


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     if not isinstance(_lowercase				,_lowercase							):
          snake_case											=							F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(_lowercase							)

     snake_case											=							''''''.join(bin(_lowercase							)[2:].zfill(8							) for byte in data							)

     snake_case											=							len(_lowercase							) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          snake_case											=							B'''=''' * ((6 - len(_lowercase							) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(_lowercase							) % 6)
     else:
          snake_case											=							B''''''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]				,2							)]
             for index in range(0				,len(_lowercase							)				,6							)							).encode()
         + padding
     )
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     if not isinstance(_lowercase				,_lowercase							) and not isinstance(_lowercase				,_lowercase							):
          snake_case											=							(
              '''argument should be a bytes-like object or ASCII string, '''
              F'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(_lowercase							)

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(_lowercase				,_lowercase							):
          try:
               snake_case											=							encoded_data.decode('''utf-8'''							)
          except UnicodeDecodeError:
               raise ValueError('''base64 encoded data should only contain ASCII characters'''							)

     snake_case											=							encoded_data.count('''='''							)

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]							), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data							), "Invalid base64 character(s) found."

     # Check the padding
     assert len(_lowercase							) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          snake_case											=							encoded_data[:-padding]

          snake_case											=							''''''.join(
              bin(B64_CHARSET.index(_lowercase							)							)[2:].zfill(6							) for char in encoded_data							)[: -padding * 2]
     else:
          snake_case											=							''''''.join(
              bin(B64_CHARSET.index(_lowercase							)							)[2:].zfill(6							) for char in encoded_data							)

     snake_case											=							[
         int(binary_stream[index : index + 8]				,2							)
         for index in range(0				,len(_lowercase							)				,8							)
     ]

     return bytes(_lowercase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()










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


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

class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=1    )					->						Dict:
         UpperCAmelCase_     :				List[Any]  =     tokenizer
         UpperCAmelCase_     :				int  =     dataset
         UpperCAmelCase_     :				Dict  =     len(_SCREAMING_SNAKE_CASE    ) if n_tasks is None else n_tasks
         UpperCAmelCase_     :				Optional[int]  =     n_copies





    def __iter__( self    )					->						Any:
         UpperCAmelCase_     :				List[Any]  =     []
         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()    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.tokenizer(_SCREAMING_SNAKE_CASE  ,padding=_SCREAMING_SNAKE_CASE  ,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     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						List[Any]:
         UpperCAmelCase_     :				str  =     start_length
         UpperCAmelCase_     :				Optional[int]  =     eof_strings
         UpperCAmelCase_     :				str  =     tokenizer





    def __call__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						List[Any]:
         UpperCAmelCase_     :				Optional[Any]  =     self.tokenizer.batch_decode(input_ids[:, self.start_length :]    )
         UpperCAmelCase_     :				Optional[int]  =     []
         for decoded_generation in decoded_generations:
              done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings    )    )
         return all(_SCREAMING_SNAKE_CASE    )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase=20					,		**_lowercase ):

     '''simple docstring'''


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

               UpperCAmelCase_,       UpperCAmelCase_     :				List[str]  =     accelerator.gather((generated_tokens, generated_tasks) )
               UpperCAmelCase_     :				Union[str, Any]  =     generated_tokens.cpu().numpy()
               UpperCAmelCase_     :				Union[str, Any]  =     generated_tasks.cpu().numpy()

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

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







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     HfArgumentParser(_lowercase )
     UpperCAmelCase_     :				int  =     parser.parse_args()

     transformers.logging.set_verbosity_error()
     # enables code execution in code_eval metric
     UpperCAmelCase_     :				Optional[Any]  =     args.HF_ALLOW_CODE_EVAL
     # make sure tokenizer plays nice with multiprocessing
     UpperCAmelCase_     :				List[Any]  =     '''false'''

     if args.num_workers is None:
          UpperCAmelCase_     :				Optional[Any]  =     multiprocessing.cpu_count()

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

     # Load model and tokenizer
     UpperCAmelCase_     :				Tuple  =     AutoTokenizer.from_pretrained(args.model_ckpt )
     UpperCAmelCase_     :				Any  =     tokenizer.eos_token
     UpperCAmelCase_     :				List[str]  =     AutoModelForCausalLM.from_pretrained(args.model_ckpt )

     # Generation settings
     UpperCAmelCase_     :				str  =     {
         '''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					,		_lowercase					,		_lowercase )] ),
     }

     # Load evaluation dataset and metric
     UpperCAmelCase_     :				Tuple  =     load_dataset('''openai_humaneval''' )
     UpperCAmelCase_     :				Dict  =     load_metric('''code_eval''' )

     UpperCAmelCase_     :				Optional[int]  =     args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
     UpperCAmelCase_     :				str  =     args.n_samples // args.batch_size

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

     # Run a quick test to see if code evaluation is enabled
     try:
          UpperCAmelCase_     :				Any  =     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

     UpperCAmelCase_,       UpperCAmelCase_     :				int  =     accelerator.prepare(_lowercase					,		_lowercase )

     UpperCAmelCase_     :				int  =     complete_code(
         _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		n_tasks=_lowercase					,		batch_size=args.batch_size					,		**_lowercase					,		)

     if accelerator.is_main_process:
          UpperCAmelCase_     :				Any  =     []

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

          # Evaluate completions with "code_eval" metric
          UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     code_eval_metric.compute(
              references=_lowercase					,		predictions=_lowercase					,		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(_lowercase					,		_lowercase )


# 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()