


'''simple docstring'''







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import YolosImageProcessor


class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]=7				,							SCREAMING_SNAKE_CASE      :							Tuple=3				,							SCREAMING_SNAKE_CASE      :							List[Any]=30				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=400				,							SCREAMING_SNAKE_CASE      :							Dict=True				,							SCREAMING_SNAKE_CASE      :							List[str]=None				,							SCREAMING_SNAKE_CASE      :							List[Any]=True				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=[0.5, 0.5, 0.5]				,							SCREAMING_SNAKE_CASE      :							List[str]=[0.5, 0.5, 0.5]				,							SCREAMING_SNAKE_CASE      :							Dict=True				,							SCREAMING_SNAKE_CASE      :							Tuple=1 / 255				,							SCREAMING_SNAKE_CASE      :							Tuple=True				,							):
           # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
           _A					:     Union[str, Any]     = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
           _A					:     Optional[int]     = parent
           _A					:     List[Any]     = batch_size
           _A					:     Tuple     = num_channels
           _A					:     List[str]     = min_resolution
           _A					:     str     = max_resolution
           _A					:     str     = do_resize
           _A					:     Optional[int]     = size
           _A					:     Any     = do_normalize
           _A					:     Union[str, Any]     = image_mean
           _A					:     List[Any]     = image_std
           _A					:     Any     = do_rescale
           _A					:     Optional[Any]     = rescale_factor
           _A					:     Union[str, Any]     = do_pad


    def        A   (  self      :							Any):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Tuple=False):
           if not batched:
                  _A					:     int     = image_inputs[0]
                  if isinstance(SCREAMING_SNAKE_CASE				,							Image.Image):
                         _A  ,			_A					:     int     = image.size
                  else:
                         _A  ,			_A					:     Dict     = image.shape[1], image.shape[2]
                  if w < h:
                         _A					:     Tuple     = int(self.size['shortest_edge'] * h / w)
                         _A					:     str     = self.size['shortest_edge']
                  elif w > h:
                         _A					:     List[str]     = self.size['shortest_edge']
                         _A					:     List[str]     = int(self.size['shortest_edge'] * w / h)
                  else:
                         _A					:     Any     = self.size['shortest_edge']
                         _A					:     Dict     = self.size['shortest_edge']

           else:
                  _A					:     List[Any]     = []
                  for image in image_inputs:
                         _A  ,			_A					:     Optional[int]     = self.get_expected_values([image])
                         expected_values.append((expected_height, expected_width))
                  _A					:     List[str]     = max(SCREAMING_SNAKE_CASE				,							key=lambda SCREAMING_SNAKE_CASE: item[0])[0]
                  _A					:     List[Any]     = max(SCREAMING_SNAKE_CASE				,							key=lambda SCREAMING_SNAKE_CASE: item[1])[1]

           return expected_height, expected_width




@require_torch
@require_vision
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   YolosImageProcessor if is_vision_available() else None


    def        A   (  self      :							Optional[int]):
           _A					:     Optional[int]     = YolosImageProcessingTester(self)


    @property
    def        A   (  self      :							Optional[Any]):
           return self.image_processor_tester.prepare_image_processor_dict()


    def        A   (  self      :							Any):
           _A					:     Tuple     = self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'image_mean'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'image_std'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'do_normalize'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'do_resize'))
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,							'size'))


    def        A   (  self      :							Optional[Any]):
           _A					:     Dict     = self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size				,							{'shortest_edge': 18, 'longest_edge': 1333})
           self.assertEqual(image_processor.do_pad				,							SCREAMING_SNAKE_CASE)

           _A					:     int     = self.image_processing_class.from_dict(
               self.image_processor_dict				,							size=42				,							max_size=84				,							pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE)
           self.assertEqual(image_processor.size				,							{'shortest_edge': 42, 'longest_edge': 84})
           self.assertEqual(image_processor.do_pad				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           pass


    def        A   (  self      :							int):
           # Initialize image_processing
           _A					:     str     = self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           _A					:     List[Any]     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							Image.Image)

           # Test not batched input
           _A					:     Optional[Any]     = image_processing(image_inputs[0]				,							return_tensors='pt').pixel_values

           _A  ,			_A					:     List[Any]     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

           self.assertEqual(
               encoded_images.shape				,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,							)

           # Test batched
           _A  ,			_A					:     Optional[Any]     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

           _A					:     List[str]     = image_processing(SCREAMING_SNAKE_CASE				,							return_tensors='pt').pixel_values
           self.assertEqual(
               encoded_images.shape				,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,							)


    def        A   (  self      :							List[Any]):
           # Initialize image_processing
           _A					:     List[str]     = self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           _A					:     Any     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							numpify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							np.ndarray)

           # Test not batched input
           _A					:     Tuple     = image_processing(image_inputs[0]				,							return_tensors='pt').pixel_values

           _A  ,			_A					:     Tuple     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

           self.assertEqual(
               encoded_images.shape				,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,							)

           # Test batched
           _A					:     str     = image_processing(SCREAMING_SNAKE_CASE				,							return_tensors='pt').pixel_values

           _A  ,			_A					:     List[str]     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

           self.assertEqual(
               encoded_images.shape				,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,							)


    def        A   (  self      :							int):
           # Initialize image_processing
           _A					:     int     = self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           _A					:     Tuple     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							torchify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							torch.Tensor)

           # Test not batched input
           _A					:     List[str]     = image_processing(image_inputs[0]				,							return_tensors='pt').pixel_values

           _A  ,			_A					:     str     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

           self.assertEqual(
               encoded_images.shape				,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,							)

           # Test batched
           _A					:     Optional[Any]     = image_processing(SCREAMING_SNAKE_CASE				,							return_tensors='pt').pixel_values

           _A  ,			_A					:     List[str]     = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE				,							batched=SCREAMING_SNAKE_CASE)

           self.assertEqual(
               encoded_images.shape				,							(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,							)


    def        A   (  self      :							Optional[int]):
           # Initialize image_processings
           _A					:     Any     = self.image_processing_class(**self.image_processor_dict)
           _A					:     Any     = self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE				,							do_normalize=SCREAMING_SNAKE_CASE				,							do_rescale=SCREAMING_SNAKE_CASE)
           # create random PyTorch tensors
           _A					:     Optional[int]     = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=SCREAMING_SNAKE_CASE				,							torchify=SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(SCREAMING_SNAKE_CASE				,							torch.Tensor)

           # Test whether the method "pad" and calling the image processor return the same tensors
           _A					:     List[Any]     = image_processing_a.pad(SCREAMING_SNAKE_CASE				,							return_tensors='pt')
           _A					:     Any     = image_processing_a(SCREAMING_SNAKE_CASE				,							return_tensors='pt')

           self.assertTrue(
               torch.allclose(encoded_images_with_method['pixel_values']				,							encoded_images['pixel_values']				,							atol=1e-4))


    @slow
    def        A   (  self      :							int):
           # prepare image and target
           _A					:     Union[str, Any]     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
           with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,							'r') as f:
                  _A					:     List[str]     = json.loads(f.read())

           _A					:     Optional[int]     = {'image_id': 39769, 'annotations': target}

           # encode them
           _A					:     Any     = YolosImageProcessor.from_pretrained('hustvl/yolos-small')
           _A					:     List[Any]     = image_processing(images=SCREAMING_SNAKE_CASE				,							annotations=SCREAMING_SNAKE_CASE				,							return_tensors='pt')

           # verify pixel values
           _A					:     Optional[int]     = torch.Size([1, 3, 800, 1066])
           self.assertEqual(encoding['pixel_values'].shape				,							SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,							SCREAMING_SNAKE_CASE				,							atol=1e-4))

           # verify area
           _A					:     str     = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
           self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,							SCREAMING_SNAKE_CASE))
           # verify boxes
           _A					:     List[str]     = torch.Size([6, 4])
           self.assertEqual(encoding['labels'][0]['boxes'].shape				,							SCREAMING_SNAKE_CASE)
           _A					:     Optional[int]     = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))
           # verify image_id
           _A					:     List[Any]     = torch.tensor([39769])
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,							SCREAMING_SNAKE_CASE))
           # verify is_crowd
           _A					:     str     = torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,							SCREAMING_SNAKE_CASE))
           # verify class_labels
           _A					:     Any     = torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,							SCREAMING_SNAKE_CASE))
           # verify orig_size
           _A					:     str     = torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,							SCREAMING_SNAKE_CASE))
           # verify size
           _A					:     List[str]     = torch.tensor([800, 1066])
           self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,							SCREAMING_SNAKE_CASE))


    @slow
    def        A   (  self      :							str):
           # prepare image, target and masks_path
           _A					:     List[str]     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
           with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'				,							'r') as f:
                  _A					:     List[str]     = json.loads(f.read())

           _A					:     int     = {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

           _A					:     List[str]     = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

           # encode them
           _A					:     str     = YolosImageProcessor(format='coco_panoptic')
           _A					:     List[Any]     = image_processing(images=SCREAMING_SNAKE_CASE				,							annotations=SCREAMING_SNAKE_CASE				,							masks_path=SCREAMING_SNAKE_CASE				,							return_tensors='pt')

           # verify pixel values
           _A					:     Dict     = torch.Size([1, 3, 800, 1066])
           self.assertEqual(encoding['pixel_values'].shape				,							SCREAMING_SNAKE_CASE)

           _A					:     Dict     = torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,							SCREAMING_SNAKE_CASE				,							atol=1e-4))

           # verify area
           _A					:     List[str]     = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
           self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,							SCREAMING_SNAKE_CASE))
           # verify boxes
           _A					:     Optional[Any]     = torch.Size([6, 4])
           self.assertEqual(encoding['labels'][0]['boxes'].shape				,							SCREAMING_SNAKE_CASE)
           _A					:     str     = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,							SCREAMING_SNAKE_CASE				,							atol=1e-3))
           # verify image_id
           _A					:     Optional[Any]     = torch.tensor([39769])
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,							SCREAMING_SNAKE_CASE))
           # verify is_crowd
           _A					:     Optional[int]     = torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,							SCREAMING_SNAKE_CASE))
           # verify class_labels
           _A					:     int     = torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,							SCREAMING_SNAKE_CASE))
           # verify masks
           _A					:     Optional[Any]     = 822873
           self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,							SCREAMING_SNAKE_CASE)
           # verify orig_size
           _A					:     Dict     = torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,							SCREAMING_SNAKE_CASE))
           # verify size
           _A					:     Optional[Any]     = torch.tensor([800, 1066])
           self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,							SCREAMING_SNAKE_CASE))










'''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 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = multiprocessing.Manager()
       _A					:     List[Any]     = manager.list()

       _A					:     Dict     = multiprocessing.Process(target=lowerCamelCase  ,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 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[Any]       ):
       with create_tempdir():
              # These system calls are needed when cleaning up tempdir.
              import os
              import shutil

              _A					:     Any     = shutil.rmtree
              _A					:     Optional[int]     = os.rmdir
              _A					:     str     = os.chdir

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

              # Run program.
              try:
                     _A					:     str     = {}
                     with swallow_io():
                            with time_limit(lowerCamelCase       ):
                                   exec(lowerCamelCase  ,lowerCamelCase       )
                     result.append('passed'       )
              except TimeoutException:
                     result.append('timed out'       )
              except BaseException as e:
                     result.append(F'failed: {e}'       )

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






@contextlib.contextmanager
def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       def signal_handler(lowerCamelCase					:      str  ,lowerCamelCase					:      Any       ):
              raise TimeoutException('Timed out!'       )

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






@contextlib.contextmanager
def 				lowerCAmelCase__	(    ):
       _A					:     Any     = WriteOnlyStringIO()
       with contextlib.redirect_stdout(lowerCamelCase       ):
              with contextlib.redirect_stderr(lowerCamelCase       ):
                     with redirect_stdin(lowerCamelCase       ):
                            yield






@contextlib.contextmanager
def 				lowerCAmelCase__	(    ):
       with tempfile.TemporaryDirectory() as dirname:
              with chdir(lowerCamelCase       ):
                     yield dirname


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    pass




class 			__lowerCamelCase			(	io.StringIO	):
    """simple docstring"""


    def        A   (  self      :							Tuple				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							Dict):
           raise OSError


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           raise OSError


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							str				,							**SCREAMING_SNAKE_CASE      :							Optional[int]):
           raise OSError


    def        A   (  self      :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           return False




class 			__lowerCamelCase			(	contextlib._RedirectStream	):  # type: ignore
    """simple docstring"""
    a													=   "stdin"






@contextlib.contextmanager
def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple       ):
       if root == ".":
              yield
              return
       _A					:     Any     = os.getcwd()
       os.chdir(lowerCamelCase       )
       try:
              yield
       except BaseException as exc:
              raise exc
       finally:
              os.chdir(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]=None       ):

       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					:     List[Any]     = None
       _A					:     Dict     = None

       import os

       _A					:     Union[str, Any]     = '1'

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

       import shutil

       _A					:     int     = None
       _A					:     Any     = None
       _A					:     List[Any]     = None

       import subprocess

       _A					:     Optional[Any]     = None  # type: ignore

       _A					:     List[Any]     = None

       import sys

       _A					:     Any     = None
       _A					:     Tuple     = None
       _A					:     str     = None
       _A					:     Tuple     = None
       _A					:     List[str]     = None






