
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 		lowercase_    (     )							->							List[str]:
    """simple docstring"""

    snake_case				  =      argparse.ArgumentParser()
    parser.add_argument("--model_ckpt"				,	type=_lowercase				,	default="microsoft/unixcoder-base-nine"	)
    parser.add_argument("--num_epochs"				,	type=_lowercase				,	default=5	)
    parser.add_argument("--batch_size"				,	type=_lowercase				,	default=6	)
    parser.add_argument("--gradient_accumulation_steps"				,	type=_lowercase				,	default=1	)
    parser.add_argument("--freeze"				,	type=_lowercase				,	default=_lowercase	)
    parser.add_argument("--learning_rate"				,	type=_lowercase				,	default=5e-4	)
    parser.add_argument("--seed"				,	type=_lowercase				,	default=0	)
    parser.add_argument("--lr_scheduler_type"				,	type=_lowercase				,	default="cosine"	)
    parser.add_argument("--num_warmup_steps"				,	type=_lowercase				,	default=10	)
    parser.add_argument("--weight_decay"				,	type=_lowercase				,	default=0.01	)
    parser.add_argument("--output_dir"				,	type=_lowercase				,	default="./results"	)
    return parser.parse_args()


_A						      =				load("accuracy")



def 		lowercase_    (     A__	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      eval_pred
    snake_case				  =      np.argmax(_lowercase				,	axis=1	)
    return metric.compute(predictions=_lowercase				,	references=_lowercase	)







class   lowerCamelCase	(						A_						):





     def __init__(self   :					str       ,					_A   :					int     )		->		None:
         super().__init__()
         snake_case				  =      trainer





     def 	UpperCAmelCase(self   :					Dict       ,					_A   :					List[Any]       ,					_A   :					Optional[Any]       ,					_A   :					int       ,					**_A   :					Any     )		->		Dict:





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



def 		lowercase_    (     )							->							Optional[Any]:
    """simple docstring"""

    snake_case				  =      get_args()
    set_seed(args.seed	)

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

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

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

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

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

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

    snake_case				  =      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"				,	)

    snake_case				  =      Trainer(
        model=_lowercase				,	args=_lowercase				,	train_dataset=tokenized_datasets["train"]				,	eval_dataset=tokenized_datasets["valid"]				,	tokenizer=_lowercase				,	data_collator=_lowercase				,	compute_metrics=_lowercase				,	)

    print("Training..."	)
    trainer.add_callback(CustomCallback(_lowercase	)	)
    trainer.train()


if __name__ == "__main__":
 main()



import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin







@require_jieba
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Any     =	CpmAntTokenizer
     UpperCAmelCase__       :							Optional[Any]     =	False





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Dict:
         super().setUp()

         snake_case				  =      [
             "<d>",
             "</d>",
             "<s>",
             "</s>",
             "</_>",
             "<unk>",
             "<pad>",
             "</n>",
             "我",
             "是",
             "C",
             "P",
             "M",
             "A",
             "n",
             "t",
         ]
         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["vocab_file"]     )
         with open(self.vocab_file       ,					"w"       ,					encoding="utf-8"     ) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]     )     )





     @tooslow
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Optional[int]:
         snake_case				  =      CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"     )
         snake_case				  =      "今天天气真好！"
         snake_case				  =      ["今天", "天气", "真", "好", "！"]
         snake_case				  =      tokenizer.tokenize(_A     )
         self.assertListEqual(_A       ,					_A     )
         snake_case				  =      "今天天气真好！"
         snake_case				  =      [tokenizer.bos_token] + tokens

         snake_case				  =      [6, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A     )       ,					_A     )

         snake_case				  =      tokenizer.decode(_A     )
         self.assertEqual(_A       ,					_A     )

