



"""simple docstring"""
import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)



def        lowerCAmelCase_(   )	->       str:
				_lowerCamelCase        =				argparse.ArgumentParser()
				parser.add_argument('''--model_ckpt'''						,   type=lowercase_						,   default='''microsoft/unixcoder-base-nine'''							)
				parser.add_argument('''--num_epochs'''						,   type=lowercase_						,   default=5							)
				parser.add_argument('''--batch_size'''						,   type=lowercase_						,   default=6							)
				parser.add_argument('''--gradient_accumulation_steps'''						,   type=lowercase_						,   default=1							)
				parser.add_argument('''--freeze'''						,   type=lowercase_						,   default=lowercase_							)
				parser.add_argument('''--learning_rate'''						,   type=lowercase_						,   default=5e-4							)
				parser.add_argument('''--seed'''						,   type=lowercase_						,   default=0							)
				parser.add_argument('''--lr_scheduler_type'''						,   type=lowercase_						,   default='''cosine'''							)
				parser.add_argument('''--num_warmup_steps'''						,   type=lowercase_						,   default=10							)
				parser.add_argument('''--weight_decay'''						,   type=lowercase_						,   default=0.0_1							)
				parser.add_argument('''--output_dir'''						,   type=lowercase_						,   default='''./results'''							)
				return parser.parse_args()


__SCREAMING_SNAKE_CASE  :						Any    							=      load('''accuracy''')



def        lowerCAmelCase_(   lowercase_     :							str							)	->       str:
				_lowerCamelCase  ,     _lowerCamelCase        =				eval_pred
				_lowerCamelCase        =				np.argmax(lowercase_						,   axis=1							)
				return metric.compute(predictions=lowercase_						,   references=lowercase_							)


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									super().__init__()
									_lowerCamelCase        =				trainer





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   ):
									if control.should_evaluate:
													_lowerCamelCase        =				deepcopy(lowerCamelCase__   )
													self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,       metric_key_prefix='''train'''   )
													return control_copy



def        lowerCAmelCase_(   )	->       Any:
				_lowerCamelCase        =				get_args()
				set_seed(args.seed							)

				_lowerCamelCase        =				load_dataset('''codeparrot/codecomplex'''						,   split='''train'''							)
				_lowerCamelCase        =				dataset.train_test_split(test_size=0.2							)
				_lowerCamelCase        =				train_test['''test'''].train_test_split(test_size=0.5							)
				_lowerCamelCase        =				DatasetDict(
				    {
				        '''train''': train_test['''train'''],
				        '''test''': test_validation['''train'''],
				        '''valid''': test_validation['''test'''],
				    }							)

				print('''Loading tokenizer and model'''							)
				_lowerCamelCase        =				AutoTokenizer.from_pretrained(args.model_ckpt							)
				_lowerCamelCase        =				tokenizer.eos_token
				_lowerCamelCase        =				AutoModelForSequenceClassification.from_pretrained(args.model_ckpt						,   num_labels=7							)
				_lowerCamelCase        =				model.config.eos_token_id

				if args.freeze:
								for param in model.roberta.parameters():
												_lowerCamelCase        =				False

				_lowerCamelCase        =				ClassLabel(num_classes=7						,   names=list(set(train_test_validation['''train''']['''complexity''']							)							)							)

				def tokenize(lowercase_     :							Union[str, Any]							):
								_lowerCamelCase        =				tokenizer(example['''src''']						,   truncation=lowercase_						,   max_length=10_24							)
								_lowerCamelCase        =				labels.straint(example['''complexity''']							)
								return {
								    "input_ids": inputs["input_ids"],
								    "attention_mask": inputs["attention_mask"],
								    "label": label,
								}

				_lowerCamelCase        =				train_test_validation.map(
				    lowercase_						,   batched=lowercase_						,   remove_columns=train_test_validation['''train'''].column_names						,   )
				_lowerCamelCase        =				DataCollatorWithPadding(tokenizer=lowercase_							)

				_lowerCamelCase        =				TrainingArguments(
				    output_dir=args.output_dir						,   learning_rate=args.learning_rate						,   lr_scheduler_type=args.lr_scheduler_type						,   evaluation_strategy='''epoch'''						,   save_strategy='''epoch'''						,   logging_strategy='''epoch'''						,   per_device_train_batch_size=args.batch_size						,   per_device_eval_batch_size=args.batch_size						,   num_train_epochs=args.num_epochs						,   gradient_accumulation_steps=args.gradient_accumulation_steps						,   weight_decay=0.0_1						,   metric_for_best_model='''accuracy'''						,   run_name='''complexity-java'''						,   report_to='''wandb'''						,   )

				_lowerCamelCase        =				Trainer(
				    model=lowercase_						,   args=lowercase_						,   train_dataset=tokenized_datasets['''train''']						,   eval_dataset=tokenized_datasets['''valid''']						,   tokenizer=lowercase_						,   data_collator=lowercase_						,   compute_metrics=lowercase_						,   )

				print('''Training...'''							)
				trainer.add_callback(CustomCallback(lowercase_							)							)
				trainer.train()


if __name__ == "__main__":
			main()





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   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.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

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

								# Check convergence.
								_lowerCamelCase        =				np.abs(lambda_ - lambda_previous							) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												_lowerCamelCase        =				True

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												_lowerCamelCase        =				real_input_matrix
												_lowerCamelCase        =				real_vector
								elif problem_type == "complex":
												_lowerCamelCase        =				complex_input_matrix
												_lowerCamelCase        =				complex_vector

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								_lowerCamelCase  ,     _lowerCamelCase        =				np.linalg.eigh(lowercase_							)
								# Last eigenvalue is the maximum one.
								_lowerCamelCase        =				eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								_lowerCamelCase        =				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(lowercase_							) - np.abs(lowercase_							)							) <= 1e-6


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
