





import numpy as np







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     (0, 0)
						UpperCAmelCase_								=     None
						UpperCAmelCase_								=     0
						UpperCAmelCase_								=     0
						UpperCAmelCase_								=     0





					def __eq__(						self    :  Tuple ,					__lowercase    :  List[str]		):






						'''simple docstring'''







						return self.position == cell.position





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						print(self.position		)







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  Tuple ,					__lowercase    :  Optional[Any]=(5, 5)		):






						'''simple docstring'''







						UpperCAmelCase_								=     np.zeros(__lowercase		)
						UpperCAmelCase_								=     world_size[0]
						UpperCAmelCase_								=     world_size[1]





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						print(self.w		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     [
						    (-1, -1),
						    (-1, 0),
						    (-1, 1),
						    (0, -1),
						    (0, 1),
						    (1, -1),
						    (1, 0),
						    (1, 1),
						]
						UpperCAmelCase_								=     cell.position[0]
						UpperCAmelCase_								=     cell.position[1]
						UpperCAmelCase_								=     []
						for n in neughbour_cord:
							UpperCAmelCase_								=     current_x + n[0]
							UpperCAmelCase_								=     current_y + n[1]
							if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
								UpperCAmelCase_								=     Cell()
								UpperCAmelCase_								=     (x, y)
								UpperCAmelCase_								=     cell
								neighbours.append(__lowercase		)
						return neighbours




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     []
	_open.append(A   )

	while _open:
		UpperCAmelCase_								=     np.argmin([n.f for n in _open]   )
		UpperCAmelCase_								=     _open[min_f]
		_closed.append(_open.pop(A   )   )
		if current == goal:
			break
		for n in world.get_neigbours(A   ):
			for c in _closed:
				if c == n:
					continue
			UpperCAmelCase_								=     current.g + 1
			UpperCAmelCase_						,	UpperCAmelCase_								=     n.position
			UpperCAmelCase_						,	UpperCAmelCase_								=     goal.position
			UpperCAmelCase_								=     (ya - ya) ** 2 + (xa - xa) ** 2
			UpperCAmelCase_								=     n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(A   )
	UpperCAmelCase_								=     []
	while current.parent is not None:
		path.append(current.position   )
		UpperCAmelCase_								=     current.parent
	path.append(current.position   )
	return path[::-1]


if __name__ == "__main__":
				UpperCamelCase__   : List[Any]					  = Gridworld()
				# Start position and goal
				UpperCamelCase__   : List[Any]					  = Cell()
				UpperCamelCase__   : Any					  = (0, 0)
				UpperCamelCase__   : Optional[Any]					  = Cell()
				UpperCamelCase__   : Optional[int]					  = (4, 4)
				print(f"path from {start.position} to {goal.position}")
				UpperCamelCase__   : Tuple					  = astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCamelCase__   : List[Any]					  = 1
				print(world.w)









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    A_(			):
	UpperCAmelCase_								=     argparse.ArgumentParser()
	parser.add_argument("""--model_ckpt"""				,      type=A				,      default="""microsoft/unixcoder-base-nine"""   )
	parser.add_argument("""--num_epochs"""				,      type=A				,      default=5   )
	parser.add_argument("""--batch_size"""				,      type=A				,      default=6   )
	parser.add_argument("""--gradient_accumulation_steps"""				,      type=A				,      default=1   )
	parser.add_argument("""--freeze"""				,      type=A				,      default=A   )
	parser.add_argument("""--learning_rate"""				,      type=A				,      default=5E-4   )
	parser.add_argument("""--seed"""				,      type=A				,      default=0   )
	parser.add_argument("""--lr_scheduler_type"""				,      type=A				,      default="""cosine"""   )
	parser.add_argument("""--num_warmup_steps"""				,      type=A				,      default=10   )
	parser.add_argument("""--weight_decay"""				,      type=A				,      default=0.01   )
	parser.add_argument("""--output_dir"""				,      type=A				,      default="""./results"""   )
	return parser.parse_args()


UpperCamelCase__   : Any					  = load("""accuracy""")




def    A_(			A   ):
	UpperCAmelCase_						,	UpperCAmelCase_								=     eval_pred
	UpperCAmelCase_								=     np.argmax(A				,      axis=1   )
	return metric.compute(predictions=A				,      references=A   )







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  str ,					__lowercase    :  Any		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     trainer





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  int ,					__lowercase    :  Tuple ,					__lowercase    :  Dict ,					**__lowercase    :  Dict		):






						'''simple docstring'''







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




def    A_(			):
	UpperCAmelCase_								=     get_args()
	set_seed(args.seed   )

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

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

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

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

	def tokenize(A   ):
		UpperCAmelCase_								=     tokenizer(example["""src"""]				,      truncation=A				,      max_length=1024   )
		UpperCAmelCase_								=     labels.straint(example["""complexity"""]   )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

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

	UpperCAmelCase_								=     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"""				,      )

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

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


if __name__ == "__main__":
				main()


