

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

import yaml




class   UpperCamelCase			(  yaml.SafeLoader ):

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   int     ):

     """simple docstring"""






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




    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   List[Any]=False     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      super().construct_mapping(__A				,     deep=__A     )
     self._check_no_duplicates_on_constructed_node(__A     )
     return mapping


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				Tuple:
 '''simple docstring'''


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

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




class   UpperCamelCase			(  __A ):
    __UpperCamelCase    ={"train_eval_index"}  # train-eval-index in the YAML metadata

    @classmethod
    def UpperCamelCase      (      cls			:   int				,     snake_case__			:   List[str]     ):

     """simple docstring"""






     with open(__A				,     encoding='utf-8'     ) as readme_file:
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      _split_yaml_from_readme(readme_file.read()     )
     if yaml_string is not None:
      return cls.from_yaml_string(__A     )
     else:
      return cls()

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Any     ):

     """simple docstring"""






     if path.exists():
      with open(__A				,     encoding='utf-8'     ) as readme_file:
       SCREAMING_SNAKE_CASE		    =      readme_file.read()
     else:
      SCREAMING_SNAKE_CASE		    =      None
     SCREAMING_SNAKE_CASE		    =      self._to_readme(__A     )
     with open(__A				,     'w'				,     encoding='utf-8'     ) as readme_file:
      readme_file.write(__A     )

    def UpperCamelCase      (      self			:   Optional[int]				,     snake_case__			:   int = None     ):

     """simple docstring"""






     if readme_content is not None:
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      _split_yaml_from_readme(__A     )
      SCREAMING_SNAKE_CASE		    =      '---\n' + self.to_yaml_string() + '---\n' + content
     else:
      SCREAMING_SNAKE_CASE		    =      '---\n' + self.to_yaml_string() + '---\n'
     return full_content

    @classmethod
    def UpperCamelCase      (      cls			:   Any				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      yaml.load(__A				,     Loader=_NoDuplicateSafeLoader     ) or {}

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




    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """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'     )


a_     :    Dict     		=   {
    "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

      a_     :    List[str]     		=   ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      a_     :    int     		=   ap.parse_args()

      a_     :    List[str]     		=   Path(args.readme_filepath)
      a_     :    List[Any]     		=   DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)





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   ():
   __a					=  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=0.0_1   )
   parser.add_argument("""--output_dir"""		,				type=lowerCAmelCase__		,				default="""./results"""   )
   return parser.parse_args()


SCREAMING_SNAKE_CASE							    =							load('accuracy')




def  a   (lowerCAmelCase__   ):
   __a	,    __a					=  eval_pred
   __a					=  np.argmax(lowerCAmelCase__		,				axis=1   )
   return metric.compute(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  trainer


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       **__A    ):
        if control.should_evaluate:
           __a					=  deepcopy(__A    )
           self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,       metric_key_prefix="""train"""    )
           return control_copy







def  a   ():
   __a					=  get_args()
   set_seed(args.seed   )

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

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

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

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

   def tokenize(lowerCAmelCase__   ):
      __a					=  tokenizer(example["""src"""]		,				truncation=lowerCAmelCase__		,				max_length=1_024   )
      __a					=  labels.straint(example["""complexity"""]   )
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

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

   __a					=  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=0.0_1		,				metric_for_best_model="""accuracy"""		,				run_name="""complexity-java"""		,				report_to="""wandb"""		,				)

   __a					=  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()



