



import argparse
import struct
import unittest





class  __magic_name__   :






		def __init__(						self      :     List[str]				,		UpperCamelCase__      :     bytes      )				->					None:
								'''simple docstring'''







								UpperCAmelCase         =     data

								# Initialize hash values
								UpperCAmelCase         =     [
								    0X6A09E667,
								    0XBB67AE85,
								    0X3C6EF372,
								    0XA54FF53A,
								    0X510E527F,
								    0X9B05688C,
								    0X1F83D9AB,
								    0X5BE0CD19,
								]

								# Initialize round constants
								UpperCAmelCase         =     [
								    0X428A2F98,
								    0X71374491,
								    0XB5C0FBCF,
								    0XE9B5DBA5,
								    0X3956C25B,
								    0X59F111F1,
								    0X923F82A4,
								    0XAB1C5ED5,
								    0XD807AA98,
								    0X12835B01,
								    0X243185BE,
								    0X550C7DC3,
								    0X72BE5D74,
								    0X80DEB1FE,
								    0X9BDC06A7,
								    0XC19BF174,
								    0XE49B69C1,
								    0XEFBE4786,
								    0X0FC19DC6,
								    0X240CA1CC,
								    0X2DE92C6F,
								    0X4A7484AA,
								    0X5CB0A9DC,
								    0X76F988DA,
								    0X983E5152,
								    0XA831C66D,
								    0XB00327C8,
								    0XBF597FC7,
								    0XC6E00BF3,
								    0XD5A79147,
								    0X06CA6351,
								    0X14292967,
								    0X27B70A85,
								    0X2E1B2138,
								    0X4D2C6DFC,
								    0X53380D13,
								    0X650A7354,
								    0X766A0ABB,
								    0X81C2C92E,
								    0X92722C85,
								    0XA2BFE8A1,
								    0XA81A664B,
								    0XC24B8B70,
								    0XC76C51A3,
								    0XD192E819,
								    0XD6990624,
								    0XF40E3585,
								    0X106AA070,
								    0X19A4C116,
								    0X1E376C08,
								    0X2748774C,
								    0X34B0BCB5,
								    0X391C0CB3,
								    0X4ED8AA4A,
								    0X5B9CCA4F,
								    0X682E6FF3,
								    0X748F82EE,
								    0X78A5636F,
								    0X84C87814,
								    0X8CC70208,
								    0X90BEFFFA,
								    0XA4506CEB,
								    0XBEF9A3F7,
								    0XC67178F2,
								]

								UpperCAmelCase         =     self.preprocessing(self.data      )
								self.final_hash()






		@staticmethod
		def SCREAMING_SNAKE_CASE_			(						UpperCamelCase__      :     bytes      )				->					bytes:
								'''simple docstring'''







								UpperCAmelCase         =     b"\x80" + (b"\x00" * (63 - (len(UpperCamelCase__      ) + 8) % 64))
								UpperCAmelCase         =     struct.pack(">Q"				,		(len(UpperCamelCase__      ) * 8)      )
								return data + padding + big_endian_integer






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					None:
								'''simple docstring'''







								UpperCAmelCase         =     [
								    self.preprocessed_data[x : x + 64]
								    for x in range(0				,		len(self.preprocessed_data      )				,		64      )
								]

								for block in self.blocks:
														# Convert the given block into a list of 4 byte integers
														UpperCAmelCase         =     list(struct.unpack(">16L"				,		UpperCamelCase__      )      )
														# add 48 0-ed integers
														words += [0] * 48

														UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     self.hashes

														for index in range(0				,		64      ):
																				if index > 15:
																										# modify the zero-ed indexes at the end of the array
																										UpperCAmelCase         =     (
																										    self.ror(words[index - 15]				,		7      )
																										    ^ self.ror(words[index - 15]				,		18      )
																										    ^ (words[index - 15] >> 3)
																										)
																										UpperCAmelCase         =     (
																										    self.ror(words[index - 2]				,		17      )
																										    ^ self.ror(words[index - 2]				,		19      )
																										    ^ (words[index - 2] >> 10)
																										)

																										UpperCAmelCase         =     (
																										    words[index - 16] + sa + words[index - 7] + sa
																										) % 0X100000000

																				# Compression
																				UpperCAmelCase         =     self.ror(UpperCamelCase__				,		6      ) ^ self.ror(UpperCamelCase__				,		11      ) ^ self.ror(UpperCamelCase__				,		25      )
																				UpperCAmelCase         =     (e & f) ^ ((~e & 0XFFFFFFFF) & g)
																				UpperCAmelCase         =     (
																				    h + sa + ch + self.round_constants[index] + words[index]
																				) % 0X100000000
																				UpperCAmelCase         =     self.ror(UpperCamelCase__				,		2      ) ^ self.ror(UpperCamelCase__				,		13      ) ^ self.ror(UpperCamelCase__				,		22      )
																				UpperCAmelCase         =     (a & b) ^ (a & c) ^ (b & c)
																				UpperCAmelCase         =     (sa + maj) % 0X100000000

																				UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     (
																				    g,
																				    f,
																				    e,
																				    ((d + tempa) % 0X100000000),
																				    c,
																				    b,
																				    a,
																				    ((tempa + tempa) % 0X100000000),
																				)

														UpperCAmelCase         =     [a, b, c, d, e, f, g, h]

														# Modify final values
														UpperCAmelCase         =     [
														    ((element + mutated_hash_values[index]) % 0X100000000)
														    for index, element in enumerate(self.hashes      )
														]

								UpperCAmelCase         =     "".join([hex(UpperCamelCase__      )[2:].zfill(8      ) for value in self.hashes]      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     int      )				->					int:
								'''simple docstring'''







								return 0XFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)





class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					None:
								'''simple docstring'''







								import hashlib

								UpperCAmelCase         =     bytes("Test String"				,		"utf-8"      )
								self.assertEqual(SHAaaa(UpperCamelCase__      ).hash				,		hashlib.shaaaa(UpperCamelCase__      ).hexdigest()      )

def    lowerCamelCase_() ->       None:
						import doctest

						doctest.testmod()

						UpperCAmelCase         =     argparse.ArgumentParser()
						parser.add_argument(
						    "-s"    ,   "--string"    ,   dest="input_string"    ,   default="Hello World!! Welcome to Cryptography"    ,   help="Hash the string"    ,   )
						parser.add_argument(
						    "-f"    ,   "--file"    ,   dest="input_file"    ,   help="Hash contents of a file"    )

						UpperCAmelCase         =     parser.parse_args()

						UpperCAmelCase         =     args.input_string

						# hash input should be a bytestring
						if args.input_file:
												with open(args.input_file    ,   "rb"    ) as f:
																		UpperCAmelCase         =     f.read()
						else:
												UpperCAmelCase         =     bytes(lowerCamelCase_    ,   "utf-8"    )

						print(SHAaaa(lowerCamelCase_    ).hash    )


if __name__ == "__main__":
		main()








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     :							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")

__lowerCamelCase     :							List[str]  							=				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__lowerCamelCase     :							List[str]  							=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class  __magic_name__   :
		lowercase      :       Optional[str]          =field(
		    default='''cifar10''',  metadata={'''help''': '''Name of a dataset from the datasets package'''}						)
		lowercase      :       Optional[str]          =field(
		    default=A__,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
		lowercase      :       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\'.'''},  )
		lowercase      :       Optional[str]          =field(default=A__,  metadata={'''help''': '''A folder containing the training data.'''}						)
		lowercase      :       Optional[str]          =field(default=A__,  metadata={'''help''': '''A folder containing the validation data.'''}						)
		lowercase      :       Optional[float]          =field(
		    default=0.15,  metadata={'''help''': '''Percent to split off of train for validation.'''}						)
		lowercase      :       int          =field(default=32,  metadata={'''help''': '''The size of the square patches to use for masking.'''}						)
		lowercase      :       float          =field(
		    default=0.6,  metadata={'''help''': '''Percentage of patches to mask.'''},  )
		lowercase      :       Optional[int]          =field(
		    default=A__,  metadata={
		        '''help''': (
		            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
		            '''value if set.'''
		        )
		    },  )
		lowercase      :       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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''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  __magic_name__   :
		lowercase      :       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.'''
		        )
		    },  )
		lowercase      :       Optional[str]          =field(
		    default=A__,  metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A__						)},  )
		lowercase      :       Optional[str]          =field(
		    default=A__,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
		lowercase      :       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'''
		        )
		    },  )
		lowercase      :       Optional[str]          =field(
		    default=A__,  metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''},  )
		lowercase      :       str          =field(
		    default='''main''',  metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''},  )
		lowercase      :       str          =field(default=A__,  metadata={'''help''': '''Name or path of preprocessor config.'''}						)
		lowercase      :       bool          =field(
		    default=A__,  metadata={
		        '''help''': (
		            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
		            '''with private models).'''
		        )
		    },  )
		lowercase      :       Optional[int]          =field(
		    default=A__,  metadata={
		        '''help''': (
		            '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
		        )
		    },  )
		lowercase      :       Optional[int]          =field(
		    default=A__,  metadata={
		        '''help''': (
		            '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
		        )
		    },  )
		lowercase      :       Optional[int]          =field(
		    default=A__,  metadata={'''help''': '''Stride to use for the encoder.'''},  )





class  __magic_name__   :






		def __init__(						self      :     List[Any]				,		UpperCamelCase__      :     Optional[int]=1_92				,		UpperCamelCase__      :     Optional[int]=32				,		UpperCamelCase__      :     Optional[Any]=4				,		UpperCamelCase__      :     Any=0.6      )				->					Union[str, Any]:
								'''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      :     Optional[int]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     np.random.permutation(self.token_count      )[: self.mask_count]
								UpperCAmelCase         =     np.zeros(self.token_count				,		dtype=UpperCamelCase__      )
								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    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[int]:
						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    lowerCamelCase_() ->       Union[str, 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         =     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"    ,   lowerCamelCase_    ,   lowerCamelCase_    )

						# 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(lowerCamelCase_    )
						transformers.utils.logging.set_verbosity(lowerCamelCase_    )
						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    ,   lowerCamelCase_    ) 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    ,   **lowerCamelCase_    )
						elif model_args.model_name_or_path:
												UpperCAmelCase         =     AutoConfig.from_pretrained(model_args.model_name_or_path    ,   **lowerCamelCase_    )
						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(lowerCamelCase_    ,   "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    ,   **lowerCamelCase_    )
						elif model_args.model_name_or_path:
												UpperCAmelCase         =     AutoImageProcessor.from_pretrained(model_args.model_name_or_path    ,   **lowerCamelCase_    )
						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=lowerCamelCase_    ,   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(lowerCamelCase_    )

						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 lowerCamelCase_    : 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(lowerCamelCase_    ):
												UpperCAmelCase         =     [transforms(lowerCamelCase_    ) 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(lowerCamelCase_    )

						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(lowerCamelCase_    )

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

						# 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=lowerCamelCase_    )
												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"    ,   lowerCamelCase_    )
												trainer.save_metrics("eval"    ,   lowerCamelCase_    )

						# 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(**lowerCamelCase_    )
						else:
												trainer.create_model_card(**lowerCamelCase_    )


if __name__ == "__main__":
		main()



