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							():
					SCREAMING_SNAKE_CASE    			=       argparse.ArgumentParser()
					parser.add_argument("--model_ckpt"      , type=UpperCAmelCase__      , default="microsoft/unixcoder-base-nine"  )
					parser.add_argument("--num_epochs"      , type=UpperCAmelCase__      , default=5  )
					parser.add_argument("--batch_size"      , type=UpperCAmelCase__      , default=6  )
					parser.add_argument("--gradient_accumulation_steps"      , type=UpperCAmelCase__      , default=1  )
					parser.add_argument("--freeze"      , type=UpperCAmelCase__      , default=UpperCAmelCase__  )
					parser.add_argument("--learning_rate"      , type=UpperCAmelCase__      , default=5e-4  )
					parser.add_argument("--seed"      , type=UpperCAmelCase__      , default=0  )
					parser.add_argument("--lr_scheduler_type"      , type=UpperCAmelCase__      , default="cosine"  )
					parser.add_argument("--num_warmup_steps"      , type=UpperCAmelCase__      , default=1_0  )
					parser.add_argument("--weight_decay"      , type=UpperCAmelCase__      , default=0.01  )
					parser.add_argument("--output_dir"      , type=UpperCAmelCase__      , default="./results"  )
					return parser.parse_args()


_lowerCamelCase					:   str							=		load('''accuracy''')





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any  ):
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       eval_pred
					SCREAMING_SNAKE_CASE    			=       np.argmax(UpperCAmelCase__      , axis=1  )
					return metric.compute(predictions=UpperCAmelCase__      , references=UpperCAmelCase__  )


class 						lowercase    (     a     ):




							def __init__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]      )					->				None:



												'''simple docstring'''

												super().__init__()
												SCREAMING_SNAKE_CASE    			=       trainer





							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Dict	,							**_UpperCamelCase   :					Union[str, Any]      )					->				Dict:



												'''simple docstring'''

												if control.should_evaluate:
																	SCREAMING_SNAKE_CASE    			=       deepcopy(_UpperCamelCase      )
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,							metric_key_prefix="train"      )
																	return control_copy





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       get_args()
					set_seed(args.seed  )

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

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

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

					SCREAMING_SNAKE_CASE    			=       ClassLabel(num_classes=7      , names=list(set(train_test_validation["train"]["complexity"]  )  )  )

					def tokenize(UpperCAmelCase__     :							List[Any]  ):
										SCREAMING_SNAKE_CASE    			=       tokenizer(example["src"]      , truncation=UpperCAmelCase__      , max_length=1_0_2_4  )
										SCREAMING_SNAKE_CASE    			=       labels.straint(example["complexity"]  )
										return {
										    "input_ids": inputs["input_ids"],
										    "attention_mask": inputs["attention_mask"],
										    "label": label,
										}

					SCREAMING_SNAKE_CASE    			=       train_test_validation.map(
					    UpperCAmelCase__      , batched=UpperCAmelCase__      , remove_columns=train_test_validation["train"].column_names      , )
					SCREAMING_SNAKE_CASE    			=       DataCollatorWithPadding(tokenizer=UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       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=0.01      , metric_for_best_model="accuracy"      , run_name="complexity-java"      , report_to="wandb"      , )

					SCREAMING_SNAKE_CASE    			=       Trainer(
					    model=UpperCAmelCase__      , args=UpperCAmelCase__      , train_dataset=tokenized_datasets["train"]      , eval_dataset=tokenized_datasets["valid"]      , tokenizer=UpperCAmelCase__      , data_collator=UpperCAmelCase__      , compute_metrics=UpperCAmelCase__      , )

					print("Training..."  )
					trainer.add_callback(CustomCallback(UpperCAmelCase__  )  )
					trainer.train()


if __name__ == "__main__":
							main()





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , 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.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

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

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

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

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

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

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




