'''simple docstring'''

import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
  from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
  from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
 from PIL import Image







def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]						,		snake_case_       :       List[Any]="shi-labs/oneformer_demo"      )					->     Optional[int]:






 '''simple docstring'''

 with open(hf_hub_download(snake_case_						,		snake_case_						,		repo_type="""dataset"""      )						,		"""r"""      ) as f:
  __lowerCAmelCase											=						json.load(snake_case_      )
 __lowerCAmelCase											=						{}
 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						[]
 for key, info in class_info.items():
  __lowerCAmelCase											=						info["""name"""]
  class_names.append(info["""name"""]      )
  if info["isthing"]:
   thing_ids.append(int(snake_case_      )      )
 __lowerCAmelCase											=						thing_ids
 __lowerCAmelCase											=						class_names
 return metadata







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def __init__(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	int=7      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=3      ,       SCREAMING_SNAKE_CASE__   :	Dict=30      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=4_00      ,       SCREAMING_SNAKE_CASE__   :	Any=None      ,       SCREAMING_SNAKE_CASE__   :	str=True      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=True      ,       SCREAMING_SNAKE_CASE__   :	List[str]=[0.5, 0.5, 0.5]      ,       SCREAMING_SNAKE_CASE__   :	Tuple=[0.5, 0.5, 0.5]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=10      ,       SCREAMING_SNAKE_CASE__   :	Dict=False      ,       SCREAMING_SNAKE_CASE__   :	int=2_55      ,       SCREAMING_SNAKE_CASE__   :	List[str]="shi-labs/oneformer_demo"      ,       SCREAMING_SNAKE_CASE__   :	Tuple="ade20k_panoptic.json"      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=10      ,       )  ->    Optional[Any]:
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						num_channels
  __lowerCAmelCase											=						min_resolution
  __lowerCAmelCase											=						max_resolution
  __lowerCAmelCase											=						do_resize
  __lowerCAmelCase											=						{"""shortest_edge""": 32, """longest_edge""": 13_33} if size is None else size
  __lowerCAmelCase											=						do_normalize
  __lowerCAmelCase											=						image_mean
  __lowerCAmelCase											=						image_std
  __lowerCAmelCase											=						class_info_file
  __lowerCAmelCase											=						prepare_metadata(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						num_text
  __lowerCAmelCase											=						repo_path

  # for the post_process_functions
  __lowerCAmelCase											=						2
  __lowerCAmelCase											=						10
  __lowerCAmelCase											=						10
  __lowerCAmelCase											=						3
  __lowerCAmelCase											=						4
  __lowerCAmelCase											=						num_labels
  __lowerCAmelCase											=						do_reduce_labels
  __lowerCAmelCase											=						ignore_index
 def a					(      self   :	Optional[int]							)  ->    List[str]:
  return {
      "do_resize": self.do_resize,
      "size": self.size,
      "do_normalize": self.do_normalize,
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "num_labels": self.num_labels,
      "do_reduce_labels": self.do_reduce_labels,
      "ignore_index": self.ignore_index,
      "class_info_file": self.class_info_file,
      "metadata": self.metadata,
      "num_text": self.num_text,
  }
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=False							)  ->    Optional[int]:
  if not batched:
   __lowerCAmelCase											=						image_inputs[0]
   if isinstance(SCREAMING_SNAKE_CASE__      ,       Image.Image							):
    __lowerCAmelCase							,       __lowerCAmelCase											=						image.size
   else:
    __lowerCAmelCase							,       __lowerCAmelCase											=						image.shape[1], image.shape[2]
   if w < h:
    __lowerCAmelCase											=						int(self.size["""shortest_edge"""] * h / w							)
    __lowerCAmelCase											=						self.size["""shortest_edge"""]
   elif w > h:
    __lowerCAmelCase											=						self.size["""shortest_edge"""]
    __lowerCAmelCase											=						int(self.size["""shortest_edge"""] * w / h							)
   else:
    __lowerCAmelCase											=						self.size["""shortest_edge"""]
    __lowerCAmelCase											=						self.size["""shortest_edge"""]

  else:
   __lowerCAmelCase											=						[]
   for image in image_inputs:
    __lowerCAmelCase							,       __lowerCAmelCase											=						self.get_expected_values([image]							)
    expected_values.append((expected_height, expected_width)							)
   __lowerCAmelCase											=						max(SCREAMING_SNAKE_CASE__      ,       key=lambda SCREAMING_SNAKE_CASE__							: item[0]							)[0]
   __lowerCAmelCase											=						max(SCREAMING_SNAKE_CASE__      ,       key=lambda SCREAMING_SNAKE_CASE__							: item[1]							)[1]

  return expected_height, expected_width





 def a					(      self   :	int							)  ->    Optional[int]:
  return OneFormerForUniversalSegmentationOutput(
      # +1 for null class
      class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)							)      ,       masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)							)      ,       )







@require_torch
@require_vision
class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
 # only for test_image_processing_common.test_image_proc_to_json_string
 _SCREAMING_SNAKE_CASE     :       Dict      	=					image_processing_class
 def a					(      self   :	Optional[int]							)  ->    List[Any]:
  __lowerCAmelCase											=						OneFormerImageProcessorTester(self							)
 @property
 def a					(      self   :	Union[str, Any]							)  ->    List[str]:
  return self.image_processing_tester.prepare_image_processor_dict()
 def a					(      self   :	Optional[int]							)  ->    Tuple:
  __lowerCAmelCase											=						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"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """ignore_index"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """class_info_file"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """num_text"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """repo_path"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """metadata"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_reduce_labels"""							)							)
 def a					(      self   :	List[Any]							)  ->    Dict:
  pass
 def a					(      self   :	Any							)  ->    str:
  # Initialize image_processor
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random PIL images
  __lowerCAmelCase											=						prepare_image_inputs(self.image_processing_tester      ,       equal_resolution=SCREAMING_SNAKE_CASE__							)
  for image in image_inputs:
   self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       Image.Image							)

  # Test not batched input
  __lowerCAmelCase											=						image_processor(image_inputs[0]      ,       ["""semantic"""]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

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

  # Test batched
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,       batched=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						image_processor(
      SCREAMING_SNAKE_CASE__      ,       ["""semantic"""] * len(SCREAMING_SNAKE_CASE__							)      ,       return_tensors="""pt"""							).pixel_values
  self.assertEqual(
      encoded_images.shape      ,       (
          self.image_processing_tester.batch_size,
          self.image_processing_tester.num_channels,
          expected_height,
          expected_width,
      )      ,       )
 def a					(      self   :	Optional[int]							)  ->    int:
  # Initialize image_processor
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random numpy tensors
  __lowerCAmelCase											=						prepare_image_inputs(self.image_processing_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
  __lowerCAmelCase											=						image_processor(image_inputs[0]      ,       ["""semantic"""]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

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

  # Test batched
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,       batched=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						image_processor(
      SCREAMING_SNAKE_CASE__      ,       ["""semantic"""] * len(SCREAMING_SNAKE_CASE__							)      ,       return_tensors="""pt"""							).pixel_values
  self.assertEqual(
      encoded_images.shape      ,       (
          self.image_processing_tester.batch_size,
          self.image_processing_tester.num_channels,
          expected_height,
          expected_width,
      )      ,       )
 def a					(      self   :	List[str]							)  ->    Tuple:
  # Initialize image_processor
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random PyTorch tensors
  __lowerCAmelCase											=						prepare_image_inputs(self.image_processing_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
  __lowerCAmelCase											=						image_processor(image_inputs[0]      ,       ["""semantic"""]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

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

  # Test batched
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,       batched=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						image_processor(
      SCREAMING_SNAKE_CASE__      ,       ["""semantic"""] * len(SCREAMING_SNAKE_CASE__							)      ,       return_tensors="""pt"""							).pixel_values
  self.assertEqual(
      encoded_images.shape      ,       (
          self.image_processing_tester.batch_size,
          self.image_processing_tester.num_channels,
          expected_height,
          expected_width,
      )      ,       )
 def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	List[str]=False      ,       SCREAMING_SNAKE_CASE__   :	List[str]=False      ,       SCREAMING_SNAKE_CASE__   :	Dict="np"							)  ->    int:
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # prepare image and target
  __lowerCAmelCase											=						self.image_processing_tester.num_labels
  __lowerCAmelCase											=						None
  __lowerCAmelCase											=						None
  __lowerCAmelCase											=						prepare_image_inputs(self.image_processing_tester      ,       equal_resolution=SCREAMING_SNAKE_CASE__							)
  if with_segmentation_maps:
   __lowerCAmelCase											=						num_labels
   if is_instance_map:
    __lowerCAmelCase											=						list(range(SCREAMING_SNAKE_CASE__							)							) * 2
    __lowerCAmelCase											=						dict(enumerate(SCREAMING_SNAKE_CASE__							)							)
   __lowerCAmelCase											=						[
       np.random.randint(0      ,       high * 2      ,       (img.size[1], img.size[0])							).astype(np.uinta							) for img in image_inputs
   ]
   if segmentation_type == "pil":
    __lowerCAmelCase											=						[Image.fromarray(SCREAMING_SNAKE_CASE__							) for annotation in annotations]

  __lowerCAmelCase											=						image_processor(
      SCREAMING_SNAKE_CASE__      ,       ["""semantic"""] * len(SCREAMING_SNAKE_CASE__							)      ,       SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""      ,       instance_id_to_semantic_id=SCREAMING_SNAKE_CASE__      ,       pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__      ,       )

  return inputs
 def a					(      self   :	str							)  ->    Optional[Any]:
  pass
 def a					(      self   :	Optional[int]							)  ->    Optional[int]:
  def common(SCREAMING_SNAKE_CASE__   :	Optional[int]=False      ,       SCREAMING_SNAKE_CASE__   :	List[str]=None							):
   __lowerCAmelCase											=						self.comm_get_image_processor_inputs(
       with_segmentation_maps=SCREAMING_SNAKE_CASE__      ,       is_instance_map=SCREAMING_SNAKE_CASE__      ,       segmentation_type=SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						inputs["""mask_labels"""]
   __lowerCAmelCase											=						inputs["""class_labels"""]
   __lowerCAmelCase											=						inputs["""pixel_values"""]
   __lowerCAmelCase											=						inputs["""text_inputs"""]

   # check the batch_size
   for mask_label, class_label, text_input in zip(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    self.assertEqual(mask_label.shape[0]      ,       class_label.shape[0]							)
    # this ensure padding has happened
    self.assertEqual(mask_label.shape[1:]      ,       pixel_values.shape[2:]							)
    self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.image_processing_tester.num_text							)

  common()
  common(is_instance_map=SCREAMING_SNAKE_CASE__							)
  common(is_instance_map=SCREAMING_SNAKE_CASE__      ,       segmentation_type="""pil"""							)
  common(is_instance_map=SCREAMING_SNAKE_CASE__      ,       segmentation_type="""pil"""							)
 def a					(      self   :	str							)  ->    int:
  __lowerCAmelCase											=						np.zeros((20, 50)							)
  __lowerCAmelCase											=						1
  __lowerCAmelCase											=						1
  __lowerCAmelCase											=						1

  __lowerCAmelCase											=						binary_mask_to_rle(SCREAMING_SNAKE_CASE__							)
  self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       4							)
  self.assertEqual(rle[0]      ,       21							)
  self.assertEqual(rle[1]      ,       45							)
 def a					(      self   :	Any							)  ->    Tuple:
  __lowerCAmelCase											=						self.image_processing_class(
      num_labels=self.image_processing_tester.num_classes      ,       max_seq_length=77      ,       task_seq_length=77      ,       class_info_file="""ade20k_panoptic.json"""      ,       num_text=self.image_processing_tester.num_text      ,       repo_path="""shi-labs/oneformer_demo"""      ,       )
  __lowerCAmelCase											=						self.image_processing_tester.get_fake_oneformer_outputs()

  __lowerCAmelCase											=						fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE__							)

  self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       self.image_processing_tester.batch_size							)
  self.assertEqual(
      segmentation[0].shape      ,       (
          self.image_processing_tester.height,
          self.image_processing_tester.width,
      )      ,       )

  __lowerCAmelCase											=						[(1, 4) for i in range(self.image_processing_tester.batch_size							)]
  __lowerCAmelCase											=						fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE__      ,       target_sizes=SCREAMING_SNAKE_CASE__							)

  self.assertEqual(segmentation[0].shape      ,       target_sizes[0]							)
 def a					(      self   :	int							)  ->    Any:
  __lowerCAmelCase											=						self.image_processing_class(
      num_labels=self.image_processing_tester.num_classes      ,       max_seq_length=77      ,       task_seq_length=77      ,       class_info_file="""ade20k_panoptic.json"""      ,       num_text=self.image_processing_tester.num_text      ,       repo_path="""shi-labs/oneformer_demo"""      ,       )
  __lowerCAmelCase											=						self.image_processing_tester.get_fake_oneformer_outputs()
  __lowerCAmelCase											=						image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE__      ,       threshold=0							)

  self.assertTrue(len(SCREAMING_SNAKE_CASE__							) == self.image_processing_tester.batch_size							)
  for el in segmentation:
   self.assertTrue("""segmentation""" in el							)
   self.assertTrue("""segments_info""" in el							)
   self.assertEqual(type(el["""segments_info"""]							)      ,       SCREAMING_SNAKE_CASE__							)
   self.assertEqual(
       el["""segmentation"""].shape      ,       (self.image_processing_tester.height, self.image_processing_tester.width)							)





 def a					(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.image_processing_class(
      num_labels=self.image_processing_tester.num_classes      ,       max_seq_length=77      ,       task_seq_length=77      ,       class_info_file="""ade20k_panoptic.json"""      ,       num_text=self.image_processing_tester.num_text      ,       repo_path="""shi-labs/oneformer_demo"""      ,       )
  __lowerCAmelCase											=						self.image_processing_tester.get_fake_oneformer_outputs()
  __lowerCAmelCase											=						image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE__      ,       threshold=0							)

  self.assertTrue(len(SCREAMING_SNAKE_CASE__							) == self.image_processing_tester.batch_size							)
  for el in segmentation:
   self.assertTrue("""segmentation""" in el							)
   self.assertTrue("""segments_info""" in el							)
   self.assertEqual(type(el["""segments_info"""]							)      ,       SCREAMING_SNAKE_CASE__							)
   self.assertEqual(
       el["""segmentation"""].shape      ,       (self.image_processing_tester.height, self.image_processing_tester.width)							)







'''simple docstring'''

def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]=2_81_23      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						[1] * (limit + 1)

 for i in range(2						,		int(limit**0.5      ) + 1      ):
  sum_divs[i * i] += i
  for k in range(i + 1						,		limit // i + 1      ):
   sum_divs[k * i] += k + i

 __lowerCAmelCase											=						set()
 __lowerCAmelCase											=						0

 for n in range(1						,		limit + 1      ):
  if sum_divs[n] > n:
   abundants.add(snake_case_      )

  if not any((n - a in abundants) for a in abundants      ):
   res += n

 return res


if __name__ == "__main__":
 print(solution())






