
"""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__   :					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')



@dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



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



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




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






@dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



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






@dataclass
class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



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






def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     torch.stack([example["""pixel_values"""] for example in examples]		)
							return {"pixel_values": pixel_values}

def lowercase_						(     ):
							# 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.

							SCREAMING_SNAKE_CASE__		: Optional[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.
														SCREAMING_SNAKE_CASE__		: Tuple     =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
							else:
														SCREAMING_SNAKE_CASE__		: List[str]     =     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()

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

							# create model
							if model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     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"""		)
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     ViTMAEForPreTraining(_snake_case		)

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

							if data_args.image_column_name is not None:
														SCREAMING_SNAKE_CASE__		: List[Any]     =     data_args.image_column_name
							elif "image" in column_names:
														SCREAMING_SNAKE_CASE__		: Tuple     =     """image"""
							elif "img" in column_names:
														SCREAMING_SNAKE_CASE__		: Dict     =     """img"""
							else:
														SCREAMING_SNAKE_CASE__		: 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:
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     image_processor.size["""shortest_edge"""]
							else:
														SCREAMING_SNAKE_CASE__		: Tuple     =     (image_processor.size["""height"""], image_processor.size["""width"""])
							SCREAMING_SNAKE_CASE__		: Dict     =     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		):
														SCREAMING_SNAKE_CASE__		: 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:
																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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:
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     (
																					    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
							SCREAMING_SNAKE_CASE__		: str     =     (
							    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
							)
							if training_args.base_learning_rate is not None:
														SCREAMING_SNAKE_CASE__		: str     =     training_args.base_learning_rate * total_train_batch_size / 256

							# Initialize our trainer
							SCREAMING_SNAKE_CASE__		: Tuple     =     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:
														SCREAMING_SNAKE_CASE__		: Any     =     None
														if training_args.resume_from_checkpoint is not None:
																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					SCREAMING_SNAKE_CASE__		: Dict     =     last_checkpoint
														SCREAMING_SNAKE_CASE__		: List[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:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     trainer.evaluate()
														trainer.log_metrics("""eval"""     ,_snake_case		)
														trainer.save_metrics("""eval"""     ,_snake_case		)

							# Write model card and (optionally) push to hub
							SCREAMING_SNAKE_CASE__		: Optional[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 lowercase_						(     _snake_case		):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
					main()



"""simple docstring"""




import logging

from transformers import PretrainedConfig


UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger(__name__)


UpperCAmelCase__   :					Any												=				{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''bertabs'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=3_05_22     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=0.2     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=0.2     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Any     =     max_pos

											SCREAMING_SNAKE_CASE__		: List[Any]     =     enc_layers
											SCREAMING_SNAKE_CASE__		: Tuple     =     enc_hidden_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_heads
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     enc_ff_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_dropout

											SCREAMING_SNAKE_CASE__		: Any     =     dec_layers
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dec_hidden_size
											SCREAMING_SNAKE_CASE__		: Any     =     dec_heads
											SCREAMING_SNAKE_CASE__		: List[Any]     =     dec_ff_size
											SCREAMING_SNAKE_CASE__		: str     =     dec_dropout




