														__snake_case		:      Union[str, Any]										=       np.zeros(self.token_count   ,     dtype=_lowercase    )
														__snake_case		:      Any										=       1

														__snake_case		:      Any										=       mask.reshape((self.rand_size, self.rand_size)    )
														__snake_case		:      str										=       mask.repeat(self.scale   ,     axis=0    ).repeat(self.scale   ,     axis=1    )

														return torch.tensor(mask.flatten()    )







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       torch.stack([example["pixel_values"] for example in examples]     )
							__snake_case		:      int										=       torch.stack([example["mask"] for example in examples]     )
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def     lowerCAmelCase_					( ):
							# 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.

							__snake_case		:      Optional[int]										=       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.
														__snake_case			,      __snake_case			,      __snake_case		:      Union[str, Any]										=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
							else:
														__snake_case			,      __snake_case			,      __snake_case		:      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"							,     snake_case_							,     snake_case_     )

							# 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()

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

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							__snake_case		:      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:
														__snake_case		:      str										=       AutoConfig.from_pretrained(model_args.config_name_or_path							,     **snake_case_     )
							elif model_args.model_name_or_path:
														__snake_case		:      str										=       AutoConfig.from_pretrained(model_args.model_name_or_path							,     **snake_case_     )
							else:
														__snake_case		:      Union[str, Any]										=       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(snake_case_							,     "decoder_type"     ):
														__snake_case		:      Optional[int]										=       "simmim"

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

							# create model
							if model_args.model_name_or_path:
														__snake_case		:      Optional[Any]										=       AutoModelForMaskedImageModeling.from_pretrained(
														    model_args.model_name_or_path							,     from_tf=bool(".ckpt" in model_args.model_name_or_path     )							,     config=snake_case_							,     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"     )
														__snake_case		:      Any										=       AutoModelForMaskedImageModeling.from_config(snake_case_     )

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

							if data_args.image_column_name is not None:
														__snake_case		:      str										=       data_args.image_column_name
							elif "image" in column_names:
														__snake_case		:      Tuple										=       "image"
							elif "img" in column_names:
														__snake_case		:      str										=       "img"
							else:
														__snake_case		:      List[str]										=       column_names[0]

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							__snake_case		:      Dict										=       Compose(
							    [
							        Lambda(lambda __lowerCamelCase     : img.convert("RGB"     ) if img.mode != "RGB" else img     ),
							        RandomResizedCrop(model_args.image_size							,     scale=(0.6_7, 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
							__snake_case		:      Any										=       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(__lowerCamelCase     ):
														__snake_case		:      List[str]										=       [transforms(snake_case_     ) for image in examples[image_column_name]]
														__snake_case		:      Any										=       [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:
																					__snake_case		:      Union[str, Any]										=       ds["train"].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
														# Set the training transforms
														ds["train"].set_transform(snake_case_     )

							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:
																					__snake_case		:      List[str]										=       (
																					    ds["validation"].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
																					)
														# Set the validation transforms
														ds["validation"].set_transform(snake_case_     )

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

							# Training
							if training_args.do_train:
														__snake_case		:      Union[str, Any]										=       None
														if training_args.resume_from_checkpoint is not None:
																					__snake_case		:      List[Any]										=       training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					__snake_case		:      Dict										=       last_checkpoint
														__snake_case		:      Optional[int]										=       trainer.train(resume_from_checkpoint=snake_case_     )
														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:
														__snake_case		:      int										=       trainer.evaluate()
														trainer.log_metrics("eval"							,     snake_case_     )
														trainer.save_metrics("eval"							,     snake_case_     )

							# Write model card and (optionally) push to hub
							__snake_case		:      List[Any]										=       {
							    "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(**snake_case_     )
							else:
														trainer.create_model_card(**snake_case_     )


if __name__ == "__main__":
			main()





from scipy.stats import spearmanr

import datasets


_snake_case					:		Optional[Any]      =    "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"

_snake_case					:		Dict      =    "\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {'spearmanr': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results['spearmanr'])\n        -0.7\n        >>> print(round(results['spearmanr_pvalue'], 2))\n        0.19\n"

_snake_case					:		Optional[Any]      =    R"\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION				)
class     a		(datasets.Metric				):




							"""simple docstring"""


							def    __snake_case (							self	:  Optional[int]    )     ->   Any:
														return datasets.MetricInfo(
														    description=_DESCRIPTION   ,     citation=_CITATION   ,     inputs_description=_KWARGS_DESCRIPTION   ,     features=datasets.Features(
														        {
														            "predictions": datasets.Value("float"    ),
														            "references": datasets.Value("float"    ),
														        }    )   ,     reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"]   ,     )






							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Tuple=False    )     ->   Dict:
														__snake_case		:      Optional[int]										=       spearmanr(lowerCamelCase   ,     lowerCamelCase    )
														if return_pvalue:
																					return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
														else:
																					return {"spearmanr": results[0]}

