




"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowercase__  		=		16
lowercase__  		=		32







def   _snake_case    (       lowercase__							,					lowercase__ = 16	):
				_lowerCamelCase  :    Optional[int]					=					AutoTokenizer.from_pretrained('bert-base-cased'	)
				_lowerCamelCase  :    Union[str, Any]					=					load_dataset('glue'							,					'mrpc'	)

				def tokenize_function(lowercase__	):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCamelCase  :    int					=					tokenizer(examples['sentence1']							,					examples['sentence2']							,					truncation=lowercase__							,					max_length=lowercase__	)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								_lowerCamelCase  :    Tuple					=					datasets.map(
								    lowercase__							,					batched=lowercase__							,					remove_columns=['idx', 'sentence1', 'sentence2']							,					)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_lowerCamelCase  :    Tuple					=					tokenized_datasets.rename_column('label'							,					'labels'	)

				def collate_fn(lowercase__	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_lowerCamelCase  :    Tuple					=					128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												_lowerCamelCase  :    Optional[int]					=					16
								elif accelerator.mixed_precision != "no":
												_lowerCamelCase  :    Optional[Any]					=					8
								else:
												_lowerCamelCase  :    str					=					None

								return tokenizer.pad(
								    lowercase__							,					padding='longest'							,					max_length=lowercase__							,					pad_to_multiple_of=lowercase__							,					return_tensors='pt'							,					)

				# Instantiate dataloaders.
				_lowerCamelCase  :    Optional[int]					=					DataLoader(
				    tokenized_datasets['train']							,					shuffle=lowercase__							,					collate_fn=lowercase__							,					batch_size=lowercase__	)
				_lowerCamelCase  :    Optional[Any]					=					DataLoader(
				    tokenized_datasets['validation']							,					shuffle=lowercase__							,					collate_fn=lowercase__							,					batch_size=lowercase__	)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			lowercase__  		=		mocked_dataloaders  # noqa: F811







def   _snake_case    (       lowercase__							,					lowercase__	):
				# For testing only
				if os.environ.get('TESTING_MOCKED_DATALOADERS'							,					lowercase__	) == "1":
								_lowerCamelCase  :    str					=					2
				# New Code #
				_lowerCamelCase  :    Optional[int]					=					int(args.gradient_accumulation_steps	)
				_lowerCamelCase  :    List[str]					=					int(args.local_sgd_steps	)
				# Initialize accelerator
				_lowerCamelCase  :    Union[str, Any]					=					Accelerator(
				    cpu=args.cpu							,					mixed_precision=args.mixed_precision							,					gradient_accumulation_steps=lowercase__	)
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'	)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_lowerCamelCase  :    Optional[int]					=					config['lr']
				_lowerCamelCase  :    Optional[Any]					=					int(config['num_epochs']	)
				_lowerCamelCase  :    Any					=					int(config['seed']	)
				_lowerCamelCase  :    str					=					int(config['batch_size']	)

				_lowerCamelCase  :    Any					=					evaluate.load('glue'							,					'mrpc'	)

				set_seed(lowercase__	)
				_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					get_dataloaders(lowercase__							,					lowercase__	)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_lowerCamelCase  :    List[str]					=					AutoModelForSequenceClassification.from_pretrained('bert-base-cased'							,					return_dict=lowercase__	)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				_lowerCamelCase  :    Any					=					model.to(accelerator.device	)

				# Instantiate optimizer
				_lowerCamelCase  :    List[Any]					=					AdamW(params=model.parameters()							,					lr=lowercase__	)

				# Instantiate scheduler
				_lowerCamelCase  :    Optional[Any]					=					get_linear_schedule_with_warmup(
				    optimizer=lowercase__							,					num_warmup_steps=100							,					num_training_steps=(len(lowercase__	) * num_epochs)							,					)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					accelerator.prepare(
				    lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	)

				# Now we train the model
				for epoch in range(lowercase__	):
								model.train()
								with LocalSGD(
								    accelerator=lowercase__							,					model=lowercase__							,					local_sgd_steps=lowercase__							,					enabled=local_sgd_steps is not None	) as local_sgd:
												for step, batch in enumerate(lowercase__	):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device	)
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(lowercase__	):
																				_lowerCamelCase  :    Any					=					model(**lowercase__	)
																				_lowerCamelCase  :    List[Any]					=					output.loss
																				accelerator.backward(lowercase__	)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								for step, batch in enumerate(lowercase__	):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device	)
												with torch.no_grad():
																_lowerCamelCase  :    Optional[Any]					=					model(**lowercase__	)
												_lowerCamelCase  :    str					=					outputs.logits.argmax(dim=-1	)
												_lowerCamelCase,				_lowerCamelCase  :    str					=					accelerator.gather_for_metrics((predictions, batch['labels'])	)
												metric.add_batch(
												    predictions=lowercase__							,					references=lowercase__							,					)

								_lowerCamelCase  :    int					=					metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''							,					lowercase__	)







def   _snake_case    (       ):
				_lowerCamelCase  :    List[str]					=					argparse.ArgumentParser(description='Simple example of training script.'	)
				parser.add_argument(
				    '--mixed_precision'							,					type=lowercase__							,					default=lowercase__							,					choices=['no', 'fp16', 'bf16', 'fp8']							,					help='Whether to use mixed precision. Choose'
				    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
				    'and an Nvidia Ampere GPU.'							,					)
				# New Code #
				parser.add_argument(
				    '--gradient_accumulation_steps'							,					type=lowercase__							,					default=1							,					help='The number of minibatches to be ran before gradients are accumulated.'							,					)
				parser.add_argument(
				    '--local_sgd_steps'							,					type=lowercase__							,					default=8							,					help='Number of local SGD steps or None to disable local SGD'	)
				parser.add_argument('--cpu'							,					action='store_true'							,					help='If passed, will train on the CPU.'	)
				_lowerCamelCase  :    Tuple					=					parser.parse_args()
				_lowerCamelCase  :    str					=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(lowercase__							,					lowercase__	)


if __name__ == "__main__":
			main()





"""simple docstring"""






import comet  # From: unbabel-comet
import torch

import datasets


lowercase__  		=		datasets.logging.get_logger(__name__)

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

lowercase__  		=		"""\
Crosslingual 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).
With 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.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""

lowercase__  		=		"""
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric('comet')
    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use
    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [0.19, 0.92]
"""






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






			'''simple docstring'''



			def       A_					(						self						):
							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       A_					(						self						,   lowercase						):
							if self.config_name == "default":
											_lowerCamelCase  :    List[Any]					=					comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'						)						)
							else:
											_lowerCamelCase  :    Tuple					=					comet.load_from_checkpoint(comet.download_model(self.config_name						)						)






			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase=None						,   lowercase=False						):
							if gpus is None:
											_lowerCamelCase  :    Tuple					=					1 if torch.cuda.is_available() else 0
							_lowerCamelCase  :    List[Any]					=					{'src': sources, 'mt': predictions, 'ref': references}
							_lowerCamelCase  :    int					=					[dict(zip(lowercase						,   lowercase						)						) for t in zip(*data.values()						)]
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self.scorer.predict(lowercase						,   gpus=lowercase						,   progress_bar=lowercase						)
							return {"mean_score": mean_score, "scores": scores}