
"""simple docstring"""



import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
    from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCamelCase__          =	25_00_04
UpperCamelCase__          =	25_00_20





@require_sentencepiece
@require_tokenizers
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Tuple           =     MBartTokenizer
      UpperCamelCase							:   Union[str, Any]           =     MBartTokenizerFast
      UpperCamelCase							:   Optional[int]           =     True
      UpperCamelCase							:   Union[str, Any]           =     True



      def  __snake_case							(   self      ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__	:      Dict     							=					MBartTokenizer(UpperCamelCase_     ,	keep_accents=UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					MBartTokenizer(UpperCamelCase_     ,	keep_accents=UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					tokenizer.tokenize('This is a test'      )
          self.assertListEqual(UpperCamelCase_     ,	['▁This', '▁is', '▁a', '▁t', 'est']      )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(UpperCamelCase_      )     ,	[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     ,	)

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
          self.assertListEqual(
              UpperCamelCase_     ,	[
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '9',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  'é',
                  '.',
              ]     ,	)
          UpperCAmelCase__	:      Dict     							=					tokenizer.convert_tokens_to_ids(UpperCamelCase_      )
          self.assertListEqual(
              UpperCamelCase_     ,	[
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                  #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
              ]     ,	)

          UpperCAmelCase__	:      List[str]     							=					tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
          self.assertListEqual(
              UpperCamelCase_     ,	[
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '<unk>',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  '<unk>',
                  '.',
              ]     ,	)



      def  __snake_case							(   self      ):
          if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

          UpperCAmelCase__	:      Tuple     							=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                  UpperCAmelCase__	:      int     							=					self.rust_tokenizer_class.from_pretrained(UpperCamelCase_     ,	**UpperCamelCase_      )
                  UpperCAmelCase__	:      Tuple     							=					self.tokenizer_class.from_pretrained(UpperCamelCase_     ,	**UpperCamelCase_      )

                  UpperCAmelCase__	:      Tuple     							=					tempfile.mkdtemp()

                  UpperCAmelCase__	:      Optional[int]     							=					tokenizer_r.save_pretrained(UpperCamelCase_      )
                  UpperCAmelCase__	:      List[Any]     							=					tokenizer_p.save_pretrained(UpperCamelCase_      )

                  # Checks it save with the same files + the tokenizer.json file for the fast one
                  self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )
                  UpperCAmelCase__	:      Optional[Any]     							=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
                  self.assertSequenceEqual(UpperCamelCase_     ,	UpperCamelCase_      )

                  # Checks everything loads correctly in the same way
                  UpperCAmelCase__	:      Tuple     							=					tokenizer_r.from_pretrained(UpperCamelCase_      )
                  UpperCAmelCase__	:      Tuple     							=					tokenizer_p.from_pretrained(UpperCamelCase_      )

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(UpperCamelCase_     ,	UpperCamelCase_      )      )
                      # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                      # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                  shutil.rmtree(UpperCamelCase_      )

                  # Save tokenizer rust, legacy_format=True
                  UpperCAmelCase__	:      int     							=					tempfile.mkdtemp()

                  UpperCAmelCase__	:      Tuple     							=					tokenizer_r.save_pretrained(UpperCamelCase_     ,	legacy_format=UpperCamelCase_      )
                  UpperCAmelCase__	:      str     							=					tokenizer_p.save_pretrained(UpperCamelCase_      )

                  # Checks it save with the same files
                  self.assertSequenceEqual(UpperCamelCase_     ,	UpperCamelCase_      )

                  # Checks everything loads correctly in the same way
                  UpperCAmelCase__	:      Optional[Any]     							=					tokenizer_r.from_pretrained(UpperCamelCase_      )
                  UpperCAmelCase__	:      Dict     							=					tokenizer_p.from_pretrained(UpperCamelCase_      )

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

                  shutil.rmtree(UpperCamelCase_      )

                  # Save tokenizer rust, legacy_format=False
                  UpperCAmelCase__	:      int     							=					tempfile.mkdtemp()

                  UpperCAmelCase__	:      str     							=					tokenizer_r.save_pretrained(UpperCamelCase_     ,	legacy_format=UpperCamelCase_      )
                  UpperCAmelCase__	:      int     							=					tokenizer_p.save_pretrained(UpperCamelCase_      )

                  # Checks it saved the tokenizer.json file
                  self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )

                  # Checks everything loads correctly in the same way
                  UpperCAmelCase__	:      str     							=					tokenizer_r.from_pretrained(UpperCamelCase_      )
                  UpperCAmelCase__	:      List[Any]     							=					tokenizer_p.from_pretrained(UpperCamelCase_      )

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

                  shutil.rmtree(UpperCamelCase_      )





@require_torch
@require_sentencepiece
@require_tokenizers
class 				a			(					unittest.TestCase      ):
      UpperCamelCase							:   Optional[int]           =     """facebook/mbart-large-en-ro"""
      UpperCamelCase							:   Tuple           =     [
          """ UN Chief Says There Is No Military Solution in Syria""",
          """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
      ]
      UpperCamelCase							:   int           =     [
          """Şeful ONU declară că nu există o soluţie militară în Siria""",
          """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
          """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
          """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
      ]
      UpperCamelCase							:   Tuple           =     [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]



      @classmethod
      def  __snake_case							(   cls      ):
          UpperCAmelCase__	:      MBartTokenizer     							=					MBartTokenizer.from_pretrained(
              cls.checkpoint_name     ,	src_lang='en_XX'     ,	tgt_lang='ro_RO'      )
          UpperCAmelCase__	:      Tuple     							=					1
          return cls



      def  __snake_case							(   self      ):
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']     ,	250_001      )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']     ,	250_004      )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']     ,	250_020      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          self.assertIn(UpperCamelCase_     ,	self.tokenizer.all_special_ids      )
          UpperCAmelCase__	:      List[Any]     							=					[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
          UpperCAmelCase__	:      Union[str, Any]     							=					self.tokenizer.decode(UpperCamelCase_     ,	skip_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					self.tokenizer.decode(generated_ids[1:]     ,	skip_special_tokens=UpperCamelCase_      )
          self.assertEqual(UpperCamelCase_     ,	UpperCamelCase_      )
          self.assertNotIn(self.tokenizer.eos_token     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					['this is gunna be a long sentence ' * 20]
          assert isinstance(src_text[0]     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					10
          UpperCAmelCase__	:      int     							=					self.tokenizer(UpperCamelCase_     ,	max_length=UpperCamelCase_     ,	truncation=UpperCamelCase_      ).input_ids[0]
          self.assertEqual(ids[-2]     ,	2      )
          self.assertEqual(ids[-1]     ,	UpperCamelCase_      )
          self.assertEqual(len(UpperCamelCase_      )     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      )     ,	[250_026, 250_001]      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					tempfile.mkdtemp()
          UpperCAmelCase__	:      List[Any]     							=					self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					MBartTokenizer.from_pretrained(UpperCamelCase_      )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,	UpperCamelCase_      )



      @require_torch
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					self.tokenizer(self.src_text     ,	text_target=self.tgt_text     ,	padding=UpperCamelCase_     ,	return_tensors='pt'      )
          UpperCAmelCase__	:      Union[str, Any]     							=					shift_tokens_right(batch['labels']     ,	self.tokenizer.pad_token_id      )

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
          assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
          assert batch.decoder_input_ids[1][-1] == 2
          assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



      @require_torch
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					self.tokenizer(
              self.src_text     ,	text_target=self.tgt_text     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=len(self.expected_src_tokens      )     ,	return_tensors='pt'     ,	)

          UpperCAmelCase__	:      List[Any]     							=					shift_tokens_right(batch['labels']     ,	self.tokenizer.pad_token_id      )

          self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_      )

          self.assertEqual((2, 14)     ,	batch.input_ids.shape      )
          self.assertEqual((2, 14)     ,	batch.attention_mask.shape      )
          UpperCAmelCase__	:      Any     							=					batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens     ,	UpperCamelCase_      )
          self.assertEqual(2     ,	batch.decoder_input_ids[0, -1]      )  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens     ,	[]      )
          self.assertEqual(self.tokenizer.suffix_tokens     ,	[self.tokenizer.eos_token_id, EN_CODE]      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.tokenizer(self.src_text     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=3     ,	return_tensors='pt'      )
          UpperCAmelCase__	:      List[str]     							=					self.tokenizer(
              text_target=self.tgt_text     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=10     ,	return_tensors='pt'      )
          UpperCAmelCase__	:      int     							=					targets['input_ids']
          UpperCAmelCase__	:      List[str]     							=					shift_tokens_right(UpperCamelCase_     ,	self.tokenizer.pad_token_id      )

          self.assertEqual(batch.input_ids.shape[1]     ,	3      )
          self.assertEqual(batch.decoder_input_ids.shape[1]     ,	10      )



      @require_torch
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.tokenizer._build_translation_inputs(
              'A test'     ,	return_tensors='pt'     ,	src_lang='en_XX'     ,	tgt_lang='ar_AR'      )

          self.assertEqual(
              nested_simplify(UpperCamelCase_      )     ,	{
                  # A, test, EOS, en_XX
                  'input_ids': [[62, 3_034, 2, 250_004]],
                  'attention_mask': [[1, 1, 1, 1]],
                  # ar_AR
                  'forced_bos_token_id': 250_001,
              }     ,	)



"""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()

