





import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


_A   :       List[str]    					=    logging.getLogger()


@unittest.skip("Temporarily disable the doc tests."   )
@require_torch
@require_tf
@slow
class   __SCREAMING_SNAKE_CASE     (					unittest.TestCase   ):







       def 		__lowerCamelCase       ( self	:      List[str]						,   A	:      Path						,   A	:      Union[str, None] = None						,   A	:      Union[List[str], None] = None						,   A	:      Union[str, List[str], None] = None						,   A	:      bool = True						,   )  ->str:
          lowerCamelCase__  :		Tuple     =				[file for file in os.listdir(__lowerCAmelCase     ) if os.path.isfile(os.path.join(__lowerCAmelCase						,   __lowerCAmelCase     )     )]

          if identifier is not None:
             lowerCamelCase__  :		List[Any]     =				[file for file in files if identifier in file]

          if n_identifier is not None:
             if isinstance(__lowerCAmelCase						,   __lowerCAmelCase     ):
                for n_ in n_identifier:
                   lowerCamelCase__  :		Optional[Any]     =				[file for file in files if n_ not in file]
             else:
                lowerCamelCase__  :		Any     =				[file for file in files if n_identifier not in file]

          lowerCamelCase__  :		Optional[int]     =				ignore_files or []
          ignore_files.append('''__init__.py'''     )
          lowerCamelCase__  :		int     =				[file for file in files if file not in ignore_files]

          for file in files:
             # Open all files
             print('''Testing'''						,   __lowerCAmelCase     )

             if only_modules:
                lowerCamelCase__  :		Optional[int]     =				file.split('''.'''     )[0]
                try:
                   lowerCamelCase__  :		Optional[int]     =				getattr(__lowerCAmelCase						,   __lowerCAmelCase     )
                   lowerCamelCase__  :		int     =				doctest.DocTestSuite(__lowerCAmelCase     )
                   lowerCamelCase__  :		Tuple     =				unittest.TextTestRunner().run(__lowerCAmelCase     )
                   self.assertIs(len(result.failures     )						,   0     )
                except AttributeError:
                   logger.info(F"{module_identifier} is not a module."     )
             else:
                lowerCamelCase__  :		Union[str, Any]     =				doctest.testfile(str('''..''' / directory / file     )						,   optionflags=doctest.ELLIPSIS     )
                self.assertIs(result.failed						,   0     )







       def 		__lowerCamelCase       ( self	:      List[Any]     )  ->Any:
          lowerCamelCase__  :		Tuple     =				Path('''src/transformers'''     )
          lowerCamelCase__  :		Dict     =				'''modeling'''
          lowerCamelCase__  :		Dict     =				[
              '''modeling_ctrl.py''',
              '''modeling_tf_ctrl.py''',
          ]
          self.analyze_directory(__lowerCAmelCase						,   identifier=__lowerCAmelCase						,   ignore_files=__lowerCAmelCase     )







       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->Union[str, Any]:
          lowerCamelCase__  :		int     =				Path('''src/transformers'''     )
          lowerCamelCase__  :		Optional[Any]     =				'''tokenization'''
          self.analyze_directory(__lowerCAmelCase						,   identifier=__lowerCAmelCase     )







       def 		__lowerCamelCase       ( self	:      List[str]     )  ->Optional[int]:
          lowerCamelCase__  :		Any     =				Path('''src/transformers'''     )
          lowerCamelCase__  :		Tuple     =				'''configuration'''
          self.analyze_directory(__lowerCAmelCase						,   identifier=__lowerCAmelCase     )







       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->Tuple:
          lowerCamelCase__  :		str     =				Path('''src/transformers'''     )
          lowerCamelCase__  :		Tuple     =				['''configuration''', '''modeling''', '''tokenization''']
          self.analyze_directory(__lowerCAmelCase						,   n_identifier=__lowerCAmelCase     )







       def 		__lowerCamelCase       ( self	:      Any     )  ->int:
          lowerCamelCase__  :		List[str]     =				Path('''docs/source'''     )
          lowerCamelCase__  :		Tuple     =				['''favicon.ico''']
          self.analyze_directory(__lowerCAmelCase						,   ignore_files=__lowerCAmelCase						,   only_modules=__lowerCAmelCase     )










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


@dataclass
class     A     :
			'''simple docstring'''




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






			def   a_			(     self  :       Union[str, Any]  )      ->      Any:







					"""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     A     :
			'''simple docstring'''




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


class     A     :
			'''simple docstring'''




			def __init__(     self  :       str				,      __lowerCAmelCase  :       Optional[Any]=1_92				,      __lowerCAmelCase  :       Tuple=32				,      __lowerCAmelCase  :       str=4				,      __lowerCAmelCase  :       Union[str, Any]=0.6  )      ->      Union[str, Any]:







					"""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  :       int  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  np.random.permutation(self.token_count  )[: self.mask_count]
					A__     =  np.zeros(self.token_count				,      dtype=__lowerCAmelCase  )
					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   __lowerCamelCase   (				__a  :Optional[Any]       )						->							Union[str, Any]:






		"""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   __lowerCamelCase   (				)						->							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"""       ,	__a       ,	__a       )

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

		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 __a       : 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(__a  :Any       ):
				A__     =  [transforms(__a       ) 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(__a       )

		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(__a       )

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

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

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


if __name__ == "__main__":
					main()



