



'''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
__lowerCAmelCase		     =  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       __magic_name__		:
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='cifar10'     ,		metadata={'help': 'Name of a dataset from the datasets package'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'The column name of the images in the files.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'A folder containing the training data.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(default=_UpperCamelCase     ,		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			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
							            'value if set.'
							        )
							    }     ,		)







							def 				__lowercase     (				self   :			Dict						):
									_a	: Union[str, Any]								=				{}
									if self.train_dir is not None:
											_a	: Union[str, Any]								=				self.train_dir
									if self.validation_dir is not None:
											_a	: Tuple								=				self.validation_dir
									_a	: List[Any]								=				data_files if data_files else None

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       str  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		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=_UpperCamelCase     ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}  )
							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=_UpperCamelCase     ,		metadata={'help': 'Name or path of preprocessor config.'}  )
							lowerCAmelCase			:       bool  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }     ,		)
							lowerCAmelCase			:       float  							=   field(
							    default=0.75     ,		metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}  )
							lowerCAmelCase			:       bool  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'Whether or not to train with normalized pixel values as target.'}  )

@dataclass
class       __magic_name__		(       _UpperCamelCase  ):
							lowerCAmelCase			:       float  							=   field(
							    default=1e-3     ,		metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}  )



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> str:
		_a	: int								=				torch.stack([example['pixel_values'] for example in examples]    )
		return {"pixel_values": pixel_values}



def 	__lowerCamelCase	( )     -> Optional[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.

		_a	: Dict								=				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.
				_a			,   _a			,   _a	: List[str]								=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
		else:
				_a			,   _a			,   _a	: Any								=				parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_mae'		,     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()

		_a	: Optional[int]								=				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.
		_a	: Tuple								=				None
		if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
				_a	: int								=				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	: Any								=				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	: Optional[int]								=				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:
				_a	: Optional[Any]								=				ds['train'].train_test_split(data_args.train_val_split    )
				_a	: Any								=				split['train']
				_a	: List[str]								=				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.
		_a	: 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:
				_a	: Dict								=				ViTMAEConfig.from_pretrained(model_args.config_name		,     **lowerCAmelCase_    )
		elif model_args.model_name_or_path:
				_a	: str								=				ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,     **lowerCAmelCase_    )
		else:
				_a	: Union[str, 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:
				_a	: Tuple								=				ViTImageProcessor.from_pretrained(model_args.image_processor_name		,     **lowerCAmelCase_    )
		elif model_args.model_name_or_path:
				_a	: Union[str, Any]								=				ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,     **lowerCAmelCase_    )
		else:
				_a	: Optional[Any]								=				ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				_a	: Optional[Any]								=				ViTMAEForPreTraining.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'    )
				_a	: Tuple								=				ViTMAEForPreTraining(lowerCAmelCase_    )

		if training_args.do_train:
				_a	: Optional[int]								=				ds['train'].column_names
		else:
				_a	: List[Any]								=				ds['validation'].column_names

		if data_args.image_column_name is not None:
				_a	: Optional[Any]								=				data_args.image_column_name
		elif "image" in column_names:
				_a	: Any								=				'image'
		elif "img" in column_names:
				_a	: List[str]								=				'img'
		else:
				_a	: Optional[int]								=				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:
				_a	: Optional[int]								=				image_processor.size['shortest_edge']
		else:
				_a	: List[str]								=				(image_processor.size['height'], image_processor.size['width'])
		_a	: Tuple								=				Compose(
		    [
		        Lambda(lambda lowerCAmelCase_    : img.convert('RGB'    ) if img.mode != "RGB" else img    ),
		        RandomResizedCrop(lowerCAmelCase_		,     scale=(0.2, 1.0)		,     interpolation=InterpolationMode.BICUBIC    ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean		,     std=image_processor.image_std    ),
		    ]    )

		def preprocess_images(lowerCAmelCase_    ):
				_a	: int								=				[transforms(lowerCAmelCase_    ) 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:
						_a	: List[Any]								=				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:
						_a	: List[str]								=				(
						    ds['validation'].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
						)
				# Set the validation transforms
				ds["validation"].set_transform(lowerCAmelCase_    )

		# Compute absolute learning rate
		_a	: int								=				(
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_a	: List[Any]								=				training_args.base_learning_rate * total_train_batch_size / 256

		# Initialize our trainer
		_a	: List[Any]								=				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:
				_a	: Union[str, Any]								=				None
				if training_args.resume_from_checkpoint is not None:
						_a	: Union[str, Any]								=				training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_a	: Dict								=				last_checkpoint
				_a	: Tuple								=				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:
				_a	: Union[str, Any]								=				trainer.evaluate()
				trainer.log_metrics('eval'		,     lowerCAmelCase_    )
				trainer.save_metrics('eval'		,     lowerCAmelCase_    )

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



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> Optional[int]:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
							main()









'''simple docstring'''

from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase		     =  logging.get_logger(__name__)

# TODO Update this
__lowerCAmelCase		     =  {
    '''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
    # See all ESM models at https://huggingface.co/models?filter=esm
}

class       __magic_name__		(       _UpperCamelCase  ):
							lowerCAmelCase			:       Any  							=   'esm'







							def __init__(				self   :			Optional[int]      ,_UpperCAmelCase   :			Optional[Any]=None      ,_UpperCAmelCase   :			Dict=None      ,_UpperCAmelCase   :			Union[str, Any]=None      ,_UpperCAmelCase   :			List[Any]=768      ,_UpperCAmelCase   :			Union[str, Any]=12      ,_UpperCAmelCase   :			List[str]=12      ,_UpperCAmelCase   :			Tuple=3072      ,_UpperCAmelCase   :			Dict=0.1      ,_UpperCAmelCase   :			Tuple=0.1      ,_UpperCAmelCase   :			List[str]=1026      ,_UpperCAmelCase   :			List[str]=0.02      ,_UpperCAmelCase   :			Optional[int]=1E-12      ,_UpperCAmelCase   :			List[str]="absolute"      ,_UpperCAmelCase   :			Tuple=True      ,_UpperCAmelCase   :			Tuple=None      ,_UpperCAmelCase   :			List[Any]=False      ,_UpperCAmelCase   :			int=False      ,_UpperCAmelCase   :			int=None      ,_UpperCAmelCase   :			Union[str, Any]=None      ,**_UpperCAmelCase   :			List[Any]      ,):
									super().__init__(pad_token_id=_UpperCAmelCase      ,mask_token_id=_UpperCAmelCase      ,**_UpperCAmelCase						)

									_a	: Optional[Any]								=				vocab_size
									_a	: Union[str, Any]								=				hidden_size
									_a	: Dict								=				num_hidden_layers
									_a	: int								=				num_attention_heads
									_a	: Dict								=				intermediate_size
									_a	: List[Any]								=				hidden_dropout_prob
									_a	: List[Any]								=				attention_probs_dropout_prob
									_a	: Optional[Any]								=				max_position_embeddings
									_a	: Optional[int]								=				initializer_range
									_a	: List[Any]								=				layer_norm_eps
									_a	: int								=				position_embedding_type
									_a	: Optional[int]								=				use_cache
									_a	: Any								=				emb_layer_norm_before
									_a	: List[str]								=				token_dropout
									_a	: List[str]								=				is_folding_model
									if is_folding_model:
											if esmfold_config is None:
													logger.info('No esmfold_config supplied for folding model, using default values.'						)
													_a	: Dict								=				EsmFoldConfig()
											elif isinstance(_UpperCAmelCase      ,_UpperCAmelCase						):
													_a	: Dict								=				EsmFoldConfig(**_UpperCAmelCase						)
											_a	: Optional[int]								=				esmfold_config
											if vocab_list is None:
													logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'						)
													_a	: Optional[int]								=				get_default_vocab_list()
											else:
													_a	: Optional[int]								=				vocab_list
									else:
											_a	: Optional[Any]								=				None
											_a	: Union[str, Any]								=				None
									if self.esmfold_config is not None and getattr(self.esmfold_config      ,'use_esm_attn_map'      ,_UpperCAmelCase						):
											raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'						)







							def 				__lowercase     (				self   :			Any						):
									_a	: str								=				super().to_dict()
									if isinstance(self.esmfold_config      ,_UpperCAmelCase						):
											_a	: List[str]								=				self.esmfold_config.to_dict()
									return output

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       str  							=   None
							lowerCAmelCase			:       bool  							=   True
							lowerCAmelCase			:       bool  							=   False
							lowerCAmelCase			:       bool  							=   False
							lowerCAmelCase			:       bool  							=   False
							lowerCAmelCase			:       float  							=   0

							lowerCAmelCase			:       bool  							=   True
							lowerCAmelCase			:       bool  							=   False

							lowerCAmelCase			:       int  							=   1_2_8
							lowerCAmelCase			:       "TrunkConfig"  							=   None







							def 				__lowercase     (				self   :			List[str]						):
									if self.trunk is None:
											_a	: Dict								=				TrunkConfig()
									elif isinstance(self.trunk      ,_UpperCAmelCase						):
											_a	: str								=				TrunkConfig(**self.trunk						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: List[str]								=				asdict(self						)
									_a	: List[str]								=				self.trunk.to_dict()
									return output

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       int  							=   4_8
							lowerCAmelCase			:       int  							=   1_0_2_4
							lowerCAmelCase			:       int  							=   1_2_8
							lowerCAmelCase			:       int  							=   3_2
							lowerCAmelCase			:       int  							=   3_2
							lowerCAmelCase			:       int  							=   3_2
							lowerCAmelCase			:       float  							=   0
							lowerCAmelCase			:       float  							=   0
							lowerCAmelCase			:       bool  							=   False
							lowerCAmelCase			:       int  							=   4
							lowerCAmelCase			:       Optional[int]  							=   1_2_8
							lowerCAmelCase			:       "StructureModuleConfig"  							=   None







							def 				__lowercase     (				self   :			str						):
									if self.structure_module is None:
											_a	: Tuple								=				StructureModuleConfig()
									elif isinstance(self.structure_module      ,_UpperCAmelCase						):
											_a	: List[str]								=				StructureModuleConfig(**self.structure_module						)

									if self.max_recycles <= 0:
											raise ValueError(F"""`max_recycles` should be positive, got {self.max_recycles}."""						)
									if self.sequence_state_dim % self.sequence_state_dim != 0:
											raise ValueError(
											    '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
											    F""" {self.sequence_state_dim} and {self.sequence_state_dim}."""						)
									if self.pairwise_state_dim % self.pairwise_state_dim != 0:
											raise ValueError(
											    '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
											    F""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""						)

									_a	: Optional[int]								=				self.sequence_state_dim // self.sequence_head_width
									_a	: int								=				self.pairwise_state_dim // self.pairwise_head_width

									if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
											raise ValueError(
											    '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
											    F""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""						)
									if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
											raise ValueError(
											    '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
											    F""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""						)
									if self.pairwise_state_dim % 2 != 0:
											raise ValueError(F"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""						)

									if self.dropout >= 0.4:
											raise ValueError(F"""`dropout` should not be greater than 0.4, got {self.dropout}."""						)







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Optional[Any]								=				asdict(self						)
									_a	: Optional[Any]								=				self.structure_module.to_dict()
									return output

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       int  							=   3_8_4
							lowerCAmelCase			:       int  							=   1_2_8
							lowerCAmelCase			:       int  							=   1_6
							lowerCAmelCase			:       int  							=   1_2_8
							lowerCAmelCase			:       int  							=   1_2
							lowerCAmelCase			:       int  							=   4
							lowerCAmelCase			:       int  							=   8
							lowerCAmelCase			:       float  							=   0.1
							lowerCAmelCase			:       int  							=   8
							lowerCAmelCase			:       int  							=   1
							lowerCAmelCase			:       int  							=   2
							lowerCAmelCase			:       int  							=   7
							lowerCAmelCase			:       int  							=   1_0
							lowerCAmelCase			:       float  							=   1e-8
							lowerCAmelCase			:       float  							=   1e5







							def 				__lowercase     (				self   :			str						):
									return asdict(self						)



def 	__lowerCamelCase	( )     -> Optional[int]:
		return (
		    "<cls>",
		    "<pad>",
		    "<eos>",
		    "<unk>",
		    "L",
		    "A",
		    "G",
		    "V",
		    "S",
		    "E",
		    "R",
		    "T",
		    "I",
		    "D",
		    "P",
		    "K",
		    "Q",
		    "N",
		    "F",
		    "Y",
		    "M",
		    "H",
		    "W",
		    "C",
		    "X",
		    "B",
		    "U",
		    "Z",
		    "O",
		    ".",
		    "-",
		    "<null_1>",
		    "<mask>",
		)




