






'''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__:	str,					SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Dict    )  ->			List[str]:

   """simple docstring"""
   __a				     =    multiprocessing.Manager()
   __a				     =    manager.list()

   __a				     =    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__:	int,					SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Dict:

   """simple docstring"""
   with create_tempdir():
      # These system calls are needed when cleaning up tempdir.
      import os
      import shutil

      __a				     =    shutil.rmtree
      __a				     =    os.rmdir
      __a				     =    os.chdir

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

      # Run program.
      try:
         __a				     =    {}
         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.
      __a				     =    rmtree
      __a				     =    rmdir
      __a				     =    chdir



@contextlib.contextmanager
def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Any    )  ->			str:

   """simple docstring"""
   def signal_handler(SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Optional[int]    ):
      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]:

   """simple docstring"""
   __a				     =    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	(				)  ->			List[str]:

   """simple docstring"""
   with tempfile.TemporaryDirectory() as dirname:
      with chdir(SCREAMING_SNAKE_CASE__    ):
         yield dirname
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      pass







class 							__SCREAMING_SNAKE_CASE       (							io.StringIO     ):




      def  __UpperCamelCase							(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         raise OSError




      def  __UpperCamelCase							(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         raise OSError




      def  __UpperCamelCase							(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Any:



         '''simple docstring'''
         raise OSError






      def  __UpperCamelCase							(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         return False







class 							__SCREAMING_SNAKE_CASE       (							contextlib._RedirectStream     ):  # type: ignore
      __a  					="stdin"







@contextlib.contextmanager
def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Union[str, Any]:

   """simple docstring"""
   if root == ".":
      yield
      return
   __a				     =    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__:	Union[str, Any]=None    )  ->			int:

   """simple docstring"""

   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

   __a				     =    None
   __a				     =    None

   import os

   __a				     =    '1'

   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None

   import shutil

   __a				     =    None
   __a				     =    None
   __a				     =    None

   import subprocess

   __a				     =    None  # type: ignore

   __a				     =    None

   import sys

   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None
   __a				     =    None







'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			List[List[ImageInput]]:

   """simple docstring"""
   if isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and isinstance(videos[0],					(list, tuple)    ) and is_valid_image(videos[0][0]    ):
      return videos

   elif isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and is_valid_image(videos[0]    ):
      return [videos]

   elif is_valid_image(SCREAMING_SNAKE_CASE__    ):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}"""    )



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=["pixel_values"]




      def __init__(  self ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = True ,   lowerCamelCase = 1 / 255 ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->None:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )
         __a				     =    size if size is not None else {'shortest_edge': 256}
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_center_crop
         __a				     =    crop_size
         __a				     =    resample
         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    offset
         __a				     =    do_normalize
         __a				     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __a				     =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         if "shortest_edge" in size:
            __a				     =    get_resize_output_image_size(lowerCamelCase ,   size['shortest_edge'] ,   default_to_square=lowerCamelCase      )
         elif "height" in size and "width" in size:
            __a				     =    (size['height'], size['width'])
         else:
            raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
         return resize(lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase      )
         if "height" not in size or "width" not in size:
            raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
         return center_crop(lowerCamelCase ,   size=(size['height'], size['width']) ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = True ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    image.astype(np.floataa      )
         if offset:
            __a				     =    image - (scale / 2)
         return rescale(lowerCamelCase ,   scale=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         return normalize(lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   )							->np.ndarray:



         '''simple docstring'''
         if do_resize and size is None or resample is None:
            raise ValueError('Size and resample must be specified if do_resize is True.'      )

         if do_center_crop and crop_size is None:
            raise ValueError('Crop size must be specified if do_center_crop is True.'      )

         if do_rescale and rescale_factor is None:
            raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

         if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

         if offset and not do_rescale:
            raise ValueError('For offset, do_rescale must also be set to True.'      )

         # All transformations expect numpy arrays.
         __a				     =    to_numpy_array(lowerCamelCase      )

         if do_resize:
            __a				     =    self.resize(image=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase      )

         if do_center_crop:
            __a				     =    self.center_crop(lowerCamelCase ,   size=lowerCamelCase      )

         if do_rescale:
            __a				     =    self.rescale(image=lowerCamelCase ,   scale=lowerCamelCase ,   offset=lowerCamelCase      )

         if do_normalize:
            __a				     =    self.normalize(image=lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase      )

         __a				     =    to_channel_dimension_format(lowerCamelCase ,   lowerCamelCase      )
         return image






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   **lowerCamelCase ,   )							->PIL.Image.Image:



         '''simple docstring'''
         __a				     =    do_resize if do_resize is not None else self.do_resize
         __a				     =    resample if resample is not None else self.resample
         __a				     =    do_center_crop if do_center_crop is not None else self.do_center_crop
         __a				     =    do_rescale if do_rescale is not None else self.do_rescale
         __a				     =    rescale_factor if rescale_factor is not None else self.rescale_factor
         __a				     =    offset if offset is not None else self.offset
         __a				     =    do_normalize if do_normalize is not None else self.do_normalize
         __a				     =    image_mean if image_mean is not None else self.image_mean
         __a				     =    image_std if image_std is not None else self.image_std

         __a				     =    size if size is not None else self.size
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else self.crop_size
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         if not valid_images(lowerCamelCase      ):
            raise ValueError(
                'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                'torch.Tensor, tf.Tensor or jax.ndarray.'      )

         __a				     =    make_batched(lowerCamelCase      )

         __a				     =    [
             [
                 self._preprocess_image(
                     image=lowerCamelCase ,   do_resize=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   do_center_crop=lowerCamelCase ,   crop_size=lowerCamelCase ,   do_rescale=lowerCamelCase ,   rescale_factor=lowerCamelCase ,   offset=lowerCamelCase ,   do_normalize=lowerCamelCase ,   image_mean=lowerCamelCase ,   image_std=lowerCamelCase ,   data_format=lowerCamelCase ,   )
                 for img in video
             ]
             for video in videos
         ]

         __a				     =    {'pixel_values': videos}
         return BatchFeature(data=lowerCamelCase ,   tensor_type=lowerCamelCase      )