


"""simple docstring"""

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class    lowerCamelCase__						(		yaml.SafeLoader    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	[self.constructed_objects[key_node] for key_node, _ in node.value]
       __lowercase									=	[tuple(A_							) if isinstance(A_      ,       A_							) else key for key in keys]
       __lowercase									=	Counter(A_							)
       __lowercase									=	[key for key in counter if counter[key] > 1]
       if duplicate_keys:
        raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Tuple      ,       A_	:	Tuple=False							):






       '''simple docstring'''


       __lowercase									=	super().construct_mapping(A_      ,       deep=A_							)
       self._check_no_duplicates_on_constructed_node(A_							)
       return mapping






def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	list(readme_content.splitlines() )
 if full_content and full_content[0] == "---" and "---" in full_content[1:]:
  __lowercase									=	full_content[1:].index("""---""" ) + 1
  __lowercase									=	"""\n""".join(full_content[1:sep_idx] )
  return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

 return None, "\n".join(UpperCamelCase__ )





class    lowerCamelCase__						(		_a    ):
      # class attributes
      a :     Optional[int]											=		{"""train_eval_index"""}  # train-eval-index in the YAML metadata
      @classmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	int      ,       A_	:	Path							):






       '''simple docstring'''


       with open(A_      ,       encoding="""utf-8"""							) as readme_file:
        __lowercase							,		__lowercase									=	_split_yaml_from_readme(readme_file.read()							)
       if yaml_string is not None:
        return cls.from_yaml_string(A_							)
       else:
        return cls()
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Path							):






       '''simple docstring'''


       if path.exists():
        with open(A_      ,       encoding="""utf-8"""							) as readme_file:
         __lowercase									=	readme_file.read()
       else:
        __lowercase									=	None
       __lowercase									=	self._to_readme(A_							)
       with open(A_      ,       """w"""      ,       encoding="""utf-8"""							) as readme_file:
        readme_file.write(A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	Optional[str] = None							):






       '''simple docstring'''


       if readme_content is not None:
        __lowercase							,		__lowercase									=	_split_yaml_from_readme(A_							)
        __lowercase									=	"""---\n""" + self.to_yaml_string() + """---\n""" + content
       else:
        __lowercase									=	"""---\n""" + self.to_yaml_string() + """---\n"""
       return full_content
      @classmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Dict      ,       A_	:	str							):






       '''simple docstring'''


       __lowercase									=	yaml.load(A_      ,       Loader=_NoDuplicateSafeLoader							) or {}

       # Convert the YAML keys to DatasetMetadata fields
       __lowercase									=	{
           (key.replace("""-"""      ,       """_"""							) if key.replace("""-"""      ,       """_"""							) in cls._FIELDS_WITH_DASHES else key): value
           for key, value in metadata_dict.items()
       }
       return cls(**A_							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       return yaml.safe_dump(
           {
               (key.replace("""_"""      ,       """-"""							) if key in self._FIELDS_WITH_DASHES else key): value
               for key, value in self.items()
           }      ,       sort_keys=A_      ,       allow_unicode=A_      ,       encoding="""utf-8"""      ,       ).decode("""utf-8"""							)






UpperCAmelCase__  							={
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
       from argparse import ArgumentParser

       UpperCAmelCase__  							=ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
       ap.add_argument("readme_filepath")
       UpperCAmelCase__  							=ap.parse_args()

       UpperCAmelCase__  							=Path(args.readme_filepath)
       UpperCAmelCase__  							=DatasetMetadata.from_readme(readme_filepath)
       print(dataset_metadata)
       dataset_metadata.to_readme(readme_filepath)




"""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_      (				):






 """simple docstring"""







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


UpperCAmelCase__  							=load("accuracy")


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase							,		__lowercase									=	eval_pred
 __lowercase									=	np.argmax(UpperCamelCase__				,   axis=1 )
 return metric.compute(predictions=UpperCamelCase__				,   references=UpperCamelCase__ )





class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Any      ,       A_	:	int							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	trainer






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	int      ,       A_	:	str      ,       A_	:	Any      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


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






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	get_args()
 set_seed(args.seed )

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

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

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

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

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

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

 __lowercase									=	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"""				,   )

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

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


if __name__ == "__main__":
       main()
