




'''simple docstring'''






# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   int	      =    multiprocessing.Manager()
      snake_case__       :   Any	      =    manager.list()

      snake_case__       :   List[str]	      =    multiprocessing.Process(target=__SCREAMING_SNAKE_CASE				,      args=(check_program, result, timeout)		)
      p.start()
      p.join(timeout=timeout + 1		)
      if p.is_alive():
            p.kill()

      if not result:
            result.append('timed out'		)

      return {
          "task_id": task_id,
          "passed": result[0] == "passed",
          "result": result[0],
          "completion_id": completion_id,
      }






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Tuple:
      with create_tempdir():
            # These system calls are needed when cleaning up tempdir.
            import os
            import shutil

            snake_case__       :   List[Any]	      =    shutil.rmtree
            snake_case__       :   Tuple	      =    os.rmdir
            snake_case__       :   Any	      =    os.chdir

            # Disable functionalities that can make destructive changes to the test.
            reliability_guard()

            # Run program.
            try:
                  snake_case__       :   Union[str, Any]	      =    {}
                  with swallow_io():
                        with time_limit(__SCREAMING_SNAKE_CASE		):
                              exec(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
                  result.append('passed'		)
            except TimeoutException:
                  result.append('timed out'		)
            except BaseException as e:
                  result.append(f"failed: {e}"		)

            # Needed for cleaning up.
            snake_case__       :   List[Any]	      =    rmtree
            snake_case__       :   Optional[int]	      =    rmdir
            snake_case__       :   Dict	      =    chdir






@contextlib.contextmanager
def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[str]:
      def signal_handler(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		):
            raise TimeoutException('Timed out!'		)

      signal.setitimer(signal.ITIMER_REAL				,      __SCREAMING_SNAKE_CASE		)
      signal.signal(signal.SIGALRM				,      __SCREAMING_SNAKE_CASE		)
      try:
            yield
      finally:
            signal.setitimer(signal.ITIMER_REAL				,      0		)






@contextlib.contextmanager
def 		UpperCamelCase__      (		)    ->   List[str]:
      snake_case__       :   Dict	      =    WriteOnlyStringIO()
      with contextlib.redirect_stdout(__SCREAMING_SNAKE_CASE		):
            with contextlib.redirect_stderr(__SCREAMING_SNAKE_CASE		):
                  with redirect_stdin(__SCREAMING_SNAKE_CASE		):
                        yield






@contextlib.contextmanager
def 		UpperCamelCase__      (		)    ->   Optional[int]:
      with tempfile.TemporaryDirectory() as dirname:
            with chdir(__SCREAMING_SNAKE_CASE		):
                  yield dirname






class        lowercase_      (						lowerCAmelCase_							):
     pass






class        lowercase_      (						io.StringIO							):





     def 			_lowerCAmelCase      (     self     :					Optional[Any]     ,							*__lowerCamelCase     :					int     ,							**__lowerCamelCase     :					Optional[int]			):
           raise OSError





     def 			_lowerCAmelCase      (     self     :					int     ,							*__lowerCamelCase     :					List[Any]     ,							**__lowerCamelCase     :					Tuple			):
           raise OSError





     def 			_lowerCAmelCase      (     self     :					Any     ,							*__lowerCamelCase     :					int     ,							**__lowerCamelCase     :					Union[str, Any]			):
           raise OSError





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							*__lowerCamelCase     :					List[str]     ,							**__lowerCamelCase     :					Optional[Any]			):
           return False






class        lowercase_      (						contextlib._RedirectStream							):  # type: ignore
     A_											=			"stdin"






@contextlib.contextmanager
def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[str]:
      if root == ".":
            yield
            return
      snake_case__       :   str	      =    os.getcwd()
      os.chdir(__SCREAMING_SNAKE_CASE		)
      try:
            yield
      except BaseException as exc:
            raise exc
      finally:
            os.chdir(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE=None		)    ->   Any:

      if maximum_memory_bytes is not None:
            import resource

            resource.setrlimit(resource.RLIMIT_AS				,      (maximum_memory_bytes, maximum_memory_bytes)		)
            resource.setrlimit(resource.RLIMIT_DATA				,      (maximum_memory_bytes, maximum_memory_bytes)		)
            if not platform.uname().system == "Darwin":
                  resource.setrlimit(resource.RLIMIT_STACK				,      (maximum_memory_bytes, maximum_memory_bytes)		)

      faulthandler.disable()

      import builtins

      snake_case__       :   List[Any]	      =    None
      snake_case__       :   List[str]	      =    None

      import os

      snake_case__       :   List[Any]	      =    '1'

      snake_case__       :   Union[str, Any]	      =    None
      snake_case__       :   Tuple	      =    None
      snake_case__       :   Dict	      =    None
      snake_case__       :   Tuple	      =    None
      snake_case__       :   List[str]	      =    None
      snake_case__       :   Any	      =    None
      snake_case__       :   Optional[Any]	      =    None
      snake_case__       :   Union[str, Any]	      =    None
      snake_case__       :   int	      =    None
      snake_case__       :   Optional[int]	      =    None
      snake_case__       :   Union[str, Any]	      =    None
      snake_case__       :   List[Any]	      =    None
      snake_case__       :   Optional[int]	      =    None
      snake_case__       :   Union[str, Any]	      =    None
      snake_case__       :   Tuple	      =    None
      snake_case__       :   int	      =    None
      snake_case__       :   Optional[Any]	      =    None
      snake_case__       :   Optional[int]	      =    None
      snake_case__       :   List[str]	      =    None
      snake_case__       :   List[str]	      =    None
      snake_case__       :   List[str]	      =    None
      snake_case__       :   Dict	      =    None
      snake_case__       :   Optional[Any]	      =    None
      snake_case__       :   List[Any]	      =    None
      snake_case__       :   Tuple	      =    None
      snake_case__       :   Optional[int]	      =    None
      snake_case__       :   int	      =    None

      import shutil

      snake_case__       :   List[str]	      =    None
      snake_case__       :   List[Any]	      =    None
      snake_case__       :   List[Any]	      =    None

      import subprocess

      snake_case__       :   int	      =    None  # type: ignore

      snake_case__       :   Dict	      =    None

      import sys

      snake_case__       :   Tuple	      =    None
      snake_case__       :   Tuple	      =    None
      snake_case__       :   List[str]	      =    None
      snake_case__       :   Optional[int]	      =    None
      snake_case__       :   List[Any]	      =    None






'''simple docstring'''






import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


A_			      =		logging.getLogger(__name__)






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					List[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					int=None			):
           super().__init__(
               __lowerCamelCase     ,							question_encoder_tokenizer=__lowerCamelCase     ,							generator_tokenizer=__lowerCamelCase     ,							index=__lowerCamelCase     ,							init_retrieval=__lowerCamelCase     ,							)
           snake_case__       :   Optional[int]	      =    None





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					int			):
           logger.info('initializing retrieval'			)

           # initializing a separate process group for retrieval as the default
           # nccl backend doesn't support gather/scatter operations while gloo
           # is too slow to replace nccl for the core gpu communication
           if dist.is_initialized():
                 logger.info('dist initialized'			)
                 # needs to be set manually
                 snake_case__       :   Any	      =    self._infer_socket_ifname()
                 # avoid clash with the NCCL port
                 snake_case__       :   int	      =    str(distributed_port + 1			)
                 snake_case__       :   Any	      =    dist.new_group(ranks=__lowerCamelCase     ,							backend='gloo'			)

           # initialize retriever only on the main worker
           if not dist.is_initialized() or self._is_main():
                 logger.info('dist not initialized / main'			)
                 self.index.init_index()

           # all processes wait untill the retriever is initialized by the main process
           if dist.is_initialized():
                 torch.distributed.barrier(group=self.process_group			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           return dist.get_rank(group=self.process_group			) == 0





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Union[str, Any]=torch.floataa			):
           snake_case__       :   Optional[int]	      =    torch.empty(__lowerCamelCase     ,							dtype=__lowerCamelCase			)
           dist.scatter(__lowerCamelCase     ,							src=0     ,							scatter_list=__lowerCamelCase     ,							group=self.process_group			)
           return target_tensor





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Union[str, Any]	      =    psutil.net_if_addrs()
           # a hacky way to deal with varying network interface names
           snake_case__       :   Any	      =    next((addr for addr in addrs if addr.startswith('e'			))     ,							__lowerCamelCase			)
           return ifname





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					np.ndarray     ,							__lowerCamelCase     :					int			):

           # single GPU training
           if not dist.is_initialized():
                 snake_case__      ,							snake_case__       :   Optional[int]	      =    self._main_retrieve(__lowerCamelCase     ,							__lowerCamelCase			)
                 return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase			)

           # distributed training
           snake_case__       :   Optional[Any]	      =    dist.get_world_size(group=self.process_group			)

           # gather logic
           snake_case__       :   Optional[int]	      =    None
           if self._is_main():
                 snake_case__       :   Optional[int]	      =    [torch.empty(question_hidden_states.shape     ,							dtype=torch.floataa			) for _ in range(__lowerCamelCase			)]
           dist.gather(torch.tensor(__lowerCamelCase			)     ,							dst=0     ,							gather_list=__lowerCamelCase     ,							group=self.process_group			)

           # scatter logic
           snake_case__       :   Union[str, Any]	      =    question_hidden_states.shape[0]
           snake_case__       :   Tuple	      =    []
           snake_case__       :   List[Any]	      =    []
           if self._is_main():
                 assert len(__lowerCamelCase			) == world_size
                 snake_case__      ,							snake_case__       :   Optional[int]	      =    self._main_retrieve(torch.cat(__lowerCamelCase			).numpy()     ,							__lowerCamelCase			)
                 snake_case__      ,							snake_case__       :   Optional[Any]	      =    torch.tensor(__lowerCamelCase			), torch.tensor(__lowerCamelCase			)
                 snake_case__       :   str	      =    self._chunk_tensor(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Dict	      =    self._chunk_tensor(__lowerCamelCase     ,							__lowerCamelCase			)
           snake_case__       :   Tuple	      =    self._scattered(__lowerCamelCase     ,							[n_queries, n_docs]     ,							target_type=torch.intaa			)
           snake_case__       :   Dict	      =    self._scattered(__lowerCamelCase     ,							[n_queries, n_docs, question_hidden_states.shape[1]]			)

           return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCamelCase			)
