





'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[Any] ={
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Optional[int] =[
        '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TimesformerModel''',
        '''TimesformerForVideoClassification''',
        '''TimesformerPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    a__							:							Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




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()

