

'''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
__snake_case			:     str										=      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 lowerCamelCase			:



			'''simple docstring'''






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





									'''simple docstring'''
									A__					:       Dict     ={}
									if self.train_dir is not None:
															A__					:       Optional[Any]     =self.train_dir
									if self.validation_dir is not None:
															A__					:       Optional[Any]     =self.validation_dir
									A__					:       str     =data_files if data_files else None



@dataclass
class lowerCamelCase			:



			'''simple docstring'''






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



@dataclass
class lowerCamelCase			(  UpperCamelCase_						):



			'''simple docstring'''






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


def 						__lowerCamelCase	(       __snake_case					:		Any	):






						"""simple docstring"""





						A__					:       Union[str, Any]     =torch.stack([example["""pixel_values"""] for example in examples]	)
						return {"pixel_values": pixel_values}
def 						__lowerCamelCase	(       ):






						"""simple docstring"""





						A__					:       Tuple     =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__					:       int     =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
						else:
												A__					:       Dict     =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__					:       List[Any]     =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__					:       Any     =None
						if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
												A__					:       Optional[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__					:       str     =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__					:       Any     =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__					:       List[Any]     =ds["""train"""].train_test_split(data_args.train_val_split	)
												A__					:       List[str]     =split["""train"""]
												A__					:       int     =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__					:       int     ={
						    """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__					:       List[Any]     =ViTMAEConfig.from_pretrained(model_args.config_name,		**__lowerCAmelCase	)
						elif model_args.model_name_or_path:
												A__					:       List[str]     =ViTMAEConfig.from_pretrained(model_args.model_name_or_path,		**__lowerCAmelCase	)
						else:
												A__					:       int     =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__					:       int     =ViTImageProcessor.from_pretrained(model_args.image_processor_name,		**__lowerCAmelCase	)
						elif model_args.model_name_or_path:
												A__					:       Tuple     =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__					:       List[str]     =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__					:       Union[str, Any]     =ViTMAEForPreTraining(__lowerCAmelCase	)

						if training_args.do_train:
												A__					:       List[str]     =ds["""train"""].column_names
						else:
												A__					:       Any     =ds["""validation"""].column_names

						if data_args.image_column_name is not None:
												A__					:       List[Any]     =data_args.image_column_name
						elif "image" in column_names:
												A__					:       Tuple     ="""image"""
						elif "img" in column_names:
												A__					:       Dict     ="""img"""
						else:
												A__					:       List[str]     =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__					:       str     =image_processor.size["""shortest_edge"""]
						else:
												A__					:       List[str]     =(image_processor.size["""height"""], image_processor.size["""width"""])
						A__					:       Any     =Compose(
						    [
						        Lambda(lambda __snake_case	: 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(__snake_case					:		Any	):
												A__					:       Optional[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__					:       Union[str, 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__					:       Any     =(
																		    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__					:       Dict     =(
						    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[str]     =training_args.base_learning_rate * total_train_batch_size / 256

						# Initialize our trainer
						A__					:       str     =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__					:       List[Any]     =None
												if training_args.resume_from_checkpoint is not None:
																		A__					:       int     =training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		A__					:       Tuple     =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__					:       List[Any]     =trainer.evaluate()
												trainer.log_metrics("""eval""",		__lowerCAmelCase	)
												trainer.save_metrics("""eval""",		__lowerCAmelCase	)

						# Write model card and (optionally) push to hub
						A__					:       str     ={
						    """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	(       __snake_case					:		str	):






						"""simple docstring"""





						main()


if __name__ == "__main__":
		main()




'''simple docstring'''



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


__snake_case			:     Union[str, Any]										=      logging.get_logger(__name__)

__snake_case			:     Optional[int]										=      {
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	'gpt_bigcode'
			__snake_case        =	['past_key_values']
			__snake_case        =	{
			    'hidden_size': 'n_embd',
			    'max_position_embeddings': 'n_positions',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}


			def __init__(				self	:						Optional[Any]		,      lowerCAmelCase_	:						List[str]=5_02_57		,      lowerCAmelCase_	:						str=10_24		,      lowerCAmelCase_	:						str=7_68		,      lowerCAmelCase_	:						str=12		,      lowerCAmelCase_	:						int=12		,      lowerCAmelCase_	:						Dict=None		,      lowerCAmelCase_	:						List[Any]="gelu_pytorch_tanh"		,      lowerCAmelCase_	:						Union[str, Any]=0.1		,      lowerCAmelCase_	:						Optional[Any]=0.1		,      lowerCAmelCase_	:						Optional[int]=0.1		,      lowerCAmelCase_	:						Dict=1e-5		,      lowerCAmelCase_	:						str=0.02		,      lowerCAmelCase_	:						List[Any]=True		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						str=5_02_56		,      lowerCAmelCase_	:						Dict=5_02_56		,      lowerCAmelCase_	:						Union[str, Any]=True		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						Optional[Any]=True		,      **lowerCAmelCase_	:						Optional[Any]		,      )      ->		Tuple:





									'''simple docstring'''
									A__					:       Optional[Any]     =vocab_size
									A__					:       Optional[Any]     =n_positions
									A__					:       List[str]     =n_embd
									A__					:       str     =n_layer
									A__					:       Optional[int]     =n_head
									A__					:       Optional[int]     =n_inner
									A__					:       int     =activation_function
									A__					:       int     =resid_pdrop
									A__					:       int     =embd_pdrop
									A__					:       Dict     =attn_pdrop
									A__					:       Any     =layer_norm_epsilon
									A__					:       List[Any]     =initializer_range
									A__					:       Dict     =scale_attn_weights
									A__					:       Any     =use_cache
									A__					:       List[Any]     =attention_softmax_in_fpaa
									A__					:       Optional[int]     =scale_attention_softmax_in_fpaa
									A__					:       Dict     =multi_query

									A__					:       List[str]     =bos_token_id
									A__					:       Any     =eos_token_id

									super().__init__(bos_token_id=lowerCAmelCase_		,      eos_token_id=lowerCAmelCase_		,      **lowerCAmelCase_			)

