
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 :		Any							      =  16
snake_case :		Optional[Any]							      =  32






def   lowerCAmelCase_						(					_snake_case      :  Accelerator      ,   _snake_case      :  int = 16      ,   _snake_case      :  str = "bert-base-cased"   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     AutoTokenizer.from_pretrained(_snake_case   )
	__magic_name__		:		Optional[int]	   =     load_dataset("glue"      ,   "mrpc"   )

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

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

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

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

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

	return train_dataloader, eval_dataloader






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Any      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Union[str, Any]   )				->		str:







	'''simple docstring'''


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

	__magic_name__		:		List[str]	   =     metric.compute()
	return eval_metric["accuracy"]






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Tuple   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     Accelerator()

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

	set_seed(_snake_case   )
	__magic_name__   ,    __magic_name__		:		Any	   =     get_dataloaders(_snake_case      ,   _snake_case      ,   _snake_case   )

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

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

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

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

	if args.partial_train_epoch is not None:
		__magic_name__		:		Tuple	   =     args.partial_train_epoch

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

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

		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(_snake_case      ,   _snake_case   )






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


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

	training_function(_snake_case      ,   _snake_case   )


if __name__ == "__main__":
				main()


def   lowerCAmelCase_						(					_snake_case      :  list[list[int | float]]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     len(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     len(matrix[0]   )
	__magic_name__		:		Union[str, Any]	   =     min(_snake_case      ,   _snake_case   )

	for row in range(_snake_case   ):
		# Check if diagonal element is not zero
		if matrix[row][row] != 0:
			# Eliminate all the elements below the diagonal
			for col in range(row + 1      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     matrix[col][row] / matrix[row][row]
				for i in range(_snake_case      ,   _snake_case   ):
					matrix[col][i] -= multiplier * matrix[row][i]
		else:
			# Find a non-zero diagonal element to swap rows
			__magic_name__		:		str	   =     True
			for i in range(row + 1      ,   _snake_case   ):
				if matrix[i][row] != 0:
					__magic_name__   ,    __magic_name__		:		List[str]	   =     matrix[i], matrix[row]
					__magic_name__		:		Union[str, Any]	   =     False
					break
			if reduce:
				rank -= 1
				for i in range(_snake_case   ):
					__magic_name__		:		Any	   =     matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
			row -= 1

	return rank


if __name__ == "__main__":
				import doctest

				doctest.testmod()
