


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 				_UpperCAmelCase							():
       _A :			Union[str, Any]							       =					argparse.ArgumentParser()
       parser.add_argument("--model_ckpt"							,				type=snake_case__							,				default="microsoft/unixcoder-base-nine"		)
       parser.add_argument("--num_epochs"							,				type=snake_case__							,				default=5		)
       parser.add_argument("--batch_size"							,				type=snake_case__							,				default=6		)
       parser.add_argument("--gradient_accumulation_steps"							,				type=snake_case__							,				default=1		)
       parser.add_argument("--freeze"							,				type=snake_case__							,				default=snake_case__		)
       parser.add_argument("--learning_rate"							,				type=snake_case__							,				default=5E-4		)
       parser.add_argument("--seed"							,				type=snake_case__							,				default=0		)
       parser.add_argument("--lr_scheduler_type"							,				type=snake_case__							,				default="cosine"		)
       parser.add_argument("--num_warmup_steps"							,				type=snake_case__							,				default=10		)
       parser.add_argument("--weight_decay"							,				type=snake_case__							,				default=0.01		)
       parser.add_argument("--output_dir"							,				type=snake_case__							,				default="./results"		)
       return parser.parse_args()


lowerCAmelCase__ 						=					load('accuracy')
def 				_UpperCAmelCase							(UpperCamelCase__	:    Optional[int]		):
       _A       ,     _A :			List[str]							       =					eval_pred
       _A :			Optional[int]							       =					np.argmax(snake_case__							,				axis=1		)
       return metric.compute(predictions=snake_case__							,				references=snake_case__		)







class 	lowerCAmelCase__			(							_lowercase):




     '''simple docstring'''


     def __init__(		self					,		__lowerCamelCase)     ->							None:
            super().__init__()
            _A :			Optional[int]							       =					trainer


     def 					_lowerCamelCase					(		self					,		__lowerCamelCase					,		__lowerCamelCase					,		__lowerCamelCase					,		**__lowerCamelCase)     ->							int:
            if control.should_evaluate:
                   _A :			Tuple							       =					deepcopy(A_)
                   self._trainer.evaluate(eval_dataset=self._trainer.train_dataset					,		metric_key_prefix="train")
                   return control_copy




def 				_UpperCAmelCase							():
       _A :			Optional[Any]							       =					get_args()
       set_seed(args.seed		)

       _A :			Optional[Any]							       =					load_dataset("codeparrot/codecomplex"							,				split="train"		)
       _A :			List[Any]							       =					dataset.train_test_split(test_size=0.2		)
       _A :			Union[str, Any]							       =					train_test["test"].train_test_split(test_size=0.5		)
       _A :			List[str]							       =					DatasetDict(
           {
               "train": train_test["train"],
               "test": test_validation["train"],
               "valid": test_validation["test"],
           }		)

       print("Loading tokenizer and model"		)
       _A :			Dict							       =					AutoTokenizer.from_pretrained(args.model_ckpt		)
       _A :			List[str]							       =					tokenizer.eos_token
       _A :			List[str]							       =					AutoModelForSequenceClassification.from_pretrained(args.model_ckpt							,				num_labels=7		)
       _A :			Optional[Any]							       =					model.config.eos_token_id

       if args.freeze:
              for param in model.roberta.parameters():
                     _A :			Optional[int]							       =					False

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

       def tokenize(UpperCamelCase__	:    Any		):
              _A :			Union[str, Any]							       =					tokenizer(example["src"]							,				truncation=snake_case__							,				max_length=1024		)
              _A :			Optional[int]							       =					labels.straint(example["complexity"]		)
              return {
                  "input_ids": inputs["input_ids"],
                  "attention_mask": inputs["attention_mask"],
                  "label": label,
              }

       _A :			str							       =					train_test_validation.map(
           snake_case__							,				batched=snake_case__							,				remove_columns=train_test_validation["train"].column_names							,				)
       _A :			Optional[int]							       =					DataCollatorWithPadding(tokenizer=snake_case__		)

       _A :			str							       =					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"							,				)

       _A :			str							       =					Trainer(
           model=snake_case__							,				args=snake_case__							,				train_dataset=tokenized_datasets["train"]							,				eval_dataset=tokenized_datasets["valid"]							,				tokenizer=snake_case__							,				data_collator=snake_case__							,				compute_metrics=snake_case__							,				)

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


if __name__ == "__main__":
    main()


"""simple docstring"""


from __future__ import annotations






def    _snake_case					(		snake_case__ :  tuple[int, int]						,   snake_case__ :  int  ):
	A ,   A     = position
	A     = [
	    (y + 1, x + 2),
	    (y - 1, x + 2),
	    (y + 1, x - 2),
	    (y - 1, x - 2),
	    (y + 2, x + 1),
	    (y + 2, x - 1),
	    (y - 2, x + 1),
	    (y - 2, x - 1),
	]
	A     = []

	for position in positions:
		A ,   A     = position
		if 0 <= y_test < n and 0 <= x_test < n:
			permissible_positions.append(snake_case__  )

	return permissible_positions






def    _snake_case					(		snake_case__ :  list[list[int]]  ):
	return not any(elem == 0 for row in board for elem in row  )






def    _snake_case					(		snake_case__ :  list[list[int]]						,   snake_case__ :  tuple[int, int]						,   snake_case__ :  int  ):

	if is_complete(snake_case__  ):
		return True

	for position in get_valid_pos(snake_case__						,   len(snake_case__  )  ):
		A ,   A     = position

		if board[y][x] == 0:
			A     = curr + 1
			if open_knight_tour_helper(snake_case__						,   snake_case__						,   curr + 1  ):
				return True
			A     = 0

	return False






def    _snake_case					(		snake_case__ :  int  ):
	A     = [[0 for i in range(snake_case__  )] for j in range(snake_case__  )]

	for i in range(snake_case__  ):
		for j in range(snake_case__  ):
			A     = 1
			if open_knight_tour_helper(snake_case__						,   (i, j)						,   1  ):
				return board
			A     = 0

	A     = F'Open Kight Tour cannot be performed on a board of size {n}'
	raise ValueError(snake_case__  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()