
'''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


lowerCAmelCase_	:		Optional[Any]			=     16
lowerCAmelCase_	:		int			=     32
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase = 1_6     ,UpperCAmelCase = "bert-base-cased"					)     ->			Tuple:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						AutoTokenizer.from_pretrained(UpperCAmelCase__					)
						_UpperCamelCase			:			Optional[int]   			=						load_dataset("glue"     ,"mrpc"					)

						def tokenize_function(UpperCAmelCase					):
												# max_length=None => use the model max length (it's actually the default)
												_UpperCamelCase			:			Optional[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
						_UpperCamelCase			:			Optional[Any]   			=						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
						_UpperCamelCase			:			Tuple   			=						tokenized_datasets.rename_column("label"     ,"labels"					)

						def collate_fn(UpperCAmelCase					):
												# 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=1_2_8     ,return_tensors="pt"					)
												return tokenizer.pad(UpperCAmelCase__     ,padding="longest"     ,return_tensors="pt"					)

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

						return train_dataloader, eval_dataloader
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Tuple:




						'''simple docstring'''
						model.eval()
						_UpperCamelCase			:			List[str]   			=						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():
																		_UpperCamelCase			:			Union[str, Any]   			=						model(**UpperCAmelCase__					)
												_UpperCamelCase			:			int   			=						outputs.logits.argmax(dim=-1					)
												# It is slightly faster to call this once, than multiple times
												_UpperCamelCase			:			List[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(UpperCAmelCase__					) - 1:
																								_UpperCamelCase			:			Optional[Any]   			=						predictions[: len(eval_dataloader.dataset					) - samples_seen]
																								_UpperCamelCase			:			List[str]   			=						references[: len(eval_dataloader.dataset					) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=UpperCAmelCase__     ,references=UpperCAmelCase__     ,)

						_UpperCamelCase			:			List[str]   			=						metric.compute()
						return eval_metric["accuracy"]
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			List[str]:




						'''simple docstring'''
						# Initialize accelerator
						_UpperCamelCase			:			Any   			=						Accelerator()

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

						set_seed(UpperCAmelCase__					)
						_UpperCamelCase			:			Tuple   			=						get_dataloaders(UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__					)

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

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

						if accelerator.state.deepspeed_plugin is not None:
												_UpperCamelCase			:			Tuple   			=						accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												_UpperCamelCase			:			List[Any]   			=						1
						_UpperCamelCase			:			List[str]   			=						(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
						):
												_UpperCamelCase			:			Tuple   			=						get_linear_schedule_with_warmup(
												    optimizer=UpperCAmelCase__     ,num_warmup_steps=0     ,num_training_steps=UpperCAmelCase__     ,)
						else:
												_UpperCamelCase			:			str   			=						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.
						_UpperCamelCase			:			Optional[int]   			=						accelerator.prepare(
						    UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__					)

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

						if args.partial_train_epoch is not None:
												_UpperCamelCase			:			List[str]   			=						args.partial_train_epoch

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

																		overall_step += 1
												_UpperCamelCase			:			List[str]   			=						f'''epoch_{epoch}'''
												_UpperCamelCase			:			Dict   			=						os.path.join(args.output_dir     ,UpperCAmelCase__					)
												accelerator.save_state(UpperCAmelCase__					)
												_UpperCamelCase			:			Dict   			=						evaluation_loop(UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__					)
												_UpperCamelCase			:			Optional[int]   			=						accuracy
												_UpperCamelCase			:			List[str]   			=						lr_scheduler.get_lr()[0]
												_UpperCamelCase			:			List[str]   			=						optimizer.param_groups[0]["""lr"""]
												_UpperCamelCase			:			List[str]   			=						epoch
												_UpperCamelCase			:			List[str]   			=						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							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[int]   			=						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."     ,)
						_UpperCamelCase			:			Dict   			=						parser.parse_args()
						_UpperCamelCase			:			Tuple   			=						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}

						training_function(UpperCAmelCase__     ,UpperCAmelCase__					)


if __name__ == "__main__":
			main()



'''simple docstring'''





import re
from pathlib import Path
from unittest import TestCase

import pytest




@pytest.mark.integration
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def  snake_case__				(						self					:						Dict       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										with open(lowercase__       ,		encoding="utf-8"					) as input_file:
																_UpperCamelCase			:			Optional[int]   			=						re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"					)
																_UpperCamelCase			:			Dict   			=						input_file.read()
																_UpperCamelCase			:			Dict   			=						regexp.search(lowercase__					)

										return match





				def  snake_case__				(						self					:						str       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										with open(lowercase__       ,		encoding="utf-8"					) as input_file:
																_UpperCamelCase			:			str   			=						re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"       ,		re.DOTALL					)
																_UpperCamelCase			:			Optional[Any]   			=						input_file.read()
																# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
																_UpperCamelCase			:			List[str]   			=						regexp.finditer(lowercase__					)

										_UpperCamelCase			:			Any   			=						[match for match in matches if match is not None and match.group(1					) is not None]
										return matches[0] if matches else None





				def  snake_case__				(						self					:						Optional[int]					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						Path("./datasets"					)
										_UpperCamelCase			:			List[Any]   			=						list(dataset_paths.absolute().glob("**/*.py"					)					)

										for dataset in dataset_files:
																if self._no_encoding_on_file_open(str(lowercase__					)					):
																						raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''					)







				def  snake_case__				(						self					:						Optional[Any]					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						Path("./datasets"					)
										_UpperCamelCase			:			Any   			=						list(dataset_paths.absolute().glob("**/*.py"					)					)

										for dataset in dataset_files:
																if self._no_print_statements(str(lowercase__					)					):
																						raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.'''					)


