



"""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


SCREAMING_SNAKE_CASE_				= 16
SCREAMING_SNAKE_CASE_				= 32



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = 16, SCREAMING_SNAKE_CASE__ = "bert-base-cased"	)			->      str:
							a_     :      int 						=     AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__	)
							a_     :      Dict 						=     load_dataset("glue", "mrpc"	)

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

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

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

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

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

							return train_dataloader, eval_dataloader



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
							# Initialize accelerator
							a_     :      Any 						=     Accelerator()

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

							set_seed(SCREAMING_SNAKE_CASE__	)
							a_     :      Optional[Any] 						=     get_dataloaders(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

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

							# Instantiate optimizer
							a_     :      Union[str, Any] 						=     (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							a_     :      Dict 						=     optimizer_cls(params=model.parameters(), lr=SCREAMING_SNAKE_CASE__	)

							if accelerator.state.deepspeed_plugin is not None:
														a_     :      List[Any] 						=     accelerator.state.deepspeed_plugin.deepspeed_config[
														    "gradient_accumulation_steps"
														]
							else:
														a_     :      Tuple 						=     1
							a_     :      List[Any] 						=     (len(SCREAMING_SNAKE_CASE__	) * 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_     :      int 						=     get_linear_schedule_with_warmup(
														    optimizer=SCREAMING_SNAKE_CASE__, num_warmup_steps=0, num_training_steps=SCREAMING_SNAKE_CASE__, )
							else:
														a_     :      int 						=     DummyScheduler(SCREAMING_SNAKE_CASE__, total_num_steps=SCREAMING_SNAKE_CASE__, 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_     :      Any 						=     accelerator.prepare(
							    SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

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

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

																					overall_step += 1

														model.eval()
														a_     :      str 						=     0
														for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device	)
																					with torch.no_grad():
																												a_     :      Optional[Any] 						=     model(**SCREAMING_SNAKE_CASE__	)
																					a_     :      Any 						=     outputs.logits.argmax(dim=-1	)
																					# It is slightly faster to call this once, than multiple times
																					a_     :      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(SCREAMING_SNAKE_CASE__	) - 1:
																																			a_     :      Tuple 						=     predictions[: len(eval_dataloader.dataset	) - samples_seen]
																																			a_     :      Union[str, Any] 						=     references[: len(eval_dataloader.dataset	) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=SCREAMING_SNAKE_CASE__, references=SCREAMING_SNAKE_CASE__, )

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

														if best_performance < eval_metric["accuracy"]:
																					a_     :      Tuple 						=     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(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)



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


if __name__ == "__main__":
					main()




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = 10, SCREAMING_SNAKE_CASE__ = 1_000, SCREAMING_SNAKE_CASE__ = True	)			->      int:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), "Invalid type of value(s) specified to function!"

		if min_val > max_val:
				raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"	)
		return min_val if option else max_val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		return int((number_a + number_a) / 2	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      None:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), 'argument values must be type of "int"'

		if lower > higher:
				raise ValueError("argument value for lower and higher must be(lower > higher)"	)

		if not lower < to_guess < higher:
				raise ValueError(
				    "guess value must be within the range of lower and higher value"	)

		def answer(SCREAMING_SNAKE_CASE__	) -> str:
				if number > to_guess:
						return "high"
				elif number < to_guess:
						return "low"
				else:
						return "same"

		print("started..."	)

		a_     :      List[str] 						=     lower
		a_     :      Dict 						=     higher

		a_     :      str 						=     []

		while True:
				a_     :      List[str] 						=     get_avg(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
				last_numbers.append(SCREAMING_SNAKE_CASE__	)

				if answer(SCREAMING_SNAKE_CASE__	) == "low":
						a_     :      Optional[Any] 						=     number
				elif answer(SCREAMING_SNAKE_CASE__	) == "high":
						a_     :      Union[str, Any] 						=     number
				else:
						break

		print(F"""guess the number : {last_numbers[-1]}"""	)
		print(F"""details : {last_numbers!s}"""	)



def        lowerCAmelCase_						(							)			->      None:
		a_     :      str 						=     int(input("Enter lower value : "	).strip()	)
		a_     :      Dict 						=     int(input("Enter high value : "	).strip()	)
		a_     :      Optional[Any] 						=     int(input("Enter value to guess : "	).strip()	)
		guess_the_number(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					main()