

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
a_     :    Tuple     		=   logging.getLogger()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	List[str]						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      '\n'.join(lowerCAmelCase__						)
 Path(lowerCAmelCase__						).open('w'						).writelines(lowerCAmelCase__						)


a_     :    int     		=   "patrickvonplaten/t5-tiny-random"
a_     :    Optional[int]     		=   "sshleifer/bart-tiny-random"
a_     :    int     		=   "sshleifer/tiny-mbart"

a_     :    Any     		=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class   UpperCamelCase			(  __A ):

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

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
     SCREAMING_SNAKE_CASE		    =      input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     SCREAMING_SNAKE_CASE		    =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(__A				,     __A     )

     SCREAMING_SNAKE_CASE		    =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
     SCREAMING_SNAKE_CASE		    =      'translation_en_to_de' if model == T5_TINY else 'summarization'
     SCREAMING_SNAKE_CASE		    =      F"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

     with patch.object(__A				,     'argv'				,     __A     ):
      run_generate()
      assert Path(__A     ).exists()
      # os.remove(Path(output_file_name))

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     self.run_eval_tester(__A     )

    @parameterized.expand([BART_TINY, MBART_TINY]     )
    @slow
    def UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   Tuple     ):

     """simple docstring"""






     self.run_eval_tester(__A     )




    @parameterized.expand([T5_TINY, MBART_TINY]     )
    @slow
    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
     SCREAMING_SNAKE_CASE		    =      input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     SCREAMING_SNAKE_CASE		    =      {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     SCREAMING_SNAKE_CASE		    =      Path(self.get_auto_remove_tmp_dir()     )
     SCREAMING_SNAKE_CASE		    =      str(tmp_dir / 'scores.json'     )
     SCREAMING_SNAKE_CASE		    =      str(tmp_dir / 'val.target'     )
     _dump_articles(__A				,     text['en']     )
     _dump_articles(__A				,     text['de']     )
     SCREAMING_SNAKE_CASE		    =      'translation_en_to_de' if model == T5_TINY else 'summarization'
     SCREAMING_SNAKE_CASE		    =      F"""
            run_eval_search.py
            {model}
            {str(__A     )}
            {str(__A     )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']     )

     with patch.object(__A				,     'argv'				,     __A     ):
      with CaptureStdout() as cs:
       run_search()
      SCREAMING_SNAKE_CASE		    =      [' num_beams | length_penalty', model, 'Best score args']
      SCREAMING_SNAKE_CASE		    =      ['Info']
      if "translation" in task:
       expected_strings.append('bleu'     )
      else:
       expected_strings.extend(__A     )
      for w in expected_strings:
       assert w in cs.out
      for w in un_expected_strings:
       assert w not in cs.out
      assert Path(__A     ).exists()
      os.remove(Path(__A     )     )





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()



