


"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
UpperCamelCase__				:Union[str, 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""")






@dataclass
class      A:

		"""simple docstring"""





		A    =							field(
		    default="cifar10"				,							metadata={"help": "Name of a dataset from the datasets package"}		)
		A    =							field(
		    default=lowerCamelCase__				,							metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}		)
		A    =							field(
		    default=lowerCamelCase__				,							metadata={"help": "The column name of the images in the files."}		)
		A    =							field(default=lowerCamelCase__				,							metadata={"help": "A folder containing the training data."}		)
		A    =							field(default=lowerCamelCase__				,							metadata={"help": "A folder containing the validation data."}		)
		A    =							field(
		    default=0.15				,							metadata={"help": "Percent to split off of train for validation."}		)
		A    =							field(
		    default=lowerCamelCase__				,							metadata={
		        "help": (
		            "For debugging purposes or quicker training, truncate the number of training examples to this "
		            "value if set."
		        )
		    }				,							)
		A    =							field(
		    default=lowerCamelCase__				,							metadata={
		        "help": (
		            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
		            "value if set."
		        )
		    }				,							)
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	{}
					if self.train_dir is not None:
								_UpperCamelCase					:List[str]					     =	self.train_dir
					if self.validation_dir is not None:
								_UpperCamelCase					:int					     =	self.validation_dir
					_UpperCamelCase					:int					     =	data_files if data_files else None






@dataclass
class      A:

		"""simple docstring"""





		A    =							field(
		    default=lowerCamelCase__				,							metadata={
		        "help": (
		            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
		        )
		    }				,							)
		A    =							field(
		    default=lowerCamelCase__				,							metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}		)
		A    =							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"
		        )
		    }				,							)
		A    =							field(
		    default=lowerCamelCase__				,							metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}		)
		A    =							field(
		    default="main"				,							metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}				,							)
		A    =							field(default=lowerCamelCase__				,							metadata={"help": "Name or path of preprocessor config."}		)
		A    =							field(
		    default=lowerCamelCase__				,							metadata={
		        "help": (
		            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
		            "with private models)."
		        )
		    }				,							)
		A    =							field(
		    default=0.75				,							metadata={"help": "The ratio of the number of masked tokens in the input sequence."}		)
		A    =							field(
		    default=lowerCamelCase__				,							metadata={"help": "Whether or not to train with normalized pixel values as target."}		)






@dataclass
class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							field(
		    default=1e-3				,							metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}		)





def 				A_       ( snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Any					     =	torch.stack([example['''pixel_values'''] for example in examples]		)
			return {"pixel_values": pixel_values}





def 				A_       ( )					->  int:
			# 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					:Union[str, Any]					     =	HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)		)
			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					:List[str]					     =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
			else:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:int					     =	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_mae'''       ,					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()

			_UpperCamelCase					: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.
			_UpperCamelCase					:Dict					     =	None
			if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
						_UpperCamelCase					:Any					     =	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					:Dict					     =	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					:Optional[int]					     =	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:
						_UpperCamelCase					:Tuple					     =	ds['''train'''].train_test_split(data_args.train_val_split		)
						_UpperCamelCase					:str					     =	split['''train''']
						_UpperCamelCase					:Tuple					     =	split['''test''']

			# Load pretrained model and image processor
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			_UpperCamelCase					:Dict					     =	{
			    '''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:
						_UpperCamelCase					:Dict					     =	ViTMAEConfig.from_pretrained(model_args.config_name       ,					**snake_case__		)
			elif model_args.model_name_or_path:
						_UpperCamelCase					:Optional[int]					     =	ViTMAEConfig.from_pretrained(model_args.model_name_or_path       ,					**snake_case__		)
			else:
						_UpperCamelCase					:List[Any]					     =	ViTMAEConfig()
						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}"		)

    # adapt config
			config.update(
			    {
			        '''mask_ratio''': model_args.mask_ratio,
			        '''norm_pix_loss''': model_args.norm_pix_loss,
			    }		)

			# create image processor
			if model_args.image_processor_name:
						_UpperCamelCase					:Union[str, Any]					     =	ViTImageProcessor.from_pretrained(model_args.image_processor_name       ,					**snake_case__		)
			elif model_args.model_name_or_path:
						_UpperCamelCase					:Any					     =	ViTImageProcessor.from_pretrained(model_args.model_name_or_path       ,					**snake_case__		)
			else:
						_UpperCamelCase					:Any					     =	ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						_UpperCamelCase					:Any					     =	ViTMAEForPreTraining.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'''		)
						_UpperCamelCase					:Dict					     =	ViTMAEForPreTraining(snake_case__		)

			if training_args.do_train:
						_UpperCamelCase					:str					     =	ds['''train'''].column_names
			else:
						_UpperCamelCase					:List[str]					     =	ds['''validation'''].column_names

			if data_args.image_column_name is not None:
						_UpperCamelCase					:Union[str, Any]					     =	data_args.image_column_name
			elif "image" in column_names:
						_UpperCamelCase					:Dict					     =	'''image'''
			elif "img" in column_names:
						_UpperCamelCase					:Tuple					     =	'''img'''
			else:
						_UpperCamelCase					:List[Any]					     =	column_names[0]

			# transformations as done in original MAE paper
			# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
			if "shortest_edge" in image_processor.size:
						_UpperCamelCase					:Optional[Any]					     =	image_processor.size['''shortest_edge''']
			else:
						_UpperCamelCase					:Dict					     =	(image_processor.size['''height'''], image_processor.size['''width'''])
			_UpperCamelCase					:Tuple					     =	Compose(
			    [
			        Lambda(lambda snake_case__		: img.convert('''RGB'''		) if img.mode != "RGB" else img		),
			        RandomResizedCrop(snake_case__       ,					scale=(0.2, 1.0)       ,					interpolation=InterpolationMode.BICUBIC		),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean       ,					std=image_processor.image_std		),
			    ]		)

			def preprocess_images(snake_case__		):
						_UpperCamelCase					:str					     =	[transforms(snake_case__		) for image in 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					:int					     =	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:
									_UpperCamelCase					: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__		)

			# Compute absolute learning rate
			_UpperCamelCase					:Optional[int]					     =	(
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						_UpperCamelCase					:Optional[Any]					     =	training_args.base_learning_rate * total_train_batch_size / 2_56

			# Initialize our trainer
			_UpperCamelCase					:Optional[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:
						_UpperCamelCase					:Union[str, Any]					     =	None
						if training_args.resume_from_checkpoint is not None:
									_UpperCamelCase					:str					     =	training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									_UpperCamelCase					:Any					     =	last_checkpoint
						_UpperCamelCase					:str					     =	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:
						_UpperCamelCase					:Optional[int]					     =	trainer.evaluate()
						trainer.log_metrics('''eval'''       ,					snake_case__		)
						trainer.save_metrics('''eval'''       ,					snake_case__		)

			# Write model card and (optionally) push to hub
			_UpperCamelCase					:Any					     =	{
			    '''tasks''': '''masked-auto-encoding''',
			    '''dataset''': data_args.dataset_name,
			    '''tags''': ['''masked-auto-encoding'''],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**snake_case__		)
			else:
						trainer.create_model_card(**snake_case__		)





def 				A_       ( snake_case__		)					->  Dict:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()





"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


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


UpperCamelCase__				:Tuple			   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__				:List[str]			   =	{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase__				:List[Any]			   =	{
    """RUCAIBox/mvp""": 1_024,
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							VOCAB_FILES_NAMES
		A    =							PRETRAINED_VOCAB_FILES_MAP
		A    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A    =							["input_ids", "attention_mask"]
		A    =							MvpTokenizer
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__="replace"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="</s>"					,				SCREAMING_SNAKE_CASE__="<s>"					,				SCREAMING_SNAKE_CASE__="<unk>"					,				SCREAMING_SNAKE_CASE__="<pad>"					,				SCREAMING_SNAKE_CASE__="<mask>"					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				**SCREAMING_SNAKE_CASE__					,				)      ->	List[str]:

					"""simple docstring"""



					super().__init__(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				tokenizer_file=SCREAMING_SNAKE_CASE__					,				errors=SCREAMING_SNAKE_CASE__					,				bos_token=SCREAMING_SNAKE_CASE__					,				eos_token=SCREAMING_SNAKE_CASE__					,				sep_token=SCREAMING_SNAKE_CASE__					,				cls_token=SCREAMING_SNAKE_CASE__					,				unk_token=SCREAMING_SNAKE_CASE__					,				pad_token=SCREAMING_SNAKE_CASE__					,				mask_token=SCREAMING_SNAKE_CASE__					,				add_prefix_space=SCREAMING_SNAKE_CASE__					,				trim_offsets=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)

					_UpperCamelCase					:int					     =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
					if pre_tok_state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
								_UpperCamelCase					:int					     =	getattr(SCREAMING_SNAKE_CASE__					,				pre_tok_state.pop('''type'''   )   )
								_UpperCamelCase					:Optional[int]					     =	add_prefix_space
								_UpperCamelCase					:List[Any]					     =	pre_tok_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					_UpperCamelCase					:Any					     =	'''post_processor'''
					_UpperCamelCase					:Optional[Any]					     =	getattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					if tokenizer_component_instance:
								_UpperCamelCase					:Optional[Any]					     =	json.loads(tokenizer_component_instance.__getstate__()   )

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''sep''']   )
								if "cls" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''cls''']   )

								_UpperCamelCase					:str					     =	False

								if state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
											_UpperCamelCase					:Optional[int]					     =	add_prefix_space
											_UpperCamelCase					:Union[str, Any]					     =	True

								if state.get('''trim_offsets'''					,				SCREAMING_SNAKE_CASE__   ) != trim_offsets:
											_UpperCamelCase					:Union[str, Any]					     =	trim_offsets
											_UpperCamelCase					:Optional[Any]					     =	True

								if changes_to_apply:
											_UpperCamelCase					:Union[str, Any]					     =	getattr(SCREAMING_SNAKE_CASE__					,				state.pop('''type'''   )   )
											_UpperCamelCase					:Optional[Any]					     =	component_class(**SCREAMING_SNAKE_CASE__   )
											setattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					if self._mask_token is None:
								if self.verbose:
											logger.error('''Using mask_token, but it is not set yet.'''   )
								return None
					return str(self._mask_token   )
		@mask_token.setter
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	AddedToken(SCREAMING_SNAKE_CASE__					,				lstrip=SCREAMING_SNAKE_CASE__					,				rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) else value
					_UpperCamelCase					:int					     =	value
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	Tuple[str]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self._tokenizer.model.save(SCREAMING_SNAKE_CASE__					,				name=SCREAMING_SNAKE_CASE__   )
					return tuple(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
								return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	List[int]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	[self.sep_token_id]
					_UpperCamelCase					:List[str]					     =	[self.cls_token_id]

					if token_ids_a is None:
								return len(cls + token_ids_a + sep   ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]

