


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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################


lowerCAmelCase			=				16
lowerCAmelCase			=				32
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = 16	):


		"""simple docstring"""




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

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

		def collate_fn(SCREAMING_SNAKE_CASE	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__				= 1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__				= 16
				elif accelerator.mixed_precision != "no":
						lowercase__				= 8
				else:
						lowercase__				= None

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

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

		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

			lowerCAmelCase			=				mocked_dataloaders  # noqa: F811
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,							SCREAMING_SNAKE_CASE	) == "1":
				lowercase__				= 2
		# New Code #
		lowercase__				= int(args.gradient_accumulation_steps	)
		# Initialize accelerator
		lowercase__				= Accelerator(
		    cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							gradient_accumulation_steps=SCREAMING_SNAKE_CASE	)
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__				= config['''lr''']
		lowercase__				= int(config['''num_epochs''']	)
		lowercase__				= int(config['''seed''']	)
		lowercase__				= int(config['''batch_size''']	)

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

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

		# 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).
		lowercase__				= model.to(accelerator.device	)

		# Instantiate optimizer
		lowercase__				= AdamW(params=model.parameters()		,							lr=SCREAMING_SNAKE_CASE	)

		# Instantiate scheduler
		lowercase__				= get_linear_schedule_with_warmup(
		    optimizer=SCREAMING_SNAKE_CASE		,							num_warmup_steps=1_00		,							num_training_steps=(len(SCREAMING_SNAKE_CASE	) * 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.
		lowercase__	,						lowercase__	,						lowercase__	,						lowercase__	,						lowercase__				= accelerator.prepare(
		    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)

		# Now we train the model
		for epoch in range(SCREAMING_SNAKE_CASE	):
				model.train()
				for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
						# 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(SCREAMING_SNAKE_CASE	):
								lowercase__				= model(**SCREAMING_SNAKE_CASE	)
								lowercase__				= output.loss
								accelerator.backward(SCREAMING_SNAKE_CASE	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

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


		"""simple docstring"""




		lowercase__				= argparse.ArgumentParser(description='''Simple example of training script.'''	)
		parser.add_argument(
		    '''--mixed_precision'''		,							type=SCREAMING_SNAKE_CASE		,							default=SCREAMING_SNAKE_CASE		,							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=SCREAMING_SNAKE_CASE		,							default=1		,							help='''The number of minibatches to be ran before gradients are accumulated.'''		,							)
		parser.add_argument('''--cpu'''		,							action='''store_true'''		,							help='''If passed, will train on the CPU.'''	)
		lowercase__				= parser.parse_args()
		lowercase__				= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
			main()






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


A			:						Dict        =  '\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

A			:						List[str]        =  '\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

A			:						Any        =  '\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION				)
class 				A       (			datasets.Metric				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  Union[str, Any]:




							"""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="""https://github.com/mjpost/sacreBLEU#chrf--chrf""" ,   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#chrf--chrf"""] ,   reference_urls=[
							        """https://github.com/m-popovic/chrF""",
							    ] ,   )


					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int = CHRF.CHAR_ORDER ,   _UpperCAmelCase       :		int = CHRF.WORD_ORDER ,   _UpperCAmelCase       :		int = CHRF.BETA ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						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"""						)
							lowercase__	     =						[[refs[i] for refs in references] for i in range(_UpperCAmelCase						)]

							lowercase__	     =						CHRF(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							lowercase__	     =						sb_chrf.corpus_score(_UpperCAmelCase ,   _UpperCAmelCase						)

							return {
							    "score": output.score,
							    "char_order": output.char_order,
							    "word_order": output.word_order,
							    "beta": output.beta,
							}

