

"""simple docstring"""


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


a       :Optional[Any]     =			get_tests_dir("fixtures/test_sentencepiece.model")
a       :List[Any]     =			get_tests_dir("fixtures/test_sentencepiece_bpe.model")

a       :Dict     =			"pt" if is_torch_available() else "tf"

@require_sentencepiece
@require_tokenizers
class   __a				(UpperCamelCase_ ,						unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :Optional[int]							=			CamembertTokenizer
     _SCREAMING_SNAKE_CASE    :Optional[int]							=			CamembertTokenizerFast
     _SCREAMING_SNAKE_CASE    :str							=			True
     _SCREAMING_SNAKE_CASE    :Tuple							=			True

     def        _a      (    self	)	->   Dict:


           """simple docstring"""


           super().setUp()

           # We have a SentencePiece fixture for testing
           SCREAMING_SNAKE_CASE__ :   int   		=						CamembertTokenizer(UpperCamelCase_	)
           tokenizer.save_pretrained(self.tmpdirname	)

     def        _a      (    self	)	->   List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						'<pad>'
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						1

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

     def        _a      (    self	)	->   List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						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(UpperCamelCase_	)						,     1_004	)

     def        _a      (    self	)	->   List[str]:


           """simple docstring"""


           self.assertEqual(self.get_tokenizer().vocab_size						,     1_005	)

     def        _a      (    self	)	->   List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Dict   		=						CamembertTokenizer(UpperCamelCase_	)
           tokenizer.save_pretrained(self.tmpdirname	)
           SCREAMING_SNAKE_CASE__ :   str   		=						CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						'I was born in 92000, and this is falsé.'

           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						tokenizer.encode(UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   Dict   		=						rust_tokenizer.encode(UpperCamelCase_	)
           self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   str   		=						tokenizer.encode(UpperCamelCase_						,     add_special_tokens=UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						rust_tokenizer.encode(UpperCamelCase_						,     add_special_tokens=UpperCamelCase_	)
           self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

           # <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)
           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						tokenizer.convert_ids_to_tokens(UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   str   		=						rust_tokenizer.tokenize(UpperCamelCase_	)
           self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

     def        _a      (    self	)	->   Any:


           """simple docstring"""


           if not self.test_rust_tokenizer:
                 return

           SCREAMING_SNAKE_CASE__ :   Any   		=						self.get_tokenizer()
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						self.get_rust_tokenizer()

           SCREAMING_SNAKE_CASE__ :   List[str]   		=						'I was born in 92000, and this is falsé.'

           SCREAMING_SNAKE_CASE__ :   List[str]   		=						tokenizer.tokenize(UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   str   		=						rust_tokenizer.tokenize(UpperCamelCase_	)
           self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						tokenizer.encode(UpperCamelCase_						,     add_special_tokens=UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						rust_tokenizer.encode(UpperCamelCase_						,     add_special_tokens=UpperCamelCase_	)
           self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   str   		=						self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						tokenizer.encode(UpperCamelCase_	)
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						rust_tokenizer.encode(UpperCamelCase_	)
           self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)





     @slow
     def        _a      (    self	)	->   Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Any   		=						{'input_ids': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 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.
           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						[
               '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=UpperCamelCase_						,     model_name="""camembert-base"""						,     revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""						,     sequences=UpperCamelCase_						,     )






"""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 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      List[Any]     							=					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()


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

    UpperCAmelCase__	:      Optional[int]     							=					load_dataset('codeparrot/codecomplex'    ,split='train' )
    UpperCAmelCase__	:      Tuple     							=					dataset.train_test_split(test_size=0.2 )
    UpperCAmelCase__	:      List[Any]     							=					train_test['test'].train_test_split(test_size=0.5 )
    UpperCAmelCase__	:      Tuple     							=					DatasetDict(
        {
            'train': train_test['train'],
            'test': test_validation['train'],
            'valid': test_validation['test'],
        } )

    print('Loading tokenizer and model' )
    UpperCAmelCase__	:      str     							=					AutoTokenizer.from_pretrained(args.model_ckpt )
    UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.eos_token
    UpperCAmelCase__	:      Optional[Any]     							=					AutoModelForSequenceClassification.from_pretrained(args.model_ckpt    ,num_labels=7 )
    UpperCAmelCase__	:      Dict     							=					model.config.eos_token_id

    if args.freeze:
        for param in model.roberta.parameters():
            UpperCAmelCase__	:      Optional[int]     							=					False

    UpperCAmelCase__	:      Optional[Any]     							=					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__	:      Any     							=					labels.straint(example['complexity'] )
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      Union[str, 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()

