




'''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 : List[Any]  				=						16
_snake_case : Tuple  				=						32
def 						snake_case_       (UpperCamelCase			:    Accelerator					,		UpperCamelCase			:    int = 16					,		UpperCamelCase			:    str = "bert-base-cased" ):





							'''simple docstring'''


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

							def tokenize_function(UpperCamelCase			:    Optional[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			:    Optional[int]					,		UpperCamelCase			:    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(UpperCamelCase )
							_a	,				_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	,				_a	,				_a	,				_a	,				_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	,				_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 requests
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							{'''Content-Type''': '''application/json'''}
							_a      	=							requests.post(UpperCamelCase					,		json={'''text''': message_body}					,		headers=UpperCamelCase )
							if response.status_code != 200:
														_a      	=							(
														    '''Request to slack returned an error '''
														    f'{response.status_code}, the response is:\n{response.text}'
														)
														raise ValueError(UpperCamelCase )


if __name__ == "__main__":
				# Set the slack url to the one provided by Slack when you create the webhook at
				# https://my.slack.com/services/new/incoming-webhook/
				send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')

