




"""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__							:					str		,   snake_case__							:					str="shi-labs/oneformer_demo"    ):
  """simple docstring"""

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






class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 def __init__(				self:		List[str],      a_:		Dict,      a_:		Any=7,      a_:		int=3,      a_:		List[Any]=30,      a_:		Tuple=400,      a_:		int=None,      a_:		List[Any]=True,      a_:		Dict=True,      a_:		Any=[0.5, 0.5, 0.5],      a_:		str=[0.5, 0.5, 0.5],      a_:		Dict=10,      a_:		List[str]=False,      a_:		Optional[Any]=255,      a_:		Optional[Any]="shi-labs/oneformer_demo",      a_:		Optional[Any]="ade20k_panoptic.json",      a_:		int=10,      ):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  parent
   _snake_case							:		Tuple								=  batch_size
   _snake_case							:		str								=  num_channels
   _snake_case							:		int								=  min_resolution
   _snake_case							:		Tuple								=  max_resolution
   _snake_case							:		int								=  do_resize
   _snake_case							:		Optional[Any]								=  {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
   _snake_case							:		Union[str, Any]								=  do_normalize
   _snake_case							:		Union[str, Any]								=  image_mean
   _snake_case							:		Dict								=  image_std
   _snake_case							:		List[str]								=  class_info_file
   _snake_case							:		Dict								=  prepare_metadata(a_,      a_		)
   _snake_case							:		List[Any]								=  num_text
   _snake_case							:		Tuple								=  repo_path

   # for the post_process_functions
   _snake_case							:		Tuple								=  2
   _snake_case							:		Any								=  10
   _snake_case							:		Optional[int]								=  10
   _snake_case							:		Any								=  3
   _snake_case							:		str								=  4
   _snake_case							:		List[str]								=  num_labels
   _snake_case							:		Any								=  do_reduce_labels
   _snake_case							:		Union[str, Any]								=  ignore_index


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   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  UpperCamelCase_   (				self:		int,      a_:		str,      a_:		Optional[int]=False		):




   '''simple docstring'''



   if not batched:
     _snake_case							:		Tuple								=  image_inputs[0]
     if isinstance(a_,      Image.Image		):
       _snake_case		,					_snake_case							:		List[str]								=  image.size
     else:
       _snake_case		,					_snake_case							:		Optional[int]								=  image.shape[1], image.shape[2]
     if w < h:
       _snake_case							:		Union[str, Any]								=  int(self.size["""shortest_edge"""] * h / w		)
       _snake_case							:		Optional[Any]								=  self.size["""shortest_edge"""]
     elif w > h:
       _snake_case							:		List[str]								=  self.size["""shortest_edge"""]
       _snake_case							:		List[Any]								=  int(self.size["""shortest_edge"""] * w / h		)
     else:
       _snake_case							:		List[str]								=  self.size["""shortest_edge"""]
       _snake_case							:		List[Any]								=  self.size["""shortest_edge"""]

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

   return expected_height, expected_width


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   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(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
 # only for test_image_processing_common.test_image_proc_to_json_string
 lowercase__ 					=					image_processing_class


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  OneFormerImageProcessorTester(self		)


 @property
 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   return self.image_processing_tester.prepare_image_processor_dict()


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.image_processing_class(**self.image_processor_dict		)
   self.assertTrue(hasattr(a_,      """image_mean"""		)		)
   self.assertTrue(hasattr(a_,      """image_std"""		)		)
   self.assertTrue(hasattr(a_,      """do_normalize"""		)		)
   self.assertTrue(hasattr(a_,      """do_resize"""		)		)
   self.assertTrue(hasattr(a_,      """size"""		)		)
   self.assertTrue(hasattr(a_,      """ignore_index"""		)		)
   self.assertTrue(hasattr(a_,      """class_info_file"""		)		)
   self.assertTrue(hasattr(a_,      """num_text"""		)		)
   self.assertTrue(hasattr(a_,      """repo_path"""		)		)
   self.assertTrue(hasattr(a_,      """metadata"""		)		)
   self.assertTrue(hasattr(a_,      """do_reduce_labels"""		)		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   pass


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		str								=  self.image_processing_class(**self.image_processor_dict		)
   # create random PIL images
   _snake_case							:		str								=  prepare_image_inputs(self.image_processing_tester,      equal_resolution=a_		)
   for image in image_inputs:
     self.assertIsInstance(a_,      Image.Image		)

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

   _snake_case		,					_snake_case							:		Dict								=  self.image_processing_tester.get_expected_values(a_		)

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

   # Test batched
   _snake_case		,					_snake_case							:		Any								=  self.image_processing_tester.get_expected_values(a_,      batched=a_		)

   _snake_case							:		Tuple								=  image_processor(
       a_,      ["""semantic"""] * len(a_		),      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  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.image_processing_class(**self.image_processor_dict		)
   # create random numpy tensors
   _snake_case							:		List[str]								=  prepare_image_inputs(self.image_processing_tester,      equal_resolution=a_,      numpify=a_		)
   for image in image_inputs:
     self.assertIsInstance(a_,      np.ndarray		)

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

   _snake_case		,					_snake_case							:		Any								=  self.image_processing_tester.get_expected_values(a_		)

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

   # Test batched
   _snake_case		,					_snake_case							:		Tuple								=  self.image_processing_tester.get_expected_values(a_,      batched=a_		)

   _snake_case							:		Optional[Any]								=  image_processor(
       a_,      ["""semantic"""] * len(a_		),      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  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  self.image_processing_class(**self.image_processor_dict		)
   # create random PyTorch tensors
   _snake_case							:		Any								=  prepare_image_inputs(self.image_processing_tester,      equal_resolution=a_,      torchify=a_		)
   for image in image_inputs:
     self.assertIsInstance(a_,      torch.Tensor		)

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

   _snake_case		,					_snake_case							:		Union[str, Any]								=  self.image_processing_tester.get_expected_values(a_		)

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

   # Test batched
   _snake_case		,					_snake_case							:		Union[str, Any]								=  self.image_processing_tester.get_expected_values(a_,      batched=a_		)

   _snake_case							:		str								=  image_processor(
       a_,      ["""semantic"""] * len(a_		),      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  UpperCamelCase_   (				self:		Dict,      a_:		List[Any]=False,      a_:		int=False,      a_:		Union[str, Any]="np"		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.image_processing_class(**self.image_processor_dict		)
   # prepare image and target
   _snake_case							:		Dict								=  self.image_processing_tester.num_labels
   _snake_case							:		int								=  None
   _snake_case							:		Union[str, Any]								=  None
   _snake_case							:		Any								=  prepare_image_inputs(self.image_processing_tester,      equal_resolution=a_		)
   if with_segmentation_maps:
     _snake_case							:		str								=  num_labels
     if is_instance_map:
       _snake_case							:		Union[str, Any]								=  list(range(a_		)		) * 2
       _snake_case							:		List[Any]								=  dict(enumerate(a_		)		)
     _snake_case							:		Any								=  [
         np.random.randint(0,      high * 2,      (img.size[1], img.size[0])		).astype(np.uinta		) for img in image_inputs
     ]
     if segmentation_type == "pil":
       _snake_case							:		List[str]								=  [Image.fromarray(a_		) for annotation in annotations]

   _snake_case							:		Dict								=  image_processor(
       a_,      ["""semantic"""] * len(a_		),      a_,      return_tensors="""pt""",      instance_id_to_semantic_id=a_,      pad_and_return_pixel_mask=a_,      )

   return inputs


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   pass


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   def common(a_:		List[Any]=False,      a_:		Any=None		):
     _snake_case							:		str								=  self.comm_get_image_processor_inputs(
         with_segmentation_maps=a_,      is_instance_map=a_,      segmentation_type=a_		)

     _snake_case							:		str								=  inputs["""mask_labels"""]
     _snake_case							:		str								=  inputs["""class_labels"""]
     _snake_case							:		Optional[int]								=  inputs["""pixel_values"""]
     _snake_case							:		int								=  inputs["""text_inputs"""]

     # check the batch_size
     for mask_label, class_label, text_input in zip(a_,      a_,      a_		):
       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(a_		),      self.image_processing_tester.num_text		)

   common()
   common(is_instance_map=a_		)
   common(is_instance_map=a_,      segmentation_type="""pil"""		)
   common(is_instance_map=a_,      segmentation_type="""pil"""		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  np.zeros((20, 50)		)
   _snake_case							:		Any								=  1
   _snake_case							:		Dict								=  1
   _snake_case							:		Tuple								=  1

   _snake_case							:		List[Any]								=  binary_mask_to_rle(a_		)
   self.assertEqual(len(a_		),      4		)
   self.assertEqual(rle[0],      21		)
   self.assertEqual(rle[1],      45		)


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  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""",      )
   _snake_case							:		Dict								=  self.image_processing_tester.get_fake_oneformer_outputs()

   _snake_case							:		Union[str, Any]								=  fature_extractor.post_process_semantic_segmentation(a_		)

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

   _snake_case							:		List[Any]								=  [(1, 4) for i in range(self.image_processing_tester.batch_size		)]
   _snake_case							:		Dict								=  fature_extractor.post_process_semantic_segmentation(a_,      target_sizes=a_		)

   self.assertEqual(segmentation[0].shape,      target_sizes[0]		)


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  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""",      )
   _snake_case							:		Union[str, Any]								=  self.image_processing_tester.get_fake_oneformer_outputs()
   _snake_case							:		str								=  image_processor.post_process_instance_segmentation(a_,      threshold=0		)

   self.assertTrue(len(a_		) == 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"""]		),      a_		)
     self.assertEqual(
         el["""segmentation"""].shape,      (self.image_processing_tester.height, self.image_processing_tester.width)		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		str								=  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""",      )
   _snake_case							:		List[Any]								=  self.image_processing_tester.get_fake_oneformer_outputs()
   _snake_case							:		Optional[Any]								=  image_processor.post_process_panoptic_segmentation(a_,      threshold=0		)

   self.assertTrue(len(a_		) == 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"""]		),      a_		)
     self.assertEqual(
         el["""segmentation"""].shape,      (self.image_processing_tester.height, self.image_processing_tester.width)		)








"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  stooge(snake_case__		,   0		,   len(snake_case__    ) - 1    )
  return arr


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any		,   snake_case__							:					int    ):
  """simple docstring"""

  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case		,					_snake_case							:		Tuple								=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case							:		Dict								=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )

    # Recursively sort last 2/3 elements
    stooge(snake_case__		,   i + t		,   (snake_case__)    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(stooge_sort(unsorted))


