




'''simple docstring'''


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


_snake_case   :   Any     							=	16
_snake_case   :   Dict     							=	32
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Optional[Any] = 16				,		UpperCamelCase :						int = "bert-base-cased"   ):





							'''simple docstring'''


							_a			  =       AutoTokenizer.from_pretrained(UpperCamelCase   )
							_a			  =       load_dataset('''glue'''				,		'''mrpc'''   )

							def tokenize_function(UpperCamelCase :						Union[str, Any]   ):
														# max_length=None => use the model max length (it's actually the default)
														_a			  =       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
							_a			  =       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
							_a			  =       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.
														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.
							_a			  =       DataLoader(
							    tokenized_datasets['''train''']				,		shuffle=UpperCamelCase				,		collate_fn=UpperCamelCase				,		batch_size=UpperCamelCase   )
							_a			  =       DataLoader(
							    tokenized_datasets['''validation''']				,		shuffle=UpperCamelCase				,		collate_fn=UpperCamelCase				,		batch_size=UpperCamelCase   )

							return train_dataloader, eval_dataloader
def 	snake_case_					(UpperCamelCase :						List[str]				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							_a			  =       Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_a			  =       config["""lr"""]
							_a			  =       int(config['''num_epochs''']   )
							_a			  =       int(config['''seed''']   )
							_a			  =       int(config['''batch_size''']   )
							_a			  =       args.model_name_or_path

							set_seed(UpperCamelCase   )
							_a			  =       get_dataloaders(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )

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

							# Instantiate optimizer
							_a			  =       (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							_a			  =       optimizer_cls(params=model.parameters()				,		lr=UpperCamelCase   )

							if accelerator.state.deepspeed_plugin is not None:
														_a			  =       accelerator.state.deepspeed_plugin.deepspeed_config[
														    """gradient_accumulation_steps"""
														]
							else:
														_a			  =       1
							_a			  =       (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
							):
														_a			  =       get_linear_schedule_with_warmup(
														    optimizer=UpperCamelCase				,		num_warmup_steps=0				,		num_training_steps=UpperCamelCase				,		)
							else:
														_a			  =       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.
							_a			  =       accelerator.prepare(
							    UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )

							# We need to keep track of how many total steps we have iterated over
							_a			  =       0
							# We also need to keep track of the stating epoch so files are named properly
							_a			  =       0

							# Now we train the model
							_a			  =       evaluate.load('''glue'''				,		'''mrpc'''   )
							_a			  =       0
							_a			  =       {}
							for epoch in range(UpperCamelCase				,		UpperCamelCase   ):
														model.train()
														for step, batch in enumerate(UpperCamelCase   ):
																					_a			  =       model(**UpperCamelCase   )
																					_a			  =       outputs.loss
																					_a			  =       loss / gradient_accumulation_steps
																					accelerator.backward(UpperCamelCase   )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

														model.eval()
														_a			  =       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():
																												_a			  =       model(**UpperCamelCase   )
																					_a			  =       outputs.logits.argmax(dim=-1   )
																					# It is slightly faster to call this once, than multiple times
																					_a			  =       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:
																																			_a			  =       predictions[: len(eval_dataloader.dataset   ) - samples_seen]
																																			_a			  =       references[: len(eval_dataloader.dataset   ) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=UpperCamelCase				,		references=UpperCamelCase				,		)

														_a			  =       metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f'epoch {epoch}:'				,		UpperCamelCase   )
														_a			  =       eval_metric["""accuracy"""]

														if best_performance < eval_metric["accuracy"]:
																					_a			  =       eval_metric["""accuracy"""]

							if args.performance_lower_bound is not None:
														assert (
														    args.performance_lower_bound <= best_performance
														), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir				,		'''all_results.json'''   )				,		'''w'''   ) as f:
																					json.dump(UpperCamelCase				,		UpperCamelCase   )
def 	snake_case_					():





							'''simple docstring'''


							_a			  =       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(
							    '''--performance_lower_bound'''				,		type=UpperCamelCase				,		default=UpperCamelCase				,		help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''				,		)
							parser.add_argument(
							    '''--num_epochs'''				,		type=UpperCamelCase				,		default=3				,		help='''Number of train epochs.'''				,		)
							_a			  =       parser.parse_args()
							_a			  =       {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
							training_function(UpperCamelCase				,		UpperCamelCase   )


if __name__ == "__main__":
				main()







'''simple docstring'''


import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_snake_case   :   Any     							=	datasets.logging.get_logger(__name__)


_snake_case   :   Tuple     							=	'\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

_snake_case   :   Optional[Any]     							=	'\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

_snake_case   :   Optional[int]     							=	'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

_snake_case   :   Dict     							=	{
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}

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







							def      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""simple docstring"""


														return datasets.MetricInfo(
														    description=_DESCRIPTION							,    citation=_CITATION							,    homepage='''https://github.com/google-research/bleurt'''							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
														        {
														            '''predictions''': datasets.Value('''string'''							,    id='''sequence'''      ),
														            '''references''': datasets.Value('''string'''							,    id='''sequence'''      ),
														        }      )							,    codebase_urls=['''https://github.com/google-research/bleurt''']							,    reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']							,    )







							def      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						str      )    ->							Tuple:


														"""simple docstring"""


														if self.config_name == "default":
																					logger.warning(
																					    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
																					    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''      )
																					_a			  =       '''bleurt-base-128'''

														if self.config_name.lower() in CHECKPOINT_URLS:
																					_a			  =       self.config_name.lower()

														elif self.config_name.upper() in CHECKPOINT_URLS:
																					_a			  =       self.config_name.upper()

														else:
																					raise KeyError(
																					    F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'      )

														# download the model checkpoint specified by self.config_name and set up the scorer
														_a			  =       dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
														_a			  =       score.BleurtScorer(os.path.join(lowerCAmelCase_							,    lowerCAmelCase_      )      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.scorer.score(references=lowerCAmelCase_							,    candidates=lowerCAmelCase_      )
														return {"scores": scores}

