





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							:       Optional[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")

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

@dataclass
class       A_			:
	UpperCAmelCase__   =     field(
	    default='''cifar10'''	,				metadata={'''help''': '''Name of a dataset from the datasets package'''}     )
	UpperCAmelCase__   =     field(
	    default=a_	,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}     )
	UpperCAmelCase__   =     field(
	    default=a_	,				metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}	,				)
	UpperCAmelCase__   =     field(default=a_	,				metadata={'''help''': '''A folder containing the training data.'''}     )
	UpperCAmelCase__   =     field(default=a_	,				metadata={'''help''': '''A folder containing the validation data.'''}     )
	UpperCAmelCase__   =     field(
	    default=0.15	,				metadata={'''help''': '''Percent to split off of train for validation.'''}     )
	UpperCAmelCase__   =     field(default=3_2	,				metadata={'''help''': '''The size of the square patches to use for masking.'''}     )
	UpperCAmelCase__   =     field(
	    default=0.6	,				metadata={'''help''': '''Percentage of patches to mask.'''}	,				)
	UpperCAmelCase__   =     field(
	    default=a_	,				metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
	            '''value if set.'''
	        )
	    }	,				)
	UpperCAmelCase__   =     field(
	    default=a_	,				metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
	            '''value if set.'''
	        )
	    }	,				)
	def 	_lowercase							(       self						):






				'''simple docstring'''







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

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

class       A_			:
	def __init__(       self				,   _A=1_9_2				,   _A=3_2				,   _A=4				,   _A=0.6						):






				'''simple docstring'''







				UpperCAmelCase  						=							input_size
				UpperCAmelCase  						=							mask_patch_size
				UpperCAmelCase  						=							model_patch_size
				UpperCAmelCase  						=							mask_ratio

				if self.input_size % self.mask_patch_size != 0:
							raise ValueError('''Input size must be divisible by mask patch size'''						)
				if self.mask_patch_size % self.model_patch_size != 0:
							raise ValueError('''Mask patch size must be divisible by model patch size'''						)

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

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







	def __call__(       self						):






				'''simple docstring'''







				UpperCAmelCase  						=							np.random.permutation(self.token_count						)[: self.mask_count]
				UpperCAmelCase  						=							np.zeros(self.token_count				,   dtype=lowerCamelCase__						)
				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 		__SCREAMING_SNAKE_CASE    (							UpperCamelCase__					)    ->							List[str]:







			'''simple docstring'''

			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 		__SCREAMING_SNAKE_CASE    (							)    ->							Dict:







			'''simple docstring'''

			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'''     ,     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  						=							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  						=							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     ,     UpperCamelCase__					) 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     ,     **UpperCamelCase__					)
			elif model_args.model_name_or_path:
						UpperCAmelCase  						=							AutoConfig.from_pretrained(model_args.model_name_or_path     ,     **UpperCamelCase__					)
			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(UpperCamelCase__     ,     '''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     ,     **UpperCamelCase__					)
			elif model_args.model_name_or_path:
						UpperCAmelCase  						=							AutoImageProcessor.from_pretrained(model_args.model_name_or_path     ,     **UpperCamelCase__					)
			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=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  						=							AutoModelForMaskedImageModeling.from_config(UpperCamelCase__					)

			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 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  						=							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__					):
						UpperCAmelCase  						=							[transforms(UpperCamelCase__					) 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(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  						=							(
									    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  						=							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  						=							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=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  						=							trainer.evaluate()
						trainer.log_metrics('''eval'''     ,     UpperCamelCase__					)
						trainer.save_metrics('''eval'''     ,     UpperCamelCase__					)

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


if __name__ == "__main__":
				main()









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.activations import gelu_new, gelu_python, get_activation







@require_torch
class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							__lowerCamelCase									=       get_activation('gelu'				)
							self.assertTrue(torch.allclose(gelu_python(lowerCamelCase__				)     ,		torch_builtin(lowerCamelCase__				)				)				)
							self.assertFalse(torch.allclose(gelu_python(lowerCamelCase__				)     ,		gelu_new(lowerCamelCase__				)				)				)


			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							__lowerCamelCase									=       get_activation('gelu'				)
							__lowerCamelCase									=       get_activation('gelu_10'				)

							__lowerCamelCase									=       torch_builtin(lowerCamelCase__				)
							__lowerCamelCase									=       geluaa(lowerCamelCase__				)

							__lowerCamelCase									=       torch.where(y_gelu_aa < 10.0     ,		1     ,		0				)

							self.assertTrue(torch.max(lowerCamelCase__				).item() == 10.0				)
							self.assertTrue(torch.allclose(y_gelu * clipped_mask     ,		y_gelu_aa * clipped_mask				)				)


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							get_activation('gelu'				)
							get_activation('gelu_10'				)
							get_activation('gelu_fast'				)
							get_activation('gelu_new'				)
							get_activation('gelu_python'				)
							get_activation('gelu_pytorch_tanh'				)
							get_activation('linear'				)
							get_activation('mish'				)
							get_activation('quick_gelu'				)
							get_activation('relu'				)
							get_activation('sigmoid'				)
							get_activation('silu'				)
							get_activation('swish'				)
							get_activation('tanh'				)
							with self.assertRaises(lowerCamelCase__				):
											get_activation('bogus'				)
							with self.assertRaises(lowerCamelCase__				):
											get_activation(lowerCamelCase__				)






			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       get_activation('gelu'				)
							__lowerCamelCase									=       1
							__lowerCamelCase									=       get_activation('gelu'				)
							self.assertEqual(acta.a     ,		1				)
							with self.assertRaises(lowerCamelCase__				):
											__lowerCamelCase									=       acta.a


