
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__       (       ):




			"""simple docstring"""


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


_A     =  load("accuracy")






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			eval_pred
			lowerCAmelCase_      			=			np.argmax(__lowerCAmelCase					,							axis=1       )
			return metric.compute(predictions=__lowerCAmelCase					,							references=__lowerCAmelCase       )





class 			_lowerCAmelCase      (			lowercase_ ):




				def __init__(      self       ,       _UpperCamelCase  )       ->   str:
							super().__init__()
							lowerCAmelCase_      			=			trainer




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   Any:







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






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			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(__lowerCAmelCase						:						Optional[Any]       ):
						lowerCAmelCase_      			=			tokenizer(example["src"]					,							truncation=__lowerCAmelCase					,							max_length=1024       )
						lowerCAmelCase_      			=			labels.straint(example["complexity"]       )
						return {
						    "input_ids": inputs["input_ids"],
						    "attention_mask": inputs["attention_mask"],
						    "label": label,
						}

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

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

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

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


if __name__ == "__main__":
	main()





def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			if upper_limit < 0:
						raise ValueError("Limit for the Catalan sequence must be ≥ 0"       )

			lowerCAmelCase_      			=			[0] * (upper_limit + 1)

			# Base case: C(0) = C(1) = 1
			lowerCAmelCase_      			=			1
			if upper_limit > 0:
						lowerCAmelCase_      			=			1

			# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
			for i in range(2					,							upper_limit + 1       ):
						for j in range(__lowerCAmelCase       ):
									catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

			return catalan_list


if __name__ == "__main__":
			print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
			print("\n*** Enter -1 at any time to quit ***")
			print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
			try:
						while True:
									_A     =  int(input().strip())
									if N < 0:
												print("\n********* Goodbye!! ************")
												break
									else:
												print(f"""The Catalan numbers from 0 through {N} are:""")
												print(catalan_numbers(N))
												print("Try another upper limit for the sequence: ", end="")
			except (NameError, ValueError):
						print("\n********* Invalid input, goodbye! ************\n")

			import doctest

			doctest.testmod()



