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      (	)  ->      str:
							__UpperCAmelCase  :	int   			=      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()


_snake_case								=  load('''accuracy''')




def      _UpperCamelCase      (	snake_case__							)  ->      int:
							__UpperCAmelCase  :	Any   			=      eval_pred
							__UpperCAmelCase  :	List[str]   			=      np.argmax(_snake_case,			axis=1							)
							return metric.compute(predictions=_snake_case,			references=_snake_case							)




class     _snake_case  (  _lowercase ):
					def __init__(     self: List[Any]		,     __lowerCamelCase: Any   )			-> Tuple:
												super().__init__()
												__UpperCAmelCase  :	Tuple   			=      trainer





					def       _lowerCamelCase       (     self: Tuple		,     __lowerCamelCase: Dict		,     __lowerCamelCase: Union[str, Any]		,     __lowerCamelCase: List[Any]		,     **__lowerCamelCase: str   )			-> Any:
												if control.should_evaluate:
																			__UpperCAmelCase  :	Any   			=      deepcopy(_a   )
																			self._trainer.evaluate(eval_dataset=self._trainer.train_dataset		,     metric_key_prefix="train"   )
																			return control_copy




def      _UpperCamelCase      (	)  ->      Any:
							__UpperCAmelCase  :	Optional[Any]   			=      get_args()
							set_seed(args.seed							)

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

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

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

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

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

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

							__UpperCAmelCase  :	Any   			=      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  :	Dict   			=      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()






import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		Union[str, Any]							      =  {
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

snake_case :		Dict							      =  {
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

snake_case :		Union[str, Any]							      =  {
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     set()
	__magic_name__		:		Any	   =     word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)   )
		__magic_name__		:		int	   =     char

	__magic_name__		:		List[str]	   =     set(_snake_case   )
	return pairs





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







			def __init__(				self						,   _a						,   _a						,   _a="<s>"						,   _a="</s>"						,   _a="</s>"						,   _a="<s>"						,   _a="<unk>"						,   _a="<pad>"						,   _a="<mask>"						,   **_a						,   ):
				super().__init__(
				    bos_token=_a						,   eos_token=_a						,   unk_token=_a						,   sep_token=_a						,   cls_token=_a						,   pad_token=_a						,   mask_token=_a						,   **_a						,   )

				__magic_name__		:		Dict	   =     vocab_file
				__magic_name__		:		Tuple	   =     merges_file

				__magic_name__		:		List[Any]	   =     {}
				__magic_name__		:		List[Any]	   =     0
				__magic_name__		:		Tuple	   =     1
				__magic_name__		:		int	   =     2
				__magic_name__		:		Union[str, Any]	   =     3

				self.add_from_file(_a		)

				__magic_name__		:		Optional[int]	   =     {v: k for k, v in self.encoder.items()}

				with open(_a						,   encoding="utf-8"		) as merges_handle:
					__magic_name__		:		List[str]	   =     merges_handle.read().split("\n"		)[:-1]
				__magic_name__		:		Union[str, Any]	   =     [tuple(merge.split()[:-1]		) for merge in merges]
				__magic_name__		:		Union[str, Any]	   =     dict(zip(_a						,   range(len(_a		)		)		)		)
				__magic_name__		:		Optional[int]	   =     {}







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__		:		Optional[Any]	   =     [self.cls_token_id]
				__magic_name__		:		Union[str, Any]	   =     [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None						,   _a = False		):

				if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=_a						,   token_ids_a=_a						,   already_has_special_tokens=_a		)

				if token_ids_a is None:
					return [1] + ([0] * len(_a		)) + [1]
				return [1] + ([0] * len(_a		)) + [1, 1] + ([0] * len(_a		)) + [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Optional[Any]	   =     [self.sep_token_id]
				__magic_name__		:		Tuple	   =     [self.cls_token_id]

				if token_ids_a is None:
					return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return len(self.encoder		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return dict(self.encoder						,   **self.added_tokens_encoder		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if token in self.cache:
					return self.cache[token]
				__magic_name__		:		List[Any]	   =     tuple(_a		)
				__magic_name__		:		List[Any]	   =     tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
				__magic_name__		:		Any	   =     get_pairs(_a		)

				if not pairs:
					return token

				while True:
					__magic_name__		:		str	   =     min(_a						,   key=lambda _a		: self.bpe_ranks.get(_a						,   float("inf"		)		)		)
					if bigram not in self.bpe_ranks:
						break
					__magic_name__   ,    __magic_name__		:		List[str]	   =     bigram
					__magic_name__		:		List[str]	   =     []
					__magic_name__		:		List[str]	   =     0
					while i < len(_a		):
						try:
							__magic_name__		:		Any	   =     word.index(_a						,   _a		)
						except ValueError:
							new_word.extend(word[i:]		)
							break
						else:
							new_word.extend(word[i:j]		)
							__magic_name__		:		Tuple	   =     j

						if word[i] == first and i < len(_a		) - 1 and word[i + 1] == second:
							new_word.append(first + second		)
							i += 2
						else:
							new_word.append(word[i]		)
							i += 1
					__magic_name__		:		Union[str, Any]	   =     tuple(_a		)
					__magic_name__		:		Optional[int]	   =     new_word
					if len(_a		) == 1:
						break
					else:
						__magic_name__		:		List[Any]	   =     get_pairs(_a		)
				__magic_name__		:		Optional[int]	   =     "@@ ".join(_a		)
				__magic_name__		:		Tuple	   =     word[:-4]
				__magic_name__		:		str	   =     word
				return word







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     []

				__magic_name__		:		Dict	   =     re.findall(r"\S+\n?"						,   _a		)

				for token in words:
					split_tokens.extend(list(self.bpe(_a		).split(" "		)		)		)
				return split_tokens







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.encoder.get(_a						,   self.encoder.get(self.unk_token		)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.decoder.get(_a						,   self.unk_token		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Tuple	   =     " ".join(_a		).replace("@@ "						,   ""		).strip()
				return out_string







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				if not os.path.isdir(_a		):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
					return
				__magic_name__		:		Optional[int]	   =     os.path.join(
				    _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				__magic_name__		:		Union[str, Any]	   =     os.path.join(
				    _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(_a		):
					copyfile(self.vocab_file						,   _a		)

				if os.path.abspath(self.merges_file		) != os.path.abspath(_a		):
					copyfile(self.merges_file						,   _a		)

				return out_vocab_file, out_merge_file







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if isinstance(_a						,   _a		):
					try:
						with open(_a						,   "r"						,   encoding="utf-8"		) as fd:
							self.add_from_file(_a		)
					except FileNotFoundError as fnfe:
						raise fnfe
					except UnicodeError:
						raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''		)
					return

				__magic_name__		:		List[Any]	   =     f.readlines()
				for lineTmp in lines:
					__magic_name__		:		Optional[Any]	   =     lineTmp.strip()
					__magic_name__		:		Union[str, Any]	   =     line.rfind(" "		)
					if idx == -1:
						raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"		)
					__magic_name__		:		Optional[int]	   =     line[:idx]
					__magic_name__		:		Dict	   =     len(self.encoder		)
