
"""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 			_snake_case    (					)    ->     Dict:
							lowerCamelCase_	:							int									=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__	:    List[Any]			 = load('accuracy')

def 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     Optional[Any]:
							lowerCamelCase_	:							Dict									=eval_pred
							lowerCamelCase_	:							Tuple									=np.argmax(lowerCamelCase__    ,						axis=1  )
							return metric.compute(predictions=lowerCamelCase__    ,						references=lowerCamelCase__  )







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  int       ,	snake_case__					:  int ):
										super().__init__()
										lowerCamelCase_	:							Optional[int]									=trainer


			def      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  Dict       ,	snake_case__					:  List[Any]       ,	snake_case__					:  List[str]       ,	**snake_case__					:  List[str] ):
										if control.should_evaluate:
																	lowerCamelCase_	:							List[str]									=deepcopy(snake_case__ )
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,	metric_key_prefix="train" )
																	return control_copy


def 			_snake_case    (					)    ->     Any:
							lowerCamelCase_	:							int									=get_args()
							set_seed(args.seed  )

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

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

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

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

							def tokenize(lowerCamelCase__     :   List[Any]  ):
														lowerCamelCase_	:							Any									=tokenizer(example["src"]    ,						truncation=lowerCamelCase__    ,						max_length=1_024  )
														lowerCamelCase_	:							Dict									=labels.straint(example["complexity"]  )
														return {
														    "input_ids": inputs["input_ids"],
														    "attention_mask": inputs["attention_mask"],
														    "label": label,
														}

							lowerCamelCase_	:							Union[str, Any]									=train_test_validation.map(
							    lowerCamelCase__    ,						batched=lowerCamelCase__    ,						remove_columns=train_test_validation["train"].column_names    ,						)
							lowerCamelCase_	:							Optional[Any]									=DataCollatorWithPadding(tokenizer=lowerCamelCase__  )

							lowerCamelCase_	:							int									=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_	:							Any									=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()











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]  )    ->     Optional[int]:
							if not head:
														return True
							# split the list to two parts
							lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=head.next, head
							while fast and fast.next:
														lowerCamelCase_	:							Optional[Any]									=fast.next.next
														lowerCamelCase_	:							str									=slow.next
							lowerCamelCase_	:							Tuple									=slow.next
							lowerCamelCase_	:							Any									=None  # Don't forget here! But forget still works!
							# reverse the second part
							lowerCamelCase_	:							List[str]									=None
							while second:
														lowerCamelCase_	:							Any									=second.next
														lowerCamelCase_	:							Union[str, Any]									=node
														lowerCamelCase_	:							Union[str, Any]									=second
														lowerCamelCase_	:							Optional[Any]									=nxt
							# compare two parts
							# second part has the same or one less node
							while node:
														if node.val != head.val:
																					return False
														lowerCamelCase_	:							List[str]									=node.next
														lowerCamelCase_	:							Optional[Any]									=head.next
							return True

def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     Optional[int]:
							if not head or not head.next:
														return True

							# 1. Get the midpoint (slow)
							lowerCamelCase_	:							List[str]									=head
							while fast and fast.next:
														lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=fast.next.next, slow.next

							# 2. Push the second half into the stack
							lowerCamelCase_	:							List[Any]									=[slow.val]
							while slow.next:
														lowerCamelCase_	:							List[Any]									=slow.next
														stack.append(slow.val  )

							# 3. Comparison
							while stack:
														if stack.pop() != cur.val:
																					return False
														lowerCamelCase_	:							Union[str, Any]									=cur.next

							return True

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Optional[Any]:
							if not head or not head.next:
														return True
							lowerCamelCase_	:							Union[str, Any]									={}
							lowerCamelCase_	:							List[Any]									=0
							while head:
														if head.val in d:
																					d[head.val].append(lowerCamelCase__  )
														else:
																					lowerCamelCase_	:							List[str]									=[pos]
														lowerCamelCase_	:							Optional[int]									=head.next
														pos += 1
							lowerCamelCase_	:							Union[str, Any]									=pos - 1
							lowerCamelCase_	:							Optional[int]									=0
							for v in d.values():
														if len(lowerCamelCase__  ) % 2 != 0:
																					middle += 1
														else:
																					lowerCamelCase_	:							Optional[Any]									=0
																					for i in range(0    ,						len(lowerCamelCase__  )  ):
																												if v[i] + v[len(lowerCamelCase__  ) - 1 - step] != checksum:
																																			return False
																												step += 1
														if middle > 1:
																					return False
							return True



