
"""simple docstring"""



import argparse
import hashlib  # hashlib is only used inside the Test class
import struct





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					data
          UpperCAmelCase__	:      Dict     							=					[0x6745_2301, 0xEFCD_AB89, 0x98BA_DCFE, 0x1032_5476, 0xC3D2_E1F0]



      @staticmethod
      def  __snake_case							(   UpperCamelCase_     ,	UpperCamelCase_      ):
          return ((n << b) | (n >> (32 - b))) & 0xFFFF_FFFF



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					b'\x80' + b'\x00' * (63 - (len(self.data      ) + 8) % 64)
          UpperCAmelCase__	:      List[Any]     							=					self.data + padding + struct.pack('>Q'     ,	8 * len(self.data      )      )
          return padded_data



      def  __snake_case							(   self      ):
          return [
              self.padded_data[i : i + 64] for i in range(0     ,	len(self.padded_data      )     ,	64      )
          ]



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Tuple     							=					list(struct.unpack('>16L'     ,	UpperCamelCase_      )      ) + [0] * 64
          for i in range(16     ,	80      ):
              UpperCAmelCase__	:      List[Any]     							=					self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])     ,	1      )
          return w



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.padding()
          UpperCAmelCase__	:      Dict     							=					self.split_blocks()
          for block in self.blocks:
              UpperCAmelCase__	:      Any     							=					self.expand_block(UpperCamelCase_      )
              UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					self.h
              for i in range(0     ,	80      ):
                  if 0 <= i < 20:
                      UpperCAmelCase__	:      Optional[Any]     							=					(b & c) | ((~b) & d)
                      UpperCAmelCase__	:      Optional[Any]     							=					0x5A82_7999
                  elif 20 <= i < 40:
                      UpperCAmelCase__	:      Union[str, Any]     							=					b ^ c ^ d
                      UpperCAmelCase__	:      Union[str, Any]     							=					0x6ED9_EBA1
                  elif 40 <= i < 60:
                      UpperCAmelCase__	:      Tuple     							=					(b & c) | (b & d) | (c & d)
                      UpperCAmelCase__	:      List[Any]     							=					0x8F1B_BCDC
                  elif 60 <= i < 80:
                      UpperCAmelCase__	:      Dict     							=					b ^ c ^ d
                      UpperCAmelCase__	:      List[str]     							=					0xCA62_C1D6
                  UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					(
                      self.rotate(UpperCamelCase_     ,	5      ) + f + e + k + expanded_block[i] & 0xFFFF_FFFF,
                      a,
                      self.rotate(UpperCamelCase_     ,	30      ),
                      c,
                      d,
                  )
              UpperCAmelCase__	:      str     							=					(
                  self.h[0] + a & 0xFFFF_FFFF,
                  self.h[1] + b & 0xFFFF_FFFF,
                  self.h[2] + c & 0xFFFF_FFFF,
                  self.h[3] + d & 0xFFFF_FFFF,
                  self.h[4] + e & 0xFFFF_FFFF,
              )
          return ("{:08x}" * 5).format(*self.h      )




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      Tuple     							=					b'Test String'
    assert SHAaHash(_snake_case ).final_hash() == hashlib.shaa(_snake_case ).hexdigest()  # noqa: S324
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      List[Any]     							=					argparse.ArgumentParser(description='Process some strings or files' )
    parser.add_argument(
        '--string'    ,dest='input_string'    ,default='Hello World!! Welcome to Cryptography'    ,help='Hash the string'    ,)
    parser.add_argument('--file'    ,dest='input_file'    ,help='Hash contents of a file' )
    UpperCAmelCase__	:      Any     							=					parser.parse_args()
    UpperCAmelCase__	:      Optional[Any]     							=					args.input_string
    # In any case hash input should be a bytestring
    if args.input_file:
        with open(args.input_file    ,'rb' ) as f:
            UpperCAmelCase__	:      Union[str, Any]     							=					f.read()
    else:
        UpperCAmelCase__	:      Dict     							=					bytes(_snake_case    ,'utf-8' )
    print(SHAaHash(_snake_case ).final_hash() )


if __name__ == "__main__":
    main()
    import doctest

    doctest.testmod()



"""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()

