


'''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__    	=     16
lowerCamelCase__    	=     32






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase = 16      , __lowerCAmelCase = "bert-base-cased"   ):
		_UpperCAmelCase		:       Dict								=      AutoTokenizer.from_pretrained(__lowerCAmelCase   )
		_UpperCAmelCase		:       Union[str, Any]								=      load_dataset("glue"      , "mrpc"   )

		def tokenize_function(__lowerCAmelCase   ):
				# max_length=None => use the model max length (it's actually the default)
				_UpperCAmelCase		:       List[str]								=      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
		_UpperCAmelCase		:       Union[str, Any]								=      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
		_UpperCAmelCase		:       Union[str, Any]								=      tokenized_datasets.rename_column("label"      , "labels"   )

		def collate_fn(__lowerCAmelCase   ):
				# 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=128      , return_tensors="pt"   )
				return tokenizer.pad(__lowerCAmelCase      , padding="longest"      , return_tensors="pt"   )

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

		return train_dataloader, eval_dataloader






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

		_UpperCAmelCase		:       Union[str, Any]								=      metric.compute()
		return eval_metric["accuracy"]






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		# Initialize accelerator
		_UpperCAmelCase		:       Optional[int]								=      Accelerator()

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

		set_seed(__lowerCAmelCase   )
		_UpperCAmelCase					,    _UpperCAmelCase		:       Tuple								=      get_dataloaders(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_UpperCAmelCase		:       Optional[Any]								=      AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase      , return_dict=__lowerCAmelCase   )

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

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

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

		if args.partial_train_epoch is not None:
				_UpperCAmelCase		:       Optional[Any]								=      args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint   )
				_UpperCAmelCase		:       List[Any]								=      args.resume_from_checkpoint.split("epoch_"   )[1]
				_UpperCAmelCase		:       int								=      ""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				_UpperCAmelCase		:       List[Any]								=      int(__lowerCAmelCase   ) + 1
				_UpperCAmelCase		:       str								=      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:
						_UpperCAmelCase		:       List[str]								=      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
		_UpperCAmelCase		:       List[Any]								=      {}
		for epoch in range(__lowerCAmelCase      , __lowerCAmelCase   ):
				model.train()
				for step, batch in enumerate(__lowerCAmelCase   ):
						_UpperCAmelCase		:       Optional[Any]								=      model(**__lowerCAmelCase   )
						_UpperCAmelCase		:       List[str]								=      outputs.loss
						_UpperCAmelCase		:       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
				_UpperCAmelCase		:       Optional[Any]								=      F"""epoch_{epoch}"""
				_UpperCAmelCase		:       Any								=      os.path.join(args.output_dir      , __lowerCAmelCase   )
				accelerator.save_state(__lowerCAmelCase   )
				_UpperCAmelCase		:       Any								=      evaluation_loop(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )
				_UpperCAmelCase		:       Optional[Any]								=      accuracy
				_UpperCAmelCase		:       List[str]								=      lr_scheduler.get_lr()[0]
				_UpperCAmelCase		:       Dict								=      optimizer.param_groups[0]["lr"]
				_UpperCAmelCase		:       Dict								=      epoch
				_UpperCAmelCase		:       Optional[int]								=      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  __lowerCAmelCase ():
		_UpperCAmelCase		:       Dict								=      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."      , )
		_UpperCAmelCase		:       List[str]								=      parser.parse_args()
		_UpperCAmelCase		:       Any								=      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

		training_function(__lowerCAmelCase      , __lowerCAmelCase   )


if __name__ == "__main__":
						main()







'''simple docstring'''


# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase__    	=     abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def  __lowerCAmelCase (__lowerCAmelCase   ):
		from transformers.testing_utils import pytest_addoption_shared

		pytest_addoption_shared(__lowerCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		from transformers.testing_utils import pytest_terminal_summary_main

		_UpperCAmelCase		:       Optional[int]								=      terminalreporter.config.getoption("--make-reports"   )
		if make_reports:
				pytest_terminal_summary_main(__lowerCAmelCase      , id=__lowerCAmelCase   )



