



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	(       ) ->					Tuple:






						'''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=10							)
						parser.add_argument("""--weight_decay"""     ,type=__lowerCamelCase     ,default=0.01							)
						parser.add_argument("""--output_dir"""     ,type=__lowerCamelCase     ,default="""./results"""							)
						return parser.parse_args()


__A							       =		load('''accuracy''')



def 			__a	(       lowerCAmelCase_  :   Union[str, Any]							) ->					str:






						'''simple docstring'''



						UpperCAmelCase_=  eval_pred
						UpperCAmelCase_=  np.argmax(__lowerCamelCase     ,axis=1							)
						return metric.compute(predictions=__lowerCamelCase     ,references=__lowerCamelCase							)






class 						lowercase		(					snake_case__):






	"""simple docstring"""


	def __init__(      self      :		List[Any]		,    __UpperCAmelCase      :		str	)     ->					None:
							super().__init__()
							UpperCAmelCase_=  trainer




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[Any]		,    __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		Dict		,    **__UpperCAmelCase      :		Union[str, Any]	)     ->					Union[str, Any]:
							if control.should_evaluate:
													UpperCAmelCase_=  deepcopy(__UpperCAmelCase	)
													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_  :   Union[str, Any]							):
												UpperCAmelCase_=  tokenizer(example["""src"""]     ,truncation=__lowerCamelCase     ,max_length=10_24							)
												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()








'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =''
   for ch in key:
      if ch == " " or ch not in key_no_dups and ch.isalpha():
         key_no_dups += ch
   return key_no_dups





def lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =[chr(i + 6_5				) for i in range(2_6				)]
   # Remove duplicate characters from key
   _UpperCAmelCase  :  Any				       =remove_duplicates(key.upper()				)
   _UpperCAmelCase  :  Optional[Any]				       =len(__lowerCamelCase				)
   # First fill cipher with key characters
   _UpperCAmelCase  :  List[Any]				       ={alphabet[i]: char for i, char in enumerate(__lowerCamelCase				)}
   # Then map remaining characters in alphabet to
   # the alphabet from the beginning
   for i in range(len(__lowerCamelCase				)			,       2_6				):
      _UpperCAmelCase  :  Union[str, Any]				       =alphabet[i - offset]
      # Ensure we are not mapping letters to letters previously mapped
      while char in key:
         offset -= 1
         _UpperCAmelCase  :  str				       =alphabet[i - offset]
      _UpperCAmelCase  :  Any				       =char
   return cipher_alphabet





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   return "".join(cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       ={v: k for k, v in cipher_map.items()}
   return "".join(rev_cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =input('Enter message to encode or decode: '				).strip()
   _UpperCAmelCase  :  int				       =input('Enter keyword: '				).strip()
   _UpperCAmelCase  :  int				       =input('Encipher or decipher? E/D:'				).strip()[0].lower()
   try:
      _UpperCAmelCase  :  Tuple				       ={'e': encipher, 'd': decipher}[option]
   except KeyError:
      raise KeyError('invalid input option'				)
   _UpperCAmelCase  :  List[str]				       =create_cipher_map(__lowerCamelCase				)
   print(func(__lowerCamelCase			,       __lowerCamelCase				)				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

