



import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase     							=						16
_lowercase     							=						32






def      _A				(UpperCamelCase		:      Accelerator      ,			UpperCamelCase		:      int = 16      ,			UpperCamelCase		:      str = "bert-base-cased" )  ->Union[str, Any]:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		AutoTokenizer.from_pretrained(UpperCamelCase )
			lowerCamelCase__ :      Any								=		load_dataset("""glue"""      ,			"""mrpc""" )

			def tokenize_function(UpperCamelCase		:      List[str] ):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase__ :      int								=		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
			lowerCamelCase__ :      Dict								=		datasets.map(
			    UpperCamelCase      ,			batched=UpperCamelCase      ,			remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,			load_from_cache_file=UpperCamelCase )

			# 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(UpperCamelCase		:      Any ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(UpperCamelCase      ,			padding="""max_length"""      ,			max_length=128      ,			return_tensors="""pt""" )
						return tokenizer.pad(UpperCamelCase      ,			padding="""longest"""      ,			return_tensors="""pt""" )

			# Instantiate dataloaders.
			lowerCamelCase__ :      str								=		DataLoader(
			    tokenized_datasets["""train"""]      ,			shuffle=UpperCamelCase      ,			collate_fn=UpperCamelCase      ,			batch_size=UpperCamelCase )
			lowerCamelCase__ :      Optional[int]								=		DataLoader(
			    tokenized_datasets["""validation"""]      ,			shuffle=UpperCamelCase      ,			collate_fn=UpperCamelCase      ,			batch_size=UpperCamelCase )

			return train_dataloader, eval_dataloader






def      _A				(UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      List[Any] )  ->Tuple:



			'''simple docstring'''







			model.eval()
			lowerCamelCase__ :      Tuple								=		0
			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__ :      List[Any]								=		model(**UpperCamelCase )
						lowerCamelCase__ :      Any								=		outputs.logits.argmax(dim=-1 )
						# It is slightly faster to call this once, than multiple times
						lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		accelerator.gather(
						    (predictions, batch["""labels"""]) )  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
									if step == len(UpperCamelCase ) - 1:
												lowerCamelCase__ :      Dict								=		predictions[: len(eval_dataloader.dataset ) - samples_seen]
												lowerCamelCase__ :      Tuple								=		references[: len(eval_dataloader.dataset ) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=UpperCamelCase      ,			references=UpperCamelCase      ,			)

			lowerCamelCase__ :      List[str]								=		metric.compute()
			return eval_metric["accuracy"]






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase__ :      Dict								=		config["""lr"""]
			lowerCamelCase__ :      List[Any]								=		int(config["""num_epochs"""] )
			lowerCamelCase__ :      Dict								=		int(config["""seed"""] )
			lowerCamelCase__ :      Any								=		int(config["""batch_size"""] )
			lowerCamelCase__ :      List[str]								=		args.model_name_or_path

			set_seed(UpperCamelCase )
			lowerCamelCase__							,lowerCamelCase__ :      str								=		get_dataloaders(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase__ :      Any								=		AutoModelForSequenceClassification.from_pretrained(UpperCamelCase      ,			return_dict=UpperCamelCase )

			# Instantiate optimizer
			lowerCamelCase__ :      Union[str, Any]								=		(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			lowerCamelCase__ :      Tuple								=		optimizer_cls(params=model.parameters()      ,			lr=UpperCamelCase )

			if accelerator.state.deepspeed_plugin is not None:
						lowerCamelCase__ :      Dict								=		accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						lowerCamelCase__ :      int								=		1
			lowerCamelCase__ :      Any								=		(len(UpperCamelCase ) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						lowerCamelCase__ :      Tuple								=		get_linear_schedule_with_warmup(
						    optimizer=UpperCamelCase      ,			num_warmup_steps=0      ,			num_training_steps=UpperCamelCase      ,			)
			else:
						lowerCamelCase__ :      Dict								=		DummyScheduler(UpperCamelCase      ,			total_num_steps=UpperCamelCase      ,			warmup_num_steps=0 )

			# 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__ :      List[str]								=		accelerator.prepare(
			    UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

			# We need to keep track of how many total steps we have iterated over
			lowerCamelCase__ :      Tuple								=		0
			# We also need to keep track of the stating epoch so files are named properly
			lowerCamelCase__ :      Union[str, Any]								=		0
			lowerCamelCase__ :      Tuple								=		evaluate.load("""glue"""      ,			"""mrpc""" )
			lowerCamelCase__ :      List[str]								=		num_epochs

			if args.partial_train_epoch is not None:
						lowerCamelCase__ :      Dict								=		args.partial_train_epoch

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint )
						lowerCamelCase__ :      Union[str, Any]								=		args.resume_from_checkpoint.split("""epoch_""" )[1]
						lowerCamelCase__ :      Union[str, Any]								=		""""""
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						lowerCamelCase__ :      Union[str, Any]								=		int(UpperCamelCase ) + 1
						lowerCamelCase__ :      Tuple								=		evaluation_loop(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
						accelerator.print("""resumed checkpoint performance:"""      ,			UpperCamelCase )
						accelerator.print("""resumed checkpoint's scheduler's lr:"""      ,			lr_scheduler.get_lr()[0] )
						accelerator.print("""resumed optimizers's lr:"""      ,			optimizer.param_groups[0]["""lr"""] )
						with open(os.path.join(args.output_dir      ,			f"state_{starting_epoch-1}.json" )      ,			"""r""" ) as f:
									lowerCamelCase__ :      str								=		json.load(UpperCamelCase )
									assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["lr"] == lr_scheduler.get_lr()[0]
									), "Scheduler learning rate mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
									), "Optimizer learning rate mismatch, loading from checkpoint failed"
									assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
									return

    # Now we train the model
			lowerCamelCase__ :      Optional[Any]								=		{}
			for epoch in range(UpperCamelCase      ,			UpperCamelCase ):
						model.train()
						for step, batch in enumerate(UpperCamelCase ):
									lowerCamelCase__ :      Tuple								=		model(**UpperCamelCase )
									lowerCamelCase__ :      Optional[Any]								=		outputs.loss
									lowerCamelCase__ :      Tuple								=		loss / gradient_accumulation_steps
									accelerator.backward(UpperCamelCase )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1
						lowerCamelCase__ :      Union[str, Any]								=		f"epoch_{epoch}"
						lowerCamelCase__ :      Union[str, Any]								=		os.path.join(args.output_dir      ,			UpperCamelCase )
						accelerator.save_state(UpperCamelCase )
						lowerCamelCase__ :      List[Any]								=		evaluation_loop(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
						lowerCamelCase__ :      Union[str, Any]								=		accuracy
						lowerCamelCase__ :      Tuple								=		lr_scheduler.get_lr()[0]
						lowerCamelCase__ :      Union[str, Any]								=		optimizer.param_groups[0]["""lr"""]
						lowerCamelCase__ :      Union[str, Any]								=		epoch
						lowerCamelCase__ :      Tuple								=		overall_step
						accelerator.print(f"epoch {epoch}:"      ,			UpperCamelCase )

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									with open(os.path.join(args.output_dir      ,			f"state_{epoch}.json" )      ,			"""w""" ) as f:
												json.dump(UpperCamelCase      ,			UpperCamelCase )






def      _A				()  ->Union[str, Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
			parser.add_argument(
			    """--model_name_or_path"""      ,			type=UpperCamelCase      ,			default="""bert-base-cased"""      ,			help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,			required=UpperCamelCase      ,			)
			parser.add_argument(
			    """--output_dir"""      ,			type=UpperCamelCase      ,			default="""."""      ,			help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,			)
			parser.add_argument(
			    """--resume_from_checkpoint"""      ,			type=UpperCamelCase      ,			default=UpperCamelCase      ,			help="""If the training should continue from a checkpoint folder."""      ,			)
			parser.add_argument(
			    """--partial_train_epoch"""      ,			type=UpperCamelCase      ,			default=UpperCamelCase      ,			help="""If passed, the training will stop after this number of epochs."""      ,			)
			parser.add_argument(
			    """--num_epochs"""      ,			type=UpperCamelCase      ,			default=2      ,			help="""Number of train epochs."""      ,			)
			lowerCamelCase__ :      Any								=		parser.parse_args()
			lowerCamelCase__ :      Tuple								=		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

			training_function(UpperCamelCase      ,			UpperCamelCase )


if __name__ == "__main__":
				main()








import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_lowercase     							=						version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
				from nltk import word_tokenize


_lowercase     							=						'''\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
'''

_lowercase     							=						'''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_lowercase     							=						'''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION      )
class 				__A					( datasets.Metric      ):





			def        _snake_case    (self				):
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
						        {
						            """predictions""": datasets.Value("""string"""							,    id="""sequence"""				),
						            """references""": datasets.Value("""string"""							,    id="""sequence"""				),
						        }				)							,    codebase_urls=["""https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"""]							,    reference_urls=[
						        """https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score""",
						        """https://en.wikipedia.org/wiki/METEOR""",
						    ]							,    )





			def        _snake_case    (self							,    __magic_name__				):
						import nltk

						nltk.download("""wordnet"""				)
						if NLTK_VERSION >= version.Version("""3.6.5"""				):
									nltk.download("""punkt"""				)
						if NLTK_VERSION >= version.Version("""3.6.6"""				):
									nltk.download("""omw-1.4"""				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__=0.9							,    __magic_name__=3							,    __magic_name__=0.5				):
						if NLTK_VERSION >= version.Version("""3.6.5"""				):
									lowerCamelCase__ :      List[str]								=		[
									    meteor_score.single_meteor_score(
									        word_tokenize(__magic_name__				)							,    word_tokenize(__magic_name__				)							,    alpha=__magic_name__							,    beta=__magic_name__							,    gamma=__magic_name__				)
									    for ref, pred in zip(__magic_name__							,    __magic_name__				)
									]
						else:
									lowerCamelCase__ :      int								=		[
									    meteor_score.single_meteor_score(__magic_name__							,    __magic_name__							,    alpha=__magic_name__							,    beta=__magic_name__							,    gamma=__magic_name__				)
									    for ref, pred in zip(__magic_name__							,    __magic_name__				)
									]

						return {"meteor": np.mean(__magic_name__				)}



