
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()		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	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=1_0 )
		parser.add_argument("""--weight_decay"""						,  type=lowerCAmelCase						,  default=0.01 )
		parser.add_argument("""--output_dir"""						,  type=lowerCAmelCase						,  default="""./results""" )
		return parser.parse_args()


lowerCamelCase__	=							load("accuracy")


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	eval_pred
		_UpperCamelCase						    =	np.argmax(lowerCAmelCase						,  axis=1 )
		return metric.compute(predictions=lowerCAmelCase						,  references=lowerCAmelCase )







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a				)					->      None:


								'''simple docstring'''

								super().__init__()
								_UpperCamelCase						    =	trainer







						def  A_				(			self       ,							a       ,							a       ,							a       ,							**a				)					->      Union[str, Any]:


								'''simple docstring'''

								if control.should_evaluate:
										_UpperCamelCase						    =	deepcopy(a				)
										self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,							metric_key_prefix="""train"""				)
										return control_copy




def 							__A()		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	get_args()
		set_seed(args.seed )

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

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

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

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

		def tokenize(lowerCAmelCase ):
				_UpperCamelCase						    =	tokenizer(example["""src"""]						,  truncation=lowerCAmelCase						,  max_length=1_0_2_4 )
				_UpperCamelCase						    =	labels.straint(example["""complexity"""] )
				return {
				    "input_ids": inputs["input_ids"],
				    "attention_mask": inputs["attention_mask"],
				    "label": label,
				}

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

		_UpperCamelCase						    =	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						    =	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()





import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__	=							get_tests_dir("fixtures/test_sentencepiece.model")







@require_sentencepiece
@require_tokenizers
class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		XLNetTokenizer
						UpperCamelCase_							: Optional[int]	=		XLNetTokenizerFast
						UpperCamelCase_							: Dict	=		True
						UpperCamelCase_							: Dict	=		True

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								super().setUp()

								# We have a SentencePiece fixture for testing
								_UpperCamelCase						    =	XLNetTokenizer(a       ,							keep_accents=a				)
								tokenizer.sanitize_special_tokens()
								tokenizer.save_pretrained(self.tmpdirname				)

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	"""<s>"""
								_UpperCamelCase						    =	1

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

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	list(self.get_tokenizer().get_vocab().keys()				)

								self.assertEqual(vocab_keys[0]       ,							"""<unk>"""				)
								self.assertEqual(vocab_keys[1]       ,							"""<s>"""				)
								self.assertEqual(vocab_keys[-1]       ,							"""<eod>"""				)
								self.assertEqual(len(a				)       ,							10_06				)

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								self.assertEqual(self.get_tokenizer().vocab_size       ,							10_00				)

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	XLNetTokenizer(a       ,							keep_accents=a				)

								_UpperCamelCase						    =	tokenizer.tokenize("""This is a test"""				)
								self.assertListEqual(a       ,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(a				)       ,							[2_85, 46, 10, 1_70, 3_82]				)

								_UpperCamelCase						    =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
								self.assertListEqual(
								    a       ,							[
								        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						    =	tokenizer.convert_tokens_to_ids(a				)
								self.assertListEqual(a       ,							[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]				)

								_UpperCamelCase						    =	tokenizer.convert_ids_to_tokens(a				)
								self.assertListEqual(
								    a       ,							[
								        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  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	XLNetTokenizer(a       ,							do_lower_case=a				)
								_UpperCamelCase						    =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
								self.assertListEqual(
								    a       ,							[
								        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""",
								        """se""",
								        """.""",
								    ]       ,							)
								self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)       ,							["""▁he""", """ll""", """o"""]				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	XLNetTokenizer(a       ,							do_lower_case=a				)
								_UpperCamelCase						    =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
								self.assertListEqual(
								    a       ,							[
								        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""",
								        """se""",
								        """.""",
								    ]       ,							)

						@slow
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	XLNetTokenizer.from_pretrained("""xlnet-base-cased"""				)

								_UpperCamelCase						    =	tokenizer.encode("""sequence builders"""       ,							add_special_tokens=a				)
								_UpperCamelCase						    =	tokenizer.encode("""multi-sequence build"""       ,							add_special_tokens=a				)

								_UpperCamelCase						    =	tokenizer.build_inputs_with_special_tokens(a				)
								_UpperCamelCase						    =	tokenizer.build_inputs_with_special_tokens(a       ,							a				)

								assert encoded_sentence == text + [4, 3]
								assert encoded_pair == text + [4] + text_a + [4, 3]







						@slow
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	{"""input_ids""": [[17, 2_14_42, 2_70, 17, 10, 1_46_45, 3_18, 34, 17, 45_46, 31_45, 7_87, 13, 77_52, 2_20_18, 23, 21, 17, 45_46, 31_45, 7_87, 13, 33_52, 1_44_31, 13, 55_00, 11, 11_76, 5_80, 13, 1_68_19, 47_97, 23, 17, 10, 1_71_35, 6_58, 19, 4_57, 79_32, 13, 1_84, 19, 31_54, 1_71_35, 64_68, 19, 14_04, 1_22_69, 19, 42_29, 53_56, 1_62_64, 46, 19, 17, 2_05_45, 1_03_95, 9, 9, 9, 11, 28, 64_21, 95_31, 2_07_29, 17, 10, 3_53, 1_70_22, 11, 21, 64_21, 95_31, 1_69_49, 17, 10, 1_15_09, 7_53, 11, 33, 95, 24_21, 73_85, 9_56, 1_44_31, 26_26, 25, 8_42, 73_85, 48_36, 21, 14_29, 22_72, 98_55, 31_20, 1_61, 2_47_38, 19, 1_32_03, 6_58, 2_18, 7_87, 21, 4_30, 1_84_82, 8_47, 26_37, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_22, 2_21_78, 27, 10_64, 22, 9_56, 13, 1_11_01, 14_29, 58_54, 2_43_13, 1_89_53, 40, 4_22, 2_43_66, 68, 17_58, 37, 1_04_83, 1_42_57, 31, 2_07, 2_63, 21, 2_03, 37_73, 25, 71, 97_35, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 20_49, 34_42, 17, 1_38_94, 33_80, 23, 95, 18, 1_76_34, 22_88, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """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, 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, 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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=a       ,							model_name="""xlnet-base-cased"""       ,							revision="""c841166438c31ec7ca9a106dee7bb312b73ae511"""       ,							)



