



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						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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()


_lowerCamelCase						    =  load("""accuracy""")






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	eval_pred
 __SCREAMING_SNAKE_CASE       :    int       =	np.argmax(__lowerCamelCase		,	axis=1     )
 return metric.compute(predictions=__lowerCamelCase		,	references=__lowerCamelCase     )

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							List[Any]     ,  UpperCamelCase :							int				)->None:
      super().__init__()
      __SCREAMING_SNAKE_CASE       :    int       =	trainer






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							List[Any]     ,  **UpperCamelCase :							Optional[int]				)->Optional[int]:
      if control.should_evaluate:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	deepcopy(UpperCamelCase				)
       self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,  metric_key_prefix="train"				)
       return control_copy






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	get_args()
 set_seed(args.seed     )

 __SCREAMING_SNAKE_CASE       :    Any       =	load_dataset("codeparrot/codecomplex"		,	split="train"     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	dataset.train_test_split(test_size=0.2     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	train_test["test"].train_test_split(test_size=0.5     )
 __SCREAMING_SNAKE_CASE       :    int       =	DatasetDict(
     {
         "train": train_test["train"],
         "test": test_validation["train"],
         "valid": test_validation["test"],
     }     )

 print("Loading tokenizer and model"     )
 __SCREAMING_SNAKE_CASE       :    Any       =	AutoTokenizer.from_pretrained(args.model_ckpt     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	tokenizer.eos_token
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt		,	num_labels=7     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	model.config.eos_token_id

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

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

 def tokenize(__lowerCamelCase   :     str     ):
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tokenizer(example["src"]		,	truncation=__lowerCamelCase		,	max_length=1024     )
  __SCREAMING_SNAKE_CASE       :    Any       =	labels.straint(example["complexity"]     )
  return {
      "input_ids": inputs["input_ids"],
      "attention_mask": inputs["attention_mask"],
      "label": label,
  }

 __SCREAMING_SNAKE_CASE       :    str       =	train_test_validation.map(
     __lowerCamelCase		,	batched=__lowerCamelCase		,	remove_columns=train_test_validation["train"].column_names		,	)
 __SCREAMING_SNAKE_CASE       :    Tuple       =	DataCollatorWithPadding(tokenizer=__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Tuple       =	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"		,	)

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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()











from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	analyze_text(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	list(" " + ascii_lowercase     )
 # what is our total sum of probabilities.
 __SCREAMING_SNAKE_CASE       :    int       =	sum(single_char_strings.values()     )

 # one length string
 __SCREAMING_SNAKE_CASE       :    str       =	0
 # for each alpha we go in our dict and if it is in it we calculate entropy
 for ch in my_alphas:
  if ch in single_char_strings:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	single_char_strings[ch]
   __SCREAMING_SNAKE_CASE       :    str       =	my_str / all_sum
   my_fir_sum += prob * math.loga(__lowerCamelCase     )  # entropy formula.

    # print entropy
 print(F"""{round(-1 * my_fir_sum     ):.1f}"""     )

 # two len string
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum(two_char_strings.values()     )
 __SCREAMING_SNAKE_CASE       :    int       =	0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   __SCREAMING_SNAKE_CASE       :    int       =	cha + cha
   if sequence in two_char_strings:
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	two_char_strings[sequence]
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(__lowerCamelCase     ) / all_sum
    my_sec_sum += prob * math.loga(__lowerCamelCase     )

    # print second entropy
 print(F"""{round(-1 * my_sec_sum     ):.1f}"""     )

 # print the difference between them
 print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)     ):.1f}"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	Counter()  # type: ignore
 __SCREAMING_SNAKE_CASE       :    int       =	Counter()  # type: ignore
 single_char_strings[text[-1]] += 1

 # first case when we have space at start.
 two_char_strings[" " + text[0]] += 1
 for i in range(0		,	len(__lowerCamelCase     ) - 1     ):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 import doctest

 doctest.testmod()
 # text = (
 #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
 #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
 #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
 #     "beloved evident through no service elderly is. Blind there if every no so "
 #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
 #     "of message cottage windows do besides against uncivil.  Delightful "
 #     "unreserved impossible few estimating men favourable see entreaties. She "
 #     "propriety immediate was improving. He or entrance humoured likewise "
 #     "moderate. Much nor game son say feel. Fat make met can must form into "
 #     "gate. Me we offending prevailed discovery. "
 # )

 # calculate_prob(text)


if __name__ == "__main__":
      main()






