
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


UpperCAmelCase_		:				Dict									=     16
UpperCAmelCase_		:				Optional[int]									=     32




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase = 16	,     lowerCamelCase = "bert-base-cased"   ):
				__magic_name__	:	List[Any]								=AutoTokenizer.from_pretrained(_A   )
				__magic_name__	:	List[str]								=load_dataset("""glue"""	,     """mrpc"""   )

				def tokenize_function(lowerCamelCase   ):
								# max_length=None => use the model max length (it's actually the default)
								__magic_name__	:	int								=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
				__magic_name__	:	Tuple								=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
				__magic_name__	:	Dict								=tokenized_datasets.rename_column("""label"""	,     """labels"""   )

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

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

				return train_dataloader, eval_dataloader




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

				__magic_name__	:	Optional[int]								=metric.compute()
				return eval_metric["accuracy"]




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Tuple								=Accelerator()

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

				set_seed(_A   )
				__magic_name__	:	Optional[Any]								=get_dataloaders(_A	,     _A	,     _A   )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__magic_name__	:	List[Any]								=AutoModelForSequenceClassification.from_pretrained(_A	,     return_dict=_A   )

				# Instantiate optimizer
				__magic_name__	:	Any								=(
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				__magic_name__	:	Optional[int]								=optimizer_cls(params=model.parameters()	,     lr=_A   )

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

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

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

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

												overall_step += 1
								__magic_name__	:	Union[str, Any]								=F"epoch_{epoch}"
								__magic_name__	:	List[Any]								=os.path.join(args.output_dir	,     _A   )
								accelerator.save_state(_A   )
								__magic_name__	:	str								=evaluation_loop(_A	,     _A	,     _A	,     _A   )
								__magic_name__	:	List[Any]								=accuracy
								__magic_name__	:	Union[str, Any]								=lr_scheduler.get_lr()[0]
								__magic_name__	:	Any								=optimizer.param_groups[0]["lr"]
								__magic_name__	:	List[Any]								=epoch
								__magic_name__	:	Dict								=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 		lowerCAmelCase_   (	):
				__magic_name__	:	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."""	,     )
				__magic_name__	:	Optional[Any]								=parser.parse_args()
				__magic_name__	:	int								={"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

				training_function(_A	,     _A   )


if __name__ == "__main__":
							main()










import numpy as np




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = 1E-12	,     lowerCamelCase = 100	,     ):
				assert np.shape(lowerCamelCase   )[0] == np.shape(lowerCamelCase   )[1]
				# Ensure proper dimensionality.
				assert np.shape(lowerCamelCase   )[0] == np.shape(lowerCamelCase   )[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowerCamelCase   ) == np.iscomplexobj(lowerCamelCase   )
				__magic_name__	:	List[Any]								=np.iscomplexobj(lowerCamelCase   )
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowerCamelCase	,     input_matrix.conj().T   )

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				__magic_name__	:	List[Any]								=False
				__magic_name__	:	List[Any]								=0
				__magic_name__	:	List[str]								=0
				__magic_name__	:	str								=1E12

				while not convergence:
								# Multiple matrix by the vector.
								__magic_name__	:	Dict								=np.dot(lowerCamelCase	,     lowerCamelCase   )
								# Normalize the resulting output vector.
								__magic_name__	:	List[str]								=w / np.linalg.norm(lowerCamelCase   )
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								__magic_name__	:	Any								=vector.conj().T if is_complex else vector.T
								__magic_name__	:	List[str]								=np.dot(lowerCamelCase	,     np.dot(lowerCamelCase	,     lowerCamelCase   )   )

								# Check convergence.
								__magic_name__	:	List[str]								=np.abs(lambda_ - lambda_previous   ) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__magic_name__	:	int								=True

								__magic_name__	:	Optional[Any]								=lambda_

				if is_complex:
								__magic_name__	:	Union[str, Any]								=np.real(lambda_   )

				return lambda_, vector




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Optional[int]								=np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]   )
				__magic_name__	:	Any								=np.array([41, 4, 20]   )
				__magic_name__	:	Optional[Any]								=real_input_matrix.astype(np.complexaaa   )
				__magic_name__	:	str								=np.triu(1J * complex_input_matrix	,     1   )
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__magic_name__	:	List[Any]								=np.array([41, 4, 20]   ).astype(np.complexaaa   )

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__magic_name__	:	Union[str, Any]								=real_input_matrix
												__magic_name__	:	List[Any]								=real_vector
								elif problem_type == "complex":
												__magic_name__	:	Optional[Any]								=complex_input_matrix
												__magic_name__	:	Tuple								=complex_vector

								# Our implementation.
								__magic_name__				, __magic_name__	:	Any								=power_iteration(lowerCamelCase	,     lowerCamelCase   )

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__magic_name__				, __magic_name__	:	int								=np.linalg.eigh(lowerCamelCase   )
								# Last eigenvalue is the maximum one.
								__magic_name__	:	List[str]								=eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__magic_name__	:	Dict								=eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max   ) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowerCamelCase   ) - np.abs(lowerCamelCase   )   ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




