






import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


__snake_case      :      Dict             =       16
__snake_case      :      Tuple             =       32







def 		_UpperCamelCase				(      UpperCamelCase_    :   Accelerator      ,    UpperCamelCase_    :   int = 16						)					->				Dict:

						"""simple docstring"""



						lowerCAmelCase__        =  AutoTokenizer.from_pretrained('bert-base-cased'						)
						lowerCAmelCase__        =  load_dataset('glue'      ,    'mrpc'						)

						def tokenize_function(UpperCamelCase_    :   List[Any]						):
												# max_length=None => use the model max length (it's actually the default)
												lowerCAmelCase__        =  tokenizer(examples['sentence1']      ,    examples['sentence2']      ,    truncation=UpperCamelCase_      ,    max_length=UpperCamelCase_						)
												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__        =  datasets.map(
												    UpperCamelCase_      ,    batched=UpperCamelCase_      ,    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__        =  tokenized_datasets.rename_column('label'      ,    'labels'						)

						def collate_fn(UpperCamelCase_    :   Union[str, Any]						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowerCAmelCase__        =  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__        =  16
												elif accelerator.mixed_precision != "no":
																		lowerCAmelCase__        =  8
												else:
																		lowerCAmelCase__        =  None

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

						# Instantiate dataloaders.
						lowerCAmelCase__        =  DataLoader(
						    tokenized_datasets['train']      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_						)
						lowerCAmelCase__        =  DataLoader(
						    tokenized_datasets['validation']      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_						)

						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

					__snake_case      :      Tuple             =       mocked_dataloaders  # noqa: F811







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   int						)					->				Optional[Any]:

						"""simple docstring"""



						if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,    UpperCamelCase_						) == "1":
												lowerCAmelCase__        =  2
						# Initialize accelerator
						lowerCAmelCase__        =  Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowerCAmelCase__        =  config['lr']
						lowerCAmelCase__        =  int(config['num_epochs']						)
						lowerCAmelCase__        =  int(config['seed']						)
						lowerCAmelCase__        =  int(config['batch_size']						)

						lowerCAmelCase__        =  evaluate.load('glue'      ,    'mrpc'						)

						# New Code #
						# We now can define an inner training loop function. It should take a batch size as the only parameter,
						# and build the dataloaders in there.
						# It also gets our decorator
						@find_executable_batch_size(starting_batch_size=UpperCamelCase_						)
						def inner_training_loop(UpperCamelCase_    :   Optional[int]						):
												# And now just move everything below under this function
												# We need to bring in the Accelerator object from earlier
												nonlocal accelerator
												# And reset all of its attributes that could hold onto any memory:
												accelerator.free_memory()

												# Then we can declare the model, optimizer, and everything else:
												set_seed(UpperCamelCase_						)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowerCAmelCase__        =  AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,    return_dict=UpperCamelCase_						)

												# 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__        =  model.to(accelerator.device						)

												# Instantiate optimizer
												lowerCAmelCase__        =  AdamW(params=model.parameters()      ,    lr=UpperCamelCase_						)
												lowerCAmelCase__  ,    lowerCAmelCase__        =  get_dataloaders(UpperCamelCase_      ,    UpperCamelCase_						)

												# Instantiate scheduler
												lowerCAmelCase__        =  get_linear_schedule_with_warmup(
												    optimizer=UpperCamelCase_      ,    num_warmup_steps=100      ,    num_training_steps=(len(UpperCamelCase_						) * 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__        =  accelerator.prepare(
												    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)

												# Now we train the model
												for epoch in range(UpperCamelCase_						):
																		model.train()
																		for step, batch in enumerate(UpperCamelCase_						):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device						)
																								lowerCAmelCase__        =  model(**UpperCamelCase_						)
																								lowerCAmelCase__        =  outputs.loss
																								accelerator.backward(UpperCamelCase_						)
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

																		lowerCAmelCase__        =  metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F"epoch {epoch}:"      ,    UpperCamelCase_						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()







def 		_UpperCamelCase				(      )					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  argparse.ArgumentParser(description='Simple example of training script.'						)
						parser.add_argument(
						    '--mixed_precision'      ,    type=UpperCamelCase_      ,    default=UpperCamelCase_      ,    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.'      ,    )
						parser.add_argument('--cpu'      ,    action='store_true'      ,    help='If passed, will train on the CPU.'						)
						lowerCAmelCase__        =  parser.parse_args()
						lowerCAmelCase__        =  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(UpperCamelCase_      ,    UpperCamelCase_						)


if __name__ == "__main__":
					main()













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

import datasets


__snake_case      :      Optional[int]             =       """\
@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\",
}
"""

__snake_case      :      List[str]             =       """\
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.
"""

__snake_case      :      Dict             =       """
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       __SCREAMING_SNAKE_CASE   (				datasets.Metric):






						def  UpperCamelCase__ (		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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  _UpperCamelCase = False       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  len(references[0]				)
												if any(len(_UpperCamelCase				) != 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(_UpperCamelCase				)]

												lowerCAmelCase__        =  TER(
												    normalized=_UpperCamelCase       ,  no_punct=_UpperCamelCase       ,  asian_support=_UpperCamelCase       ,  case_sensitive=_UpperCamelCase       ,  )
												lowerCAmelCase__        =  sb_ter.corpus_score(_UpperCamelCase       ,  _UpperCamelCase				)

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





