





'''simple docstring'''

import qiskit







def    a__							(							lowercase   :	int,   lowercase   :	int )						->						qiskit.result.counts.Counts:





     """simple docstring"""

     _UpperCamelCase							       =			qiskit.Aer.get_backend('''aer_simulator''' )

     # Create a Quantum Circuit acting on the q register
     _UpperCamelCase							       =			qiskit.QuantumCircuit(lowercase,   lowercase )

     # Apply X (NOT) Gate to Qubits 0 & 1
     circuit.x(0 )
     circuit.x(1 )

     # Map the quantum measurement to the classical bits
     circuit.measure([0, 1],   [0, 1] )

     # Execute the circuit on the qasm simulator
     _UpperCamelCase							       =			qiskit.execute(lowercase,   lowercase,   shots=1000 )

     # Return the histogram data of the results of the experiment.
     return job.result().get_counts(lowercase )


if __name__ == "__main__":
   lowercase__		:     List[str]			=							single_qubit_measure(2, 2)
   print(F"""Total count for various states are: {counts}""")









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()