


import comet  # From: unbabel-comet
import torch

import datasets


lowerCAmelCase       =							datasets.logging.get_logger(__name__)

lowerCAmelCase       =							'\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

lowerCAmelCase       =							'\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

lowerCAmelCase       =							'\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


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

							def       lowerCamelCase_ (							self:	List[Any]							)			->   int:





									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION    ,						citation=_CITATION    ,						homepage='''https://unbabel.github.io/COMET/html/index.html'''    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
									        {
									            '''sources''': datasets.Value('''string'''    ,						id='''sequence'''							),
									            '''predictions''': datasets.Value('''string'''    ,						id='''sequence'''							),
									            '''references''': datasets.Value('''string'''    ,						id='''sequence'''							),
									        }							)    ,						codebase_urls=['''https://github.com/Unbabel/COMET''']    ,						reference_urls=[
									        '''https://github.com/Unbabel/COMET''',
									        '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
									        '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
									    ]    ,						)

							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	Dict							)			->   Union[str, Any]:





									"""simple docstring"""







									if self.config_name == "default":
											lowercase__            =      comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''							)							)
									else:
											lowercase__            =      comet.load_from_checkpoint(comet.download_model(self.config_name							)							)


							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	List[Any]=None    ,						UpperCamelCase_:	Optional[Any]=False							)			->   Union[str, Any]:





									"""simple docstring"""







									if gpus is None:
											lowercase__            =      1 if torch.cuda.is_available() else 0
									lowercase__            =      {'''src''': sources, '''mt''': predictions, '''ref''': references}
									lowercase__            =      [dict(zip(UpperCamelCase_    ,						UpperCamelCase_							)							) for t in zip(*data.values()							)]
									lowercase__     ,		lowercase__            =      self.scorer.predict(UpperCamelCase_    ,						gpus=UpperCamelCase_    ,						progress_bar=UpperCamelCase_							)
									return {"mean_score": mean_score, "scores": scores}






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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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-classification/requirements.txt')

lowerCAmelCase       =							list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCAmelCase       =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		with open(SCREAMING_SNAKE_CASE						,				'''rb'''     ) as f:
				lowercase__            =      Image.open(SCREAMING_SNAKE_CASE     )
				return im.convert('''RGB'''     )


@dataclass
class        _a							:
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
							    }     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
							_lowercase	:			Optional[str]        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''A folder containing the training data.'''}			)
							_lowercase	:			Optional[str]        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''A folder containing the validation data.'''}			)
							_lowercase	:			Optional[float]        = field(
							    default=0.15     ,	metadata={'''help''': '''Percent to split off of train for validation.'''}			)
							_lowercase	:			Optional[int]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }     ,	)
							_lowercase	:			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       lowerCamelCase_ (							self:	Any							)			->   int:





									"""simple docstring"""







									if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
											raise ValueError(
											    '''You must specify either a dataset name from the hub or a train and/or validation directory.'''							)


@dataclass
class        _a							:
							_lowercase	:			str        = field(
							    default='''google/vit-base-patch16-224-in21k'''     ,	metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase__			)}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}			)
							_lowercase	:			str        = field(
							    default='''main'''     ,	metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}     ,	)
							_lowercase	:			str        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''Name or path of preprocessor config.'''}			)
							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
							            '''with private models).'''
							        )
							    }     ,	)
							_lowercase	:			bool        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}     ,	)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__            =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
		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.
				lowercase__     ,		lowercase__     ,		lowercase__            =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				lowercase__     ,		lowercase__     ,		lowercase__            =      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_image_classification'''						,				SCREAMING_SNAKE_CASE						,				SCREAMING_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()

		lowercase__            =      training_args.get_process_log_level()
		logger.setLevel(SCREAMING_SNAKE_CASE     )
		transformers.utils.logging.set_verbosity(SCREAMING_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.
		lowercase__            =      None
		if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
				lowercase__            =      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.'''     )

    # Set seed before initializing model.
		set_seed(training_args.seed     )

		# Initialize our dataset and prepare it for the 'image-classification' task.
		if data_args.dataset_name is not None:
				lowercase__            =      load_dataset(
				    data_args.dataset_name						,				data_args.dataset_config_name						,				cache_dir=model_args.cache_dir						,				task='''image-classification'''						,				use_auth_token=True if model_args.use_auth_token else None						,				)
		else:
				lowercase__            =      {}
				if data_args.train_dir is not None:
						lowercase__            =      os.path.join(data_args.train_dir						,				'''**'''     )
				if data_args.validation_dir is not None:
						lowercase__            =      os.path.join(data_args.validation_dir						,				'''**'''     )
				lowercase__            =      load_dataset(
				    '''imagefolder'''						,				data_files=SCREAMING_SNAKE_CASE						,				cache_dir=model_args.cache_dir						,				task='''image-classification'''						,				)

		# If we don't have a validation split, split off a percentage of train as validation.
		lowercase__            =      None if '''validation''' in dataset.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split						,				SCREAMING_SNAKE_CASE     ) and data_args.train_val_split > 0.0:
				lowercase__            =      dataset['''train'''].train_test_split(data_args.train_val_split     )
				lowercase__            =      split['''train''']
				lowercase__            =      split['''test''']

		# Prepare label mappings.
		# We'll include these in the model's config to get human readable labels in the Inference API.
		lowercase__            =      dataset['''train'''].features['''labels'''].names
		lowercase__     ,		lowercase__            =      {}, {}
		for i, label in enumerate(SCREAMING_SNAKE_CASE     ):
				lowercase__            =      str(SCREAMING_SNAKE_CASE     )
				lowercase__            =      label

		# Load the accuracy metric from the datasets package
		lowercase__            =      evaluate.load('''accuracy'''     )

		# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(SCREAMING_SNAKE_CASE     ):
				return metric.compute(predictions=np.argmax(p.predictions						,				axis=1     )						,				references=p.label_ids     )

		lowercase__            =      AutoConfig.from_pretrained(
		    model_args.config_name or model_args.model_name_or_path						,				num_labels=len(SCREAMING_SNAKE_CASE     )						,				labelaid=SCREAMING_SNAKE_CASE						,				idalabel=SCREAMING_SNAKE_CASE						,				finetuning_task='''image-classification'''						,				cache_dir=model_args.cache_dir						,				revision=model_args.model_revision						,				use_auth_token=True if model_args.use_auth_token else None						,				)
		lowercase__            =      AutoModelForImageClassification.from_pretrained(
		    model_args.model_name_or_path						,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path     )						,				config=SCREAMING_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						,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,				)
		lowercase__            =      AutoImageProcessor.from_pretrained(
		    model_args.image_processor_name or model_args.model_name_or_path						,				cache_dir=model_args.cache_dir						,				revision=model_args.model_revision						,				use_auth_token=True if model_args.use_auth_token else None						,				)

		# Define torchvision transforms to be applied to each image.
		if "shortest_edge" in image_processor.size:
				lowercase__            =      image_processor.size['''shortest_edge''']
		else:
				lowercase__            =      (image_processor.size['''height'''], image_processor.size['''width'''])
		lowercase__            =      Normalize(mean=image_processor.image_mean						,				std=image_processor.image_std     )
		lowercase__            =      Compose(
		    [
		        RandomResizedCrop(SCREAMING_SNAKE_CASE     ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        normalize,
		    ]     )
		lowercase__            =      Compose(
		    [
		        Resize(SCREAMING_SNAKE_CASE     ),
		        CenterCrop(SCREAMING_SNAKE_CASE     ),
		        ToTensor(),
		        normalize,
		    ]     )

		def train_transforms(SCREAMING_SNAKE_CASE     ):
				lowercase__            =      [
				    _train_transforms(pil_img.convert('''RGB'''     )     ) for pil_img in example_batch['''image''']
				]
				return example_batch

		def val_transforms(SCREAMING_SNAKE_CASE     ):
				lowercase__            =      [_val_transforms(pil_img.convert('''RGB'''     )     ) for pil_img in example_batch['''image''']]
				return example_batch

		if training_args.do_train:
				if "train" not in dataset:
						raise ValueError('''--do_train requires a train dataset'''     )
				if data_args.max_train_samples is not None:
						lowercase__            =      (
						    dataset['''train'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
						)
				# Set the training transforms
				dataset["train"].set_transform(SCREAMING_SNAKE_CASE     )

		if training_args.do_eval:
				if "validation" not in dataset:
						raise ValueError('''--do_eval requires a validation dataset'''     )
				if data_args.max_eval_samples is not None:
						lowercase__            =      (
						    dataset['''validation'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
						)
				# Set the validation transforms
				dataset["validation"].set_transform(SCREAMING_SNAKE_CASE     )

		# Initalize our trainer
		lowercase__            =      Trainer(
		    model=SCREAMING_SNAKE_CASE						,				args=SCREAMING_SNAKE_CASE						,				train_dataset=dataset['''train'''] if training_args.do_train else None						,				eval_dataset=dataset['''validation'''] if training_args.do_eval else None						,				compute_metrics=SCREAMING_SNAKE_CASE						,				tokenizer=SCREAMING_SNAKE_CASE						,				data_collator=SCREAMING_SNAKE_CASE						,				)

		# Training
		if training_args.do_train:
				lowercase__            =      None
				if training_args.resume_from_checkpoint is not None:
						lowercase__            =      training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						lowercase__            =      last_checkpoint
				lowercase__            =      trainer.train(resume_from_checkpoint=SCREAMING_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:
				lowercase__            =      trainer.evaluate()
				trainer.log_metrics('''eval'''						,				SCREAMING_SNAKE_CASE     )
				trainer.save_metrics('''eval'''						,				SCREAMING_SNAKE_CASE     )

		# Write model card and (optionally) push to hub
		lowercase__            =      {
		    '''finetuned_from''': model_args.model_name_or_path,
		    '''tasks''': '''image-classification''',
		    '''dataset''': data_args.dataset_name,
		    '''tags''': ['''image-classification''', '''vision'''],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**SCREAMING_SNAKE_CASE     )
		else:
				trainer.create_model_card(**SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			main()


