



import argparse
import gc
import json
import os

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


UpperCAmelCase							 =					16
UpperCAmelCase							 =					32

def 			A_      (	__a     :				Dict		):

				"""simple docstring"""
				return int(x / 2**20		)

class  __snake_case      :







							'''simple docstring'''






							def __enter__(							self		):
											gc.collect()
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
											a__				      =		torch.cuda.memory_allocated()
											return self







							def __exit__(							self  ,  *a_		):
											gc.collect()
											torch.cuda.empty_cache()
											a__				      =		torch.cuda.memory_allocated()
											a__				      =		torch.cuda.max_memory_allocated()
											a__				      =		bamb(self.end - self.begin		)
											a__				      =		bamb(self.peak - self.begin		)
											# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def 			A_      (	__a     :				Accelerator ,		__a     :				int = 16 ,		__a     :				str = "bert-base-cased" ,		__a     :				int = 320 ,		__a     :				int = 160 ,		):

				"""simple docstring"""
				a__				      =		AutoTokenizer.from_pretrained(__a		)
				a__				      =		load_dataset(
				    """glue""" ,		"""mrpc""" ,		split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''}		)

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				a__				      =		datasets.map(
				    __a ,		batched=__a ,		remove_columns=["""idx""", """sentence1""", """sentence2"""] ,		load_from_cache_file=__a		)

				# 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(__a     :				Optional[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(__a ,		padding="""max_length""" ,		max_length=128 ,		return_tensors="""pt"""		)
								return tokenizer.pad(__a ,		padding="""longest""" ,		return_tensors="""pt"""		)

				# Instantiate dataloaders.
				a__				      =		DataLoader(
				    tokenized_datasets["""train"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)
				a__				      =		DataLoader(
				    tokenized_datasets["""validation"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)

				return train_dataloader, eval_dataloader

def 			A_      (	__a     :				Union[str, Any] ,		__a     :				Optional[int]		):

				"""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(__a		)
				a__      ,			a__				      =		get_dataloaders(__a ,		__a ,		__a ,		args.n_train ,		args.n_val		)

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

				# 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=__a		)

				if accelerator.state.deepspeed_plugin is not None:
								a__				      =		accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								a__				      =		1
				a__				      =		(len(__a		) * 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=__a ,		num_warmup_steps=0 ,		num_training_steps=__a ,		)
				else:
								a__				      =		DummyScheduler(__a ,		total_num_steps=__a ,		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__      ,			a__      ,			a__      ,			a__      ,			a__				      =		accelerator.prepare(
				    __a ,		__a ,		__a ,		__a ,		__a		)

				# 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__				      =		{}
				for epoch in range(__a ,		__a		):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(__a		):
																a__				      =		model(**__a		)
																a__				      =		outputs.loss
																a__				      =		loss / gradient_accumulation_steps
																accelerator.backward(__a		)
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin		)		)		)
								accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used		)		)
								accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked		)		)
								accelerator.print(
								    """Total Peak Memory consumed during the train (max): {}""".format(
								        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
								a__				      =		tracemalloc.peaked + bamb(tracemalloc.begin		)
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir ,		"""peak_memory_utilization.json"""		) ,		"""w"""		) as f:
												json.dump(__a ,		__a		)

def 			A_      (	):

				"""simple docstring"""
				a__				      =		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""		)
				parser.add_argument(
				    """--model_name_or_path""" ,		type=__a ,		default="""bert-base-cased""" ,		help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,		required=__a ,		)
				parser.add_argument(
				    """--output_dir""" ,		type=__a ,		default=""".""" ,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,		)
				parser.add_argument(
				    """--peak_memory_upper_bound""" ,		type=__a ,		default=__a ,		help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.""" ,		)
				parser.add_argument(
				    """--n_train""" ,		type=__a ,		default=320 ,		help="""Number of training examples to use.""" ,		)
				parser.add_argument(
				    """--n_val""" ,		type=__a ,		default=160 ,		help="""Number of validation examples to use.""" ,		)
				parser.add_argument(
				    """--num_epochs""" ,		type=__a ,		default=1 ,		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(__a ,		__a		)


if __name__ == "__main__":
				main()






import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
	raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
UpperCAmelCase							 =					False
UpperCAmelCase							 =					False

def 			A_      (	__a     :				Namespace		):

				"""simple docstring"""
				return TrainCommand(__a		)




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@staticmethod
							def _a      (							a_		):
											a__				      =		parser.add_parser("""train"""  ,  help="""CLI tool to train a model on a task."""		)

											train_parser.add_argument(
											    """--train_data"""  ,  type=a_  ,  required=a_  ,  help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""  ,  )
											train_parser.add_argument(
											    """--column_label"""  ,  type=a_  ,  default=0  ,  help="""Column of the dataset csv file with example labels."""		)
											train_parser.add_argument(
											    """--column_text"""  ,  type=a_  ,  default=1  ,  help="""Column of the dataset csv file with example texts."""		)
											train_parser.add_argument(
											    """--column_id"""  ,  type=a_  ,  default=2  ,  help="""Column of the dataset csv file with example ids."""		)
											train_parser.add_argument(
											    """--skip_first_row"""  ,  action="""store_true"""  ,  help="""Skip the first row of the csv file (headers)."""		)

											train_parser.add_argument("""--validation_data"""  ,  type=a_  ,  default=""""""  ,  help="""path to validation dataset."""		)
											train_parser.add_argument(
											    """--validation_split"""  ,  type=a_  ,  default=0.1  ,  help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""  ,  )

											train_parser.add_argument("""--output"""  ,  type=a_  ,  default="""./"""  ,  help="""path to saved the trained model."""		)

											train_parser.add_argument(
											    """--task"""  ,  type=a_  ,  default="""text_classification"""  ,  help="""Task to train the model on."""		)
											train_parser.add_argument(
											    """--model"""  ,  type=a_  ,  default="""bert-base-uncased"""  ,  help="""Model's name or path to stored model."""		)
											train_parser.add_argument("""--train_batch_size"""  ,  type=a_  ,  default=32  ,  help="""Batch size for training."""		)
											train_parser.add_argument("""--valid_batch_size"""  ,  type=a_  ,  default=64  ,  help="""Batch size for validation."""		)
											train_parser.add_argument("""--learning_rate"""  ,  type=a_  ,  default=3E-5  ,  help="""Learning rate."""		)
											train_parser.add_argument("""--adam_epsilon"""  ,  type=a_  ,  default=1E-0_8  ,  help="""Epsilon for Adam optimizer."""		)
											train_parser.set_defaults(func=a_		)


							def __init__(							self  ,  a_		):
											a__				      =		logging.get_logger("""transformers-cli/training"""		)

											a__				      =		"""tf""" if is_tf_available() else """torch"""

											os.makedirs(args.output  ,  exist_ok=a_		)
											a__				      =		args.output

											a__				      =		args.column_label
											a__				      =		args.column_text
											a__				      =		args.column_id

											self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''		)
											if args.task == "text_classification":
															a__				      =		TextClassificationPipeline.from_pretrained(args.model		)
											elif args.task == "token_classification":
															raise NotImplementedError
											elif args.task == "question_answering":
															raise NotImplementedError

											self.logger.info(F'''Loading dataset from {args.train_data}'''		)
											a__				      =		Processor.create_from_csv(
											    args.train_data  ,  column_label=args.column_label  ,  column_text=args.column_text  ,  column_id=args.column_id  ,  skip_first_row=args.skip_first_row  ,  )
											a__				      =		None
											if args.validation_data:
															self.logger.info(F'''Loading validation dataset from {args.validation_data}'''		)
															a__				      =		Processor.create_from_csv(
															    args.validation_data  ,  column_label=args.column_label  ,  column_text=args.column_text  ,  column_id=args.column_id  ,  skip_first_row=args.skip_first_row  ,  )

											a__				      =		args.validation_split
											a__				      =		args.train_batch_size
											a__				      =		args.valid_batch_size
											a__				      =		args.learning_rate
											a__				      =		args.adam_epsilon


							def _a      (							self		):
											if self.framework == "tf":
															return self.run_tf()
											return self.run_torch()


							def _a      (							self		):
											raise NotImplementedError







							def _a      (							self		):
											self.pipeline.fit(
											    self.train_dataset  ,  validation_data=self.valid_dataset  ,  validation_split=self.validation_split  ,  learning_rate=self.learning_rate  ,  adam_epsilon=self.adam_epsilon  ,  train_batch_size=self.train_batch_size  ,  valid_batch_size=self.valid_batch_size  ,  )

											# Save trained pipeline
											self.pipeline.save_pretrained(self.output		)

