





'''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 :Tuple     =    16
__lowerCAmelCase :Dict     =    32







def    A				(							UpperCAmelCase  ,  UpperCAmelCase = 16  ,  UpperCAmelCase = "bert-base-cased"				):
					_snake_case       :					Union[str, Any]				       =   AutoTokenizer.from_pretrained(UpperCAmelCase				)
					_snake_case       :					List[Any]				       =   load_dataset("glue"  ,  "mrpc"				)

					def tokenize_function(UpperCAmelCase				):
										# max_length=None => use the model max length (it's actually the default)
										_snake_case       :					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
					_snake_case       :					str				       =   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
					_snake_case       :					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=128  ,  return_tensors="pt"				)
										return tokenizer.pad(UpperCAmelCase  ,  padding="longest"  ,  return_tensors="pt"				)

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

					return train_dataloader, eval_dataloader







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					# Initialize accelerator
					_snake_case       :					Optional[Any]				       =   Accelerator()

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

					set_seed(UpperCAmelCase				)
					_snake_case       :					Union[str, Any]				       =   get_dataloaders(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

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

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

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

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

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

															overall_step += 1

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

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

										if best_performance < eval_metric["accuracy"]:
															_snake_case       :					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(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					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(
					    "--performance_lower_bound"  ,  type=UpperCAmelCase  ,  default=UpperCAmelCase  ,  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=UpperCAmelCase  ,  default=3  ,  help="Number of train epochs."  ,  )
					_snake_case       :					Optional[int]				       =   parser.parse_args()
					_snake_case       :					Union[str, Any]				       =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
					training_function(UpperCAmelCase  ,  UpperCAmelCase				)


if __name__ == "__main__":
							main()






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__lowerCAmelCase :Union[str, Any]     =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__lowerCAmelCase :Optional[Any]     =    typing.Union[np.floataa, int, float]  # noqa: UP007







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return np.sqrt(np.sum((np.asarray(UpperCAmelCase				) - np.asarray(UpperCAmelCase				)) ** 2				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return sum((va - va) ** 2 for va, va in zip(UpperCAmelCase  ,  UpperCAmelCase				)				) ** (1 / 2)


if __name__ == "__main__":







							def    A				(							):
												from timeit import timeit

												print("Without Numpy"				)
												print(
												    timeit(
												        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)
												print("With Numpy"				)
												print(
												    timeit(
												        "euclidean_distance([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)

							benchmark()