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		(      ):



 """simple docstring"""


 __a							=	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()


SCREAMING_SNAKE_CASE			:						Tuple				  =		load("""accuracy""")



def 	__A		(      _A       ):



 """simple docstring"""


 __a							,  __a							=	eval_pred
 __a							=	np.argmax(_A				,					axis=1       )
 return metric.compute(predictions=_A				,					references=_A       )
class  A_			(							a_			):

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int							):
   super().__init__()
   __a							=	trainer

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				**__SCREAMING_SNAKE_CASE			:   int							):
   if control.should_evaluate:
    __a							=	deepcopy(__SCREAMING_SNAKE_CASE							)
    self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,				metric_key_prefix="train"							)
    return control_copy



def 	__A		(      ):



 """simple docstring"""


 __a							=	get_args()
 set_seed(args.seed       )

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

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

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

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

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

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

 __a							=	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							=	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()



import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

SCREAMING_SNAKE_CASE			:						Any				  =		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizer
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizerFast
  _SCREAMING_SNAKE_CASE     					=      True
  _SCREAMING_SNAKE_CASE     					=      True

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   super().setUp()

   # We have a SentencePiece fixture for testing
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"<pad>"
   __a							=	1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	list(self.get_tokenizer().get_vocab().keys()							)

   self.assertEqual(vocab_keys[0]  ,				"<s>NOTUSED"							)
   self.assertEqual(vocab_keys[1]  ,				"<pad>"							)
   self.assertEqual(vocab_keys[-1]  ,				"<mask>"							)
   self.assertEqual(len(__SCREAMING_SNAKE_CASE							)  ,				10_04							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,				10_05							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)
   __a							=	CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   # <unk> tokens are not the same for `rust` than for `slow`.
   # Because spm gives back raw token instead of `unk` in EncodeAsPieces
   # tokens = tokenizer.tokenize(sequence)
   __a							=	tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   if not self.test_rust_tokenizer:
    return

   __a							=	self.get_tokenizer()
   __a							=	self.get_rust_tokenizer()

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	self.get_rust_tokenizer()
   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   # fmt: off
   __a							=	{"input_ids": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # camembert is a french model. So we also use french texts.
   __a							=	[
       "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
       "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
       "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
       "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
       "telles que la traduction et la synthèse de texte.",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=__SCREAMING_SNAKE_CASE  ,				model_name="camembert-base"  ,				revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"  ,				sequences=__SCREAMING_SNAKE_CASE  ,				)


