

'''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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1_6   ,UpperCamelCase = "bert-base-cased"  )    ->    Tuple:
	UpperCAmelCase_		:      Optional[Any]       	=					AutoTokenizer.from_pretrained(UpperCamelCase  )
	UpperCAmelCase_		:      Dict       	=					load_dataset('glue'   ,'mrpc'  )

	def tokenize_function(UpperCamelCase  ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_		:      List[Any]       	=					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
	UpperCAmelCase_		:      Any       	=					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
	UpperCAmelCase_		:      Optional[Any]       	=					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=1_2_8   ,return_tensors='pt'  )
		return tokenizer.pad(UpperCamelCase   ,padding='longest'   ,return_tensors='pt'  )

	# Instantiate dataloaders.
	UpperCAmelCase_		:      str       	=					DataLoader(
	    tokenized_datasets['train']   ,shuffle=UpperCamelCase   ,collate_fn=UpperCamelCase   ,batch_size=UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					DataLoader(
	    tokenized_datasets['validation']   ,shuffle=UpperCamelCase   ,collate_fn=UpperCamelCase   ,batch_size=UpperCamelCase  )

	return train_dataloader, eval_dataloader







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

	UpperCAmelCase_		:      Dict       	=					metric.compute()
	return eval_metric["accuracy"]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	# Initialize accelerator
	UpperCAmelCase_		:      Optional[int]       	=					Accelerator()

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

	set_seed(UpperCamelCase  )
	UpperCAmelCase_		:      str       	=					get_dataloaders(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

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

	# 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=UpperCamelCase  )

	if accelerator.state.deepspeed_plugin is not None:
		UpperCAmelCase_		:      str       	=					accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		UpperCAmelCase_		:      Optional[Any]       	=					1
	UpperCAmelCase_		:      Union[str, Any]       	=					(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
	):
		UpperCAmelCase_		:      Optional[int]       	=					get_linear_schedule_with_warmup(
		    optimizer=UpperCamelCase   ,num_warmup_steps=0   ,num_training_steps=UpperCamelCase   ,)
	else:
		UpperCAmelCase_		:      Union[str, Any]       	=					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.
	UpperCAmelCase_		:      Any       	=					accelerator.prepare(
	    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

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

	if args.partial_train_epoch is not None:
		UpperCAmelCase_		:      str       	=					args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint  )
		UpperCAmelCase_		:      Union[str, Any]       	=					args.resume_from_checkpoint.split('epoch_'  )[1]
		UpperCAmelCase_		:      int       	=					''
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		UpperCAmelCase_		:      Tuple       	=					int(UpperCamelCase  ) + 1
		UpperCAmelCase_		:      Tuple       	=					evaluation_loop(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
		accelerator.print('resumed checkpoint performance:'   ,UpperCamelCase  )
		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_		:      Dict       	=					json.load(UpperCamelCase  )
			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_		:      Any       	=					{}
	for epoch in range(UpperCamelCase   ,UpperCamelCase  ):
		model.train()
		for step, batch in enumerate(UpperCamelCase  ):
			UpperCAmelCase_		:      List[Any]       	=					model(**UpperCamelCase  )
			UpperCAmelCase_		:      Any       	=					outputs.loss
			UpperCAmelCase_		:      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
		UpperCAmelCase_		:      int       	=					f"""epoch_{epoch}"""
		UpperCAmelCase_		:      str       	=					os.path.join(args.output_dir   ,UpperCamelCase  )
		accelerator.save_state(UpperCamelCase  )
		UpperCAmelCase_		:      Dict       	=					evaluation_loop(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
		UpperCAmelCase_		:      Dict       	=					accuracy
		UpperCAmelCase_		:      Union[str, Any]       	=					lr_scheduler.get_lr()[0]
		UpperCAmelCase_		:      int       	=					optimizer.param_groups[0]['lr']
		UpperCAmelCase_		:      int       	=					epoch
		UpperCAmelCase_		:      int       	=					overall_step
		accelerator.print(f"""epoch {epoch}:"""   ,UpperCamelCase  )

		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(UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      str       	=					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(
	    '--resume_from_checkpoint'   ,type=UpperCamelCase   ,default=UpperCamelCase   ,help='If the training should continue from a checkpoint folder.'   ,)
	parser.add_argument(
	    '--partial_train_epoch'   ,type=UpperCamelCase   ,default=UpperCamelCase   ,help='If passed, the training will stop after this number of epochs.'   ,)
	parser.add_argument(
	    '--num_epochs'   ,type=UpperCamelCase   ,default=2   ,help='Number of train epochs.'   ,)
	UpperCAmelCase_		:      Tuple       	=					parser.parse_args()
	UpperCAmelCase_		:      List[Any]       	=					{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

	training_function(UpperCamelCase   ,UpperCamelCase  )


if __name__ == "__main__":
						main()









'''simple docstring'''


import qiskit







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    qiskit.result.counts.Counts:
	UpperCAmelCase_		:      Tuple       	=					qiskit.Aer.get_backend('aer_simulator'  )

	UpperCAmelCase_		:      List[str]       	=					qiskit.QuantumCircuit(4   ,2  )
	# encode inputs in qubits 0 and 1
	if bita == 1:
		qc_ha.x(0  )
	if bita == 1:
		qc_ha.x(1  )
	qc_ha.barrier()

	# use cnots to write XOR of the inputs on qubit2
	qc_ha.cx(0   ,2  )
	qc_ha.cx(1   ,2  )

	# use ccx / toffoli gate to write AND of the inputs on qubit3
	qc_ha.ccx(0   ,1   ,3  )
	qc_ha.barrier()

	# extract outputs
	qc_ha.measure(2   ,0  )  # extract XOR value
	qc_ha.measure(3   ,1  )  # extract AND value

	# Execute the circuit on the qasm simulator
	UpperCAmelCase_		:      List[str]       	=					qiskit.execute(UpperCamelCase   ,UpperCamelCase   ,shots=1_0_0_0  )

	# Return the histogram data of the results of the experiment
	return job.result().get_counts(UpperCamelCase  )


if __name__ == "__main__":
						lowerCAmelCase__										= half_adder(1, 1)
						print(F'Half Adder Output Qubit Counts: {counts}')






