


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 assert isinstance(__snake_case     ,						__snake_case						)
 assert dataset.num_rows == 4
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_1", "col_2", "col_3"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''     ,						[False, True]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  _lowercase							      =ParquetDatasetReader(__snake_case     ,						cache_dir=__snake_case     ,						keep_in_memory=__snake_case						).read()
 _check_parquet_dataset(__snake_case     ,						__snake_case						)
@pytest.mark.parametrize(
    '''features'''     ,						[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Any:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =features.copy() if features else default_expected_features
 _lowercase							      =(
     Features({feature: Value(__snake_case						) for feature, dtype in features.items()}						) if features is not None else None
 )
 _lowercase							      =ParquetDatasetReader(__snake_case     ,						features=__snake_case     ,						cache_dir=__snake_case						).read()
 _check_parquet_dataset(__snake_case     ,						__snake_case						)
@pytest.mark.parametrize('''split'''     ,						[None, NamedSplit('''train'''						), '''train''', '''test''']						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =ParquetDatasetReader(__snake_case     ,						cache_dir=__snake_case     ,						split=__snake_case						).read()
 _check_parquet_dataset(__snake_case     ,						__snake_case						)
 assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type'''     ,						[str, list]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 if issubclass(__snake_case     ,						__snake_case						):
  _lowercase							      =parquet_path
 elif issubclass(__snake_case     ,						__snake_case						):
  _lowercase							      =[parquet_path]
 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =ParquetDatasetReader(__snake_case     ,						cache_dir=__snake_case						).read()
 _check_parquet_dataset(__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=("train",)						)     ->			Tuple:







 """simple docstring"""


 assert isinstance(__snake_case     ,						__snake_case						)
 for split in splits:
  _lowercase							      =dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''     ,						[False, True]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  _lowercase							      =ParquetDatasetReader(
      {'''train''': parquet_path}     ,						cache_dir=__snake_case     ,						keep_in_memory=__snake_case						).read()
 _check_parquet_datasetdict(__snake_case     ,						__snake_case						)
@pytest.mark.parametrize(
    '''features'''     ,						[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =features.copy() if features else default_expected_features
 _lowercase							      =(
     Features({feature: Value(__snake_case						) for feature, dtype in features.items()}						) if features is not None else None
 )
 _lowercase							      =ParquetDatasetReader({'''train''': parquet_path}     ,						features=__snake_case     ,						cache_dir=__snake_case						).read()
 _check_parquet_datasetdict(__snake_case     ,						__snake_case						)
@pytest.mark.parametrize('''split'''     ,						[None, NamedSplit('''train'''						), '''train''', '''test''']						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 if split:
  _lowercase							      ={split: parquet_path}
 else:
  _lowercase							      ='''train'''
  _lowercase							      ={'''train''': parquet_path, '''test''': parquet_path}
 _lowercase							      =tmp_path / '''cache'''
 _lowercase							      ={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 _lowercase							      =ParquetDatasetReader(__snake_case     ,						cache_dir=__snake_case						).read()
 _check_parquet_datasetdict(__snake_case     ,						__snake_case     ,						splits=list(path.keys()						)						)
 assert all(dataset[split].split == split for split in path.keys()						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =ParquetDatasetWriter(__snake_case     ,						tmp_path / '''foo.parquet'''						)
 assert writer.write() > 0
 _lowercase							      =pq.ParquetFile(tmp_path / '''foo.parquet'''						)
 _lowercase							      =pf.read()
 assert dataset.data.table == output_table
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =str(shared_datadir / '''test_image_rgb.jpg'''						)
 _lowercase							      ={'''image''': [image_path]}
 _lowercase							      =Features({'''image''': Image()}						)
 _lowercase							      =Dataset.from_dict(__snake_case     ,						features=__snake_case						)
 _lowercase							      =ParquetDatasetWriter(__snake_case     ,						tmp_path / '''foo.parquet'''						)
 assert writer.write() > 0

 _lowercase							      =Dataset.from_parquet(str(tmp_path / '''foo.parquet'''						)						)
 assert dataset.features == reloaded_dataset.features

 _lowercase							      =ParquetDatasetReader(str(tmp_path / '''foo.parquet'''						)     ,						streaming=__snake_case						).read()
 assert dataset.features == reloaded_iterable_dataset.features



@pytest.mark.parametrize(
    '''feature, expected'''     ,						[
        (Features({'''foo''': Value('''int32'''						)}						), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Optional[int]:







 """simple docstring"""


 assert get_writer_batch_size(__snake_case						) == expected





import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


UpperCAmelCase__   	=							['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=1       )     ->   Dict:
      _lowercase							      =tokenizer
      _lowercase							      =dataset
      _lowercase							      =len(UpperCAmelCase       ) if n_tasks is None else n_tasks
      _lowercase							      =n_copies





     def __iter__(self       )     ->   Optional[Any]:
      _lowercase							      =[]
      for task in range(self.n_tasks       ):
       # without strip, the model generate commented codes ...
       prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip()       )
      _lowercase							      =self.tokenizer(UpperCAmelCase		,     padding=UpperCAmelCase		,     return_tensors='''pt'''       )
      for task in range(self.n_tasks       ):
       for _ in range(self.n_copies       ):
        yield {
            "ids": outputs.input_ids[task],
            "task_id": task,
            "input_len": outputs.attention_mask[task].sum(),
        }







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =start_length
      _lowercase							      =eof_strings
      _lowercase							      =tokenizer





     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Dict:
      _lowercase							      =self.tokenizer.batch_decode(input_ids[:, self.start_length :]       )
      _lowercase							      =[]
      for decoded_generation in decoded_generations:
       done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings       )       )
      return all(UpperCAmelCase       )



def 				UpperCAmelCase_					(							__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =re.split('''(%s)''' % '''|'''.join(__snake_case						)     ,						__snake_case						)
 # last string should be ""
 return "".join(string_list[:-2]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=20     ,						**__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =defaultdict(__snake_case						)  # dict of list of generated tokens
 for step, batch in tqdm(enumerate(__snake_case						)						):
  with torch.no_grad():
   _lowercase							      =batch['''ids'''].shape[-1]
   _lowercase							      =accelerator.unwrap_model(__snake_case						).generate(
       input_ids=batch['''ids'''][:, : batch['''input_len''']]     ,						num_return_sequences=__snake_case     ,						**__snake_case						)
   # each task is generated batch_size times
   _lowercase							      =batch['''task_id'''].repeat(__snake_case						)
   _lowercase							      =accelerator.pad_across_processes(
       __snake_case     ,						dim=1     ,						pad_index=tokenizer.pad_token_id						)

   _lowercase      ,    _lowercase							      =accelerator.gather((generated_tokens, generated_tasks)						)
   _lowercase							      =generated_tokens.cpu().numpy()
   _lowercase							      =generated_tasks.cpu().numpy()

   for task, generated_tokens in zip(__snake_case     ,						__snake_case						):
    gen_token_dict[task].append(__snake_case						)

 _lowercase							      =[[] for _ in range(__snake_case						)]
 for task, generated_tokens in gen_token_dict.items():
  for s in generated_tokens:
   _lowercase							      =tokenizer.decode(__snake_case     ,						skip_special_tokens=__snake_case     ,						clean_up_tokenization_spaces=__snake_case						)
   code_gens[task].append(remove_last_block(__snake_case						)						)
 return code_gens
def 				UpperCAmelCase_					(							)     ->			str:







 """simple docstring"""


 _lowercase							      =HfArgumentParser(__snake_case						)
 _lowercase							      =parser.parse_args()

 transformers.logging.set_verbosity_error()
 # enables code execution in code_eval metric
 _lowercase							      =args.HF_ALLOW_CODE_EVAL
 # make sure tokenizer plays nice with multiprocessing
 _lowercase							      ='''false'''

 if args.num_workers is None:
  _lowercase							      =multiprocessing.cpu_count()

 # Use dataset load to feed to accelerate
 _lowercase							      =Accelerator()
 set_seed(args.seed     ,						device_specific=__snake_case						)

 # Load model and tokenizer
 _lowercase							      =AutoTokenizer.from_pretrained(args.model_ckpt						)
 _lowercase							      =tokenizer.eos_token
 _lowercase							      =AutoModelForCausalLM.from_pretrained(args.model_ckpt						)

 # Generation settings
 _lowercase							      ={
     '''do_sample''': args.do_sample,
     '''temperature''': args.temperature,
     '''max_new_tokens''': args.max_new_tokens,
     '''top_p''': args.top_p,
     '''top_k''': args.top_k,
     '''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0     ,						__snake_case     ,						__snake_case						)]						),
 }

 # Load evaluation dataset and metric
 _lowercase							      =load_dataset('''openai_humaneval'''						)
 _lowercase							      =load_metric('''code_eval'''						)

 _lowercase							      =args.num_tasks if args.num_tasks is not None else len(human_eval['''test''']						)
 _lowercase							      =args.n_samples // args.batch_size

 _lowercase							      =TokenizedDataset(__snake_case     ,						human_eval['''test''']     ,						n_copies=__snake_case     ,						n_tasks=__snake_case						)
 # do not confuse args.batch_size, which is actually the num_return_sequences
 _lowercase							      =DataLoader(__snake_case     ,						batch_size=1						)

 # Run a quick test to see if code evaluation is enabled
 try:
  _lowercase							      =code_eval_metric.compute(references=['''''']     ,						predictions=[['''''']]						)
 except ValueError as exception:
  print(
      '''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
      ''' flag to enable code evaluation.'''						)
  raise exception

 _lowercase      ,    _lowercase							      =accelerator.prepare(__snake_case     ,						__snake_case						)

 _lowercase							      =complete_code(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						n_tasks=__snake_case     ,						batch_size=args.batch_size     ,						**__snake_case     ,						)

 if accelerator.is_main_process:
  _lowercase							      =[]

  for task in tqdm(range(__snake_case						)						):
   _lowercase							      =human_eval['''test'''][task]['''test''']
   _lowercase							      =F"check({human_eval['test'][task]['entry_point']})"
   references.append('''\n''' + test_func + '''\n''' + entry_point						)

  # Evaluate completions with "code_eval" metric
  _lowercase      ,    _lowercase							      =code_eval_metric.compute(
      references=__snake_case     ,						predictions=__snake_case     ,						num_workers=args.num_workers						)
  print(F"Results: {pass_at_k}"						)

  # Save results to json file
  with open(args.output_file     ,						'''w'''						) as fp:
   json.dump(__snake_case     ,						__snake_case						)


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
  main()

