






"""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 							snake_case (  A__      ,A__ = 16      ,A__ = "bert-base-cased"		):
			UpperCAmelCase_	:					str     	=   AutoTokenizer.from_pretrained(A__		)
			UpperCAmelCase_	:					Dict     	=   load_dataset("glue"      ,"mrpc"		)

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

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

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

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

			# Instantiate dataloaders.
			UpperCAmelCase_	:					int     	=   DataLoader(
			    tokenized_datasets["train"]      ,shuffle=A__      ,collate_fn=A__      ,batch_size=A__		)
			UpperCAmelCase_	:					List[str]     	=   DataLoader(
			    tokenized_datasets["validation"]      ,shuffle=A__      ,collate_fn=A__      ,batch_size=A__		)

			return train_dataloader, eval_dataloader

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			model.eval()
			UpperCAmelCase_	:					Optional[Any]     	=   0
			for step, batch in enumerate(A__		):
						# 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(**A__		)
						UpperCAmelCase_	:					Optional[Any]     	=   outputs.logits.argmax(dim=-1		)
						# It is slightly faster to call this once, than multiple times
						UpperCAmelCase_	,      UpperCAmelCase_	:					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(A__		) - 1:
												UpperCAmelCase_	:					List[Any]     	=   predictions[: len(eval_dataloader.dataset		) - samples_seen]
												UpperCAmelCase_	:					Union[str, Any]     	=   references[: len(eval_dataloader.dataset		) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=A__      ,references=A__      ,)

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

def 							snake_case (  A__      ,A__		):
			# Initialize accelerator
			UpperCAmelCase_	:					List[Any]     	=   Accelerator()

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

			set_seed(A__		)
			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   get_dataloaders(A__      ,A__      ,A__		)

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

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

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

			# 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_	:					int     	=   0
			UpperCAmelCase_	:					Optional[Any]     	=   evaluate.load("glue"      ,"mrpc"		)
			UpperCAmelCase_	:					Optional[Any]     	=   num_epochs

			if args.partial_train_epoch is not None:
						UpperCAmelCase_	:					Optional[int]     	=   args.partial_train_epoch

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint		)
						UpperCAmelCase_	:					List[str]     	=   args.resume_from_checkpoint.split("epoch_"		)[1]
						UpperCAmelCase_	:					str     	=   ""
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						UpperCAmelCase_	:					List[str]     	=   int(A__		) + 1
						UpperCAmelCase_	:					List[str]     	=   evaluation_loop(A__      ,A__      ,A__      ,A__		)
						accelerator.print("resumed checkpoint performance:"      ,A__		)
						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_	:					Optional[int]     	=   json.load(A__		)
									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_	:					int     	=   {}
			for epoch in range(A__      ,A__		):
						model.train()
						for step, batch in enumerate(A__		):
									UpperCAmelCase_	:					str     	=   model(**A__		)
									UpperCAmelCase_	:					Any     	=   outputs.loss
									UpperCAmelCase_	:					Any     	=   loss / gradient_accumulation_steps
									accelerator.backward(A__		)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1
						UpperCAmelCase_	:					Any     	=   F"""epoch_{epoch}"""
						UpperCAmelCase_	:					Optional[Any]     	=   os.path.join(args.output_dir      ,A__		)
						accelerator.save_state(A__		)
						UpperCAmelCase_	:					Optional[int]     	=   evaluation_loop(A__      ,A__      ,A__      ,A__		)
						UpperCAmelCase_	:					List[Any]     	=   accuracy
						UpperCAmelCase_	:					str     	=   lr_scheduler.get_lr()[0]
						UpperCAmelCase_	:					Dict     	=   optimizer.param_groups[0]["lr"]
						UpperCAmelCase_	:					List[str]     	=   epoch
						UpperCAmelCase_	:					str     	=   overall_step
						accelerator.print(F"""epoch {epoch}:"""      ,A__		)

						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(A__      ,A__		)

def 							snake_case (  ):
			UpperCAmelCase_	:					Optional[Any]     	=   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."		)
			parser.add_argument(
			    "--model_name_or_path"      ,type=A__      ,default="bert-base-cased"      ,help="Path to pretrained model or model identifier from huggingface.co/models."      ,required=A__      ,)
			parser.add_argument(
			    "--output_dir"      ,type=A__      ,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=A__      ,default=A__      ,help="If the training should continue from a checkpoint folder."      ,)
			parser.add_argument(
			    "--partial_train_epoch"      ,type=A__      ,default=A__      ,help="If passed, the training will stop after this number of epochs."      ,)
			parser.add_argument(
			    "--num_epochs"      ,type=A__      ,default=2      ,help="Number of train epochs."      ,)
			UpperCAmelCase_	:					List[Any]     	=   parser.parse_args()
			UpperCAmelCase_	:					List[str]     	=   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

			training_function(A__      ,A__		)


if __name__ == "__main__":
			main()




"""simple docstring"""






from math import pow, sqrt


def      UpperCAmelCase__			(							*lowerCAmelCase__	:float						)					->       bool:
      '''simple docstring'''


      lowercase								=      len(lowerCAmelCase__						) > 0 and all(value > 0.0 for value in values						)
      return result


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError("""Input Error: Molar mass values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )


def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a   ,						2						)   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )






def      UpperCAmelCase__			(							lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float   ,						lowerCAmelCase__	:float						)					->       float | ValueError:
      '''simple docstring'''


      return (
          round(pow(effusion_rate_a / effusion_rate_a   ,						2						) / molar_mass   ,						6						)
          if validate(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)
          else ValueError(
              """Input Error: Molar mass and effusion rate values must greater than 0."""						)
      )



