




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







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



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



def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							torch.stack([example['''pixel_values'''] for example in examples] )
							return {"pixel_values": pixel_values}
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							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      	=							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_mae'''					,		UpperCamelCase					,		UpperCamelCase )

							# 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(UpperCamelCase )
							transformers.utils.logging.set_verbosity(UpperCamelCase )
							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					,		UpperCamelCase ) 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''']

							# Load pretrained model and image processor
							#
							# 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:
														_a      	=							ViTMAEConfig.from_pretrained(model_args.config_name					,		**UpperCamelCase )
							elif model_args.model_name_or_path:
														_a      	=							ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,		**UpperCamelCase )
							else:
														_a      	=							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      	=							ViTImageProcessor.from_pretrained(model_args.image_processor_name					,		**UpperCamelCase )
							elif model_args.model_name_or_path:
														_a      	=							ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,		**UpperCamelCase )
							else:
														_a      	=							ViTImageProcessor()

							# create model
							if model_args.model_name_or_path:
														_a      	=							ViTMAEForPreTraining.from_pretrained(
														    model_args.model_name_or_path					,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path )					,		config=UpperCamelCase					,		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      	=							ViTMAEForPreTraining(UpperCamelCase )

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

							def preprocess_images(UpperCamelCase			:    Union[str, Any] ):
														_a      	=							[transforms(UpperCamelCase ) 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      	=							ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
														# Set the training transforms
														ds["train"].set_transform(UpperCamelCase )

							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(UpperCamelCase )

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

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

							# 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=UpperCamelCase )
														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'''					,		UpperCamelCase )
														trainer.save_metrics('''eval'''					,		UpperCamelCase )

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





							'''simple docstring'''


							main()


if __name__ == "__main__":
				main()


'''simple docstring'''




import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__lowercase        =    '''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__lowercase        =    '''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

__lowercase        =    '''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,		_KWARGS_DESCRIPTION      )
class 							a__(   datasets.Metric      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					if version.parse(scb.__version__) < version.parse("""1.4.12"""):
							raise ImportWarning(
							    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
							    """You can install it with `pip install \"sacrebleu>=1.4.12\"`.""")
					return datasets.MetricInfo(
					    description=_DESCRIPTION			,       citation=_CITATION			,       homepage="""http://www.cs.umd.edu/~snover/tercom/"""			,       inputs_description=_KWARGS_DESCRIPTION			,       features=datasets.Features(
					        {
					            """predictions""": datasets.Value("""string"""			,       id="""sequence"""),
					            """references""": datasets.Sequence(datasets.Value("""string"""			,       id="""sequence""")			,       id="""references"""),
					        })			,       codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""]			,       reference_urls=[
					        """https://github.com/jhclark/tercom""",
					    ]			,       )







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    len(references[0])
					if any(len(__lowerCAmelCase) != references_per_prediction for refs in references):
							raise ValueError("""Sacrebleu requires the same number of references for each prediction""")
					lowerCAmelCase     =    [[refs[i] for refs in references] for i in range(__lowerCAmelCase)]

					lowerCAmelCase     =    TER(
					    normalized=__lowerCAmelCase			,       no_punct=__lowerCAmelCase			,       asian_support=__lowerCAmelCase			,       case_sensitive=__lowerCAmelCase			,       )
					lowerCAmelCase     =    sb_ter.corpus_score(__lowerCAmelCase			,       __lowerCAmelCase)

					return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}




