



"""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


A		=						1_6
A		=						3_2

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Accelerator     ,		lowerCamelCase_:							int = 1_6     ,		lowerCamelCase_:							str = "bert-base-cased" ):
						"""simple docstring"""






						snake_case   :			str						    = AutoTokenizer.from_pretrained(lowerCamelCase_ )
						snake_case   :			List[Any]						    = load_dataset("glue"     ,		"mrpc" )

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

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

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						snake_case   :			int						    = tokenized_datasets.rename_column("label"     ,		"labels" )

						def collate_fn(lowerCamelCase_:							Dict ):
												# 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(lowerCamelCase_     ,		padding="max_length"     ,		max_length=1_2_8     ,		return_tensors="pt" )
												return tokenizer.pad(lowerCamelCase_     ,		padding="longest"     ,		return_tensors="pt" )

						# Instantiate dataloaders.
						snake_case   :			Union[str, Any]						    = DataLoader(
						    tokenized_datasets["train"]     ,		shuffle=lowerCamelCase_     ,		collate_fn=lowerCamelCase_     ,		batch_size=lowerCamelCase_ )
						snake_case   :			str						    = DataLoader(
						    tokenized_datasets["validation"]     ,		shuffle=lowerCamelCase_     ,		collate_fn=lowerCamelCase_     ,		batch_size=lowerCamelCase_ )

						return train_dataloader, eval_dataloader

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict     ,		lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						model.eval()
						snake_case   :			Optional[int]						    = 0
						for step, batch in enumerate(lowerCamelCase_ ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												with torch.no_grad():
																		snake_case   :			Tuple						    = model(**lowerCamelCase_ )
												snake_case   :			int						    = outputs.logits.argmax(dim=-1 )
												# It is slightly faster to call this once, than multiple times
												snake_case    ,			snake_case   :			Any						    = accelerator.gather(
												    (predictions, batch["labels"]) )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(lowerCamelCase_ ) - 1:
																								snake_case   :			Optional[int]						    = predictions[: len(eval_dataloader.dataset ) - samples_seen]
																								snake_case   :			List[str]						    = references[: len(eval_dataloader.dataset ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=lowerCamelCase_     ,		references=lowerCamelCase_     ,		)

						snake_case   :			Any						    = metric.compute()
						return eval_metric["accuracy"]

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = Accelerator()

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

						set_seed(lowerCamelCase_ )
						snake_case    ,			snake_case   :			str						    = get_dataloaders(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )

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

						# Instantiate optimizer
						snake_case   :			int						    = (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						snake_case   :			str						    = optimizer_cls(params=model.parameters()     ,		lr=lowerCamelCase_ )

						if accelerator.state.deepspeed_plugin is not None:
												snake_case   :			Tuple						    = accelerator.state.deepspeed_plugin.deepspeed_config[
												    "gradient_accumulation_steps"
												]
						else:
												snake_case   :			Optional[Any]						    = 1
						snake_case   :			Optional[Any]						    = (len(lowerCamelCase_ ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												snake_case   :			Optional[int]						    = get_linear_schedule_with_warmup(
												    optimizer=lowerCamelCase_     ,		num_warmup_steps=0     ,		num_training_steps=lowerCamelCase_     ,		)
						else:
												snake_case   :			Union[str, Any]						    = DummyScheduler(lowerCamelCase_     ,		total_num_steps=lowerCamelCase_     ,		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.
						snake_case    ,			snake_case    ,			snake_case    ,			snake_case    ,			snake_case   :			List[Any]						    = accelerator.prepare(
						    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )

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

						if args.partial_train_epoch is not None:
												snake_case   :			Tuple						    = args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint )
												snake_case   :			Union[str, Any]						    = args.resume_from_checkpoint.split("epoch_" )[1]
												snake_case   :			Optional[int]						    = ""
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												snake_case   :			str						    = int(lowerCamelCase_ ) + 1
												snake_case   :			List[Any]						    = evaluation_loop(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												accelerator.print("resumed checkpoint performance:"     ,		lowerCamelCase_ )
												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:
																		snake_case   :			Any						    = json.load(lowerCamelCase_ )
																		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
						snake_case   :			Tuple						    = {}
						for epoch in range(lowerCamelCase_     ,		lowerCamelCase_ ):
												model.train()
												for step, batch in enumerate(lowerCamelCase_ ):
																		snake_case   :			Optional[int]						    = model(**lowerCamelCase_ )
																		snake_case   :			Optional[int]						    = outputs.loss
																		snake_case   :			List[Any]						    = loss / gradient_accumulation_steps
																		accelerator.backward(lowerCamelCase_ )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												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(lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Tuple						    = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
						parser.add_argument(
						    "--model_name_or_path"     ,		type=lowerCamelCase_     ,		default="bert-base-cased"     ,		help="Path to pretrained model or model identifier from huggingface.co/models."     ,		required=lowerCamelCase_     ,		)
						parser.add_argument(
						    "--output_dir"     ,		type=lowerCamelCase_     ,		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=lowerCamelCase_     ,		default=lowerCamelCase_     ,		help="If the training should continue from a checkpoint folder."     ,		)
						parser.add_argument(
						    "--partial_train_epoch"     ,		type=lowerCamelCase_     ,		default=lowerCamelCase_     ,		help="If passed, the training will stop after this number of epochs."     ,		)
						parser.add_argument(
						    "--num_epochs"     ,		type=lowerCamelCase_     ,		default=2     ,		help="Number of train epochs."     ,		)
						snake_case   :			str						    = parser.parse_args()
						snake_case   :			int						    = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}

						training_function(lowerCamelCase_     ,		lowerCamelCase_ )


if __name__ == "__main__":
			main()








"""simple docstring"""







import requests

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			List[str]						    = {"Content-Type": "application/json"}
						snake_case   :			int						    = requests.post(lowerCamelCase_     ,		json={"text": message_body}     ,		headers=lowerCamelCase_ )
						if response.status_code != 2_0_0:
												snake_case   :			str						    = (
												    "Request to slack returned an error "
												    f'''{response.status_code}, the response is:\n{response.text}'''
												)
												raise ValueError(lowerCamelCase_ )


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>')



