

"""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_   :   int        =       1_6
snake_case_   :   List[str]        =       3_2



def   lowercase_	(    _lowercase       :		Accelerator							,      _lowercase       :		int = 16							,      _lowercase       :		str = "bert-base-cased"	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				AutoTokenizer.from_pretrained(_lowercase	)
					UpperCAmelCase			:   Tuple      =				load_dataset("glue"							,      "mrpc"	)

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

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

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

					def collate_fn(_lowercase       :		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(_lowercase							,      padding="max_length"							,      max_length=1_28							,      return_tensors="pt"	)
										return tokenizer.pad(_lowercase							,      padding="longest"							,      return_tensors="pt"	)

					# Instantiate dataloaders.
					UpperCAmelCase			:   List[Any]      =				DataLoader(
					    tokenized_datasets["train"]							,      shuffle=_lowercase							,      collate_fn=_lowercase							,      batch_size=_lowercase	)
					UpperCAmelCase			:   str      =				DataLoader(
					    tokenized_datasets["validation"]							,      shuffle=_lowercase							,      collate_fn=_lowercase							,      batch_size=_lowercase	)

					return train_dataloader, eval_dataloader



def   lowercase_	(    _lowercase       :		Any							,      _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				Accelerator()

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

					set_seed(_lowercase	)
					UpperCAmelCase      ,							UpperCAmelCase			:   Tuple      =				get_dataloaders(_lowercase							,      _lowercase							,      _lowercase	)

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

					# Instantiate optimizer
					UpperCAmelCase			:   List[str]      =				(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					UpperCAmelCase			:   Tuple      =				optimizer_cls(params=model.parameters()							,      lr=_lowercase	)

					if accelerator.state.deepspeed_plugin is not None:
										UpperCAmelCase			:   Optional[Any]      =				accelerator.state.deepspeed_plugin.deepspeed_config[
										    "gradient_accumulation_steps"
										]
					else:
										UpperCAmelCase			:   Optional[Any]      =				1
					UpperCAmelCase			:   Tuple      =				(len(_lowercase	) * 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			:   Union[str, Any]      =				get_linear_schedule_with_warmup(
										    optimizer=_lowercase							,      num_warmup_steps=0							,      num_training_steps=_lowercase							,      )
					else:
										UpperCAmelCase			:   Optional[int]      =				DummyScheduler(_lowercase							,      total_num_steps=_lowercase							,      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			:   int      =				accelerator.prepare(
					    _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase	)

					# We need to keep track of how many total steps we have iterated over
					UpperCAmelCase			:   List[str]      =				0
					# We also need to keep track of the stating epoch so files are named properly
					UpperCAmelCase			:   Union[str, Any]      =				0

					# Now we train the model
					UpperCAmelCase			:   str      =				evaluate.load("glue"							,      "mrpc"	)
					UpperCAmelCase			:   List[Any]      =				0
					UpperCAmelCase			:   Tuple      =				{}
					for epoch in range(_lowercase							,      _lowercase	):
										model.train()
										for step, batch in enumerate(_lowercase	):
															UpperCAmelCase			:   Optional[int]      =				model(**_lowercase	)
															UpperCAmelCase			:   str      =				outputs.loss
															UpperCAmelCase			:   List[Any]      =				loss / gradient_accumulation_steps
															accelerator.backward(_lowercase	)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

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

										UpperCAmelCase			:   str      =				metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""							,      _lowercase	)
										UpperCAmelCase			:   List[str]      =				eval_metric["accuracy"]

										if best_performance < eval_metric["accuracy"]:
															UpperCAmelCase			:   Dict      =				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(_lowercase							,      _lowercase	)



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Any      =				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."	)
					parser.add_argument(
					    "--model_name_or_path"							,      type=_lowercase							,      default="bert-base-cased"							,      help="Path to pretrained model or model identifier from huggingface.co/models."							,      required=_lowercase							,      )
					parser.add_argument(
					    "--output_dir"							,      type=_lowercase							,      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=_lowercase							,      default=_lowercase							,      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=_lowercase							,      default=3							,      help="Number of train epochs."							,      )
					UpperCAmelCase			:   Optional[Any]      =				parser.parse_args()
					UpperCAmelCase			:   Dict      =				{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
					training_function(_lowercase							,      _lowercase	)


if __name__ == "__main__":
	main()





"""simple docstring"""






snake_case_   :   List[str]        =       6_5_5_2_1



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				1
					UpperCAmelCase			:   Optional[int]      =				0
					for plain_chr in plain_text:
										UpperCAmelCase			:   int      =				(a + ord(_lowercase	)) % MOD_ADLER
										UpperCAmelCase			:   str      =				(b + a) % MOD_ADLER
					return (b << 16) | a


