'''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__					(					_lowercase    :				List[str]   ,    _lowercase    :				Tuple="shi-labs/oneformer_demo"     )							->	Dict:







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




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=None   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=[0.5, 0.5, 0.5]   ,    snake_case_=[0.5, 0.5, 0.5]   ,    snake_case_=10   ,    snake_case_=False   ,    snake_case_=255   ,    snake_case_="shi-labs/oneformer_demo"   ,    snake_case_="ade20k_panoptic.json"   ,    snake_case_=10   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= parent
    __UpperCAmelCase:					Union[str, Any]		= batch_size
    __UpperCAmelCase:					Any		= num_channels
    __UpperCAmelCase:					Optional[int]		= min_resolution
    __UpperCAmelCase:					Optional[int]		= max_resolution
    __UpperCAmelCase:					Optional[int]		= do_resize
    __UpperCAmelCase:					List[Any]		= {'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
    __UpperCAmelCase:					List[str]		= do_normalize
    __UpperCAmelCase:					Any		= image_mean
    __UpperCAmelCase:					Union[str, Any]		= image_std
    __UpperCAmelCase:					int		= class_info_file
    __UpperCAmelCase:					Optional[Any]		= prepare_metadata(UpperCamelCase__   ,    UpperCamelCase__					)
    __UpperCAmelCase:					Optional[int]		= num_text
    __UpperCAmelCase:					Optional[int]		= repo_path

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







  def    lowercase_  (							self					):

    '''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    lowercase_  (							self   ,    snake_case_   ,    snake_case_=False					):

    '''simple docstring'''







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

    else:
      __UpperCAmelCase:					Tuple		= []
      for image in image_inputs:
        __UpperCAmelCase:					Tuple		= self.get_expected_values([image]					)
        expected_values.append((expected_height, expected_width)					)
      __UpperCAmelCase:					List[Any]		= max(UpperCamelCase__   ,    key=lambda snake_case_					: item[0]					)[0]
      __UpperCAmelCase:					Union[str, Any]		= max(UpperCamelCase__   ,    key=lambda snake_case_					: item[1]					)[1]

    return expected_height, expected_width







  def    lowercase_  (							self					):

    '''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    a (       SCREAMING_SNAKE_CASE_						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  __lowerCAmelCase				      =					image_processing_class







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= OneFormerImageProcessorTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processing_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """image_mean"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """image_std"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """do_normalize"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """size"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """ignore_index"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """class_info_file"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """num_text"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """repo_path"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """metadata"""					)					)
    self.assertTrue(hasattr(UpperCamelCase__   ,    """do_reduce_labels"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    # create random PIL images
    __UpperCAmelCase:					Dict		= prepare_image_inputs(self.image_processing_tester   ,    equal_resolution=UpperCamelCase__					)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__   ,    Image.Image					)

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

    __UpperCAmelCase:					Any		= self.image_processing_tester.get_expected_values(UpperCamelCase__					)

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

    # Test batched
    __UpperCAmelCase:					str		= self.image_processing_tester.get_expected_values(UpperCamelCase__   ,    batched=UpperCamelCase__					)

    __UpperCAmelCase:					Optional[Any]		= image_processor(
        UpperCamelCase__   ,    ["""semantic"""] * len(UpperCamelCase__					)   ,    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    lowercase_  (							self					):

    '''simple docstring'''







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

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

    __UpperCAmelCase:					Optional[Any]		= self.image_processing_tester.get_expected_values(UpperCamelCase__					)

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

    # Test batched
    __UpperCAmelCase:					List[str]		= self.image_processing_tester.get_expected_values(UpperCamelCase__   ,    batched=UpperCamelCase__					)

    __UpperCAmelCase:					Optional[Any]		= image_processor(
        UpperCamelCase__   ,    ["""semantic"""] * len(UpperCamelCase__					)   ,    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    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.image_processing_class(**self.image_processor_dict					)
    # create random PyTorch tensors
    __UpperCAmelCase:					Union[str, Any]		= prepare_image_inputs(self.image_processing_tester   ,    equal_resolution=UpperCamelCase__   ,    torchify=UpperCamelCase__					)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__   ,    torch.Tensor					)

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

    __UpperCAmelCase:					Union[str, Any]		= self.image_processing_tester.get_expected_values(UpperCamelCase__					)

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

    # Test batched
    __UpperCAmelCase:					int		= self.image_processing_tester.get_expected_values(UpperCamelCase__   ,    batched=UpperCamelCase__					)

    __UpperCAmelCase:					Any		= image_processor(
        UpperCamelCase__   ,    ["""semantic"""] * len(UpperCamelCase__					)   ,    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    lowercase_  (							self   ,    snake_case_=False   ,    snake_case_=False   ,    snake_case_="np"					):

    '''simple docstring'''







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

    __UpperCAmelCase:					Optional[int]		= image_processor(
        UpperCamelCase__   ,    ["""semantic"""] * len(UpperCamelCase__					)   ,    UpperCamelCase__   ,    return_tensors="""pt"""   ,    instance_id_to_semantic_id=UpperCamelCase__   ,    pad_and_return_pixel_mask=UpperCamelCase__   ,    )

    return inputs







  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    def common(snake_case_=False   ,    snake_case_=None					):
      __UpperCAmelCase:					List[Any]		= self.comm_get_image_processor_inputs(
          with_segmentation_maps=UpperCamelCase__   ,    is_instance_map=UpperCamelCase__   ,    segmentation_type=UpperCamelCase__					)

      __UpperCAmelCase:					Optional[int]		= inputs['''mask_labels''']
      __UpperCAmelCase:					Union[str, Any]		= inputs['''class_labels''']
      __UpperCAmelCase:					Dict		= inputs['''pixel_values''']
      __UpperCAmelCase:					Union[str, Any]		= inputs['''text_inputs''']

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

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= np.zeros((20, 50)					)
    __UpperCAmelCase:					Any		= 1
    __UpperCAmelCase:					str		= 1
    __UpperCAmelCase:					Dict		= 1

    __UpperCAmelCase:					Tuple		= binary_mask_to_rle(UpperCamelCase__					)
    self.assertEqual(len(UpperCamelCase__					)   ,    4					)
    self.assertEqual(rle[0]   ,    21					)
    self.assertEqual(rle[1]   ,    45					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, 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"""   ,    )
    __UpperCAmelCase:					List[Any]		= self.image_processing_tester.get_fake_oneformer_outputs()

    __UpperCAmelCase:					Union[str, Any]		= fature_extractor.post_process_semantic_segmentation(UpperCamelCase__					)

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

    __UpperCAmelCase:					Dict		= [(1, 4) for i in range(self.image_processing_tester.batch_size					)]
    __UpperCAmelCase:					Optional[int]		= fature_extractor.post_process_semantic_segmentation(UpperCamelCase__   ,    target_sizes=UpperCamelCase__					)

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, 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"""   ,    )
    __UpperCAmelCase:					int		= self.image_processing_tester.get_fake_oneformer_outputs()
    __UpperCAmelCase:					str		= image_processor.post_process_instance_segmentation(UpperCamelCase__   ,    threshold=0					)

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					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"""   ,    )
    __UpperCAmelCase:					Tuple		= self.image_processing_tester.get_fake_oneformer_outputs()
    __UpperCAmelCase:					int		= image_processor.post_process_panoptic_segmentation(UpperCamelCase__   ,    threshold=0					)

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






'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

SCREAMING_SNAKE_CASE_	   =			[8, 5, 9, 7]
SCREAMING_SNAKE_CASE_	   =			[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
SCREAMING_SNAKE_CASE_	   =			[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					str		= claim_vector
    __UpperCAmelCase:					Optional[Any]		= allocated_resources_table
    __UpperCAmelCase:					List[str]		= maximum_claim_table







  def    lowercase_  (							self					):

    '''simple docstring'''







    return [
        sum(p_item[i] for p_item in self.__allocated_resources_table					)
        for i in range(len(self.__allocated_resources_table[0]					)					)
    ]







  def    lowercase_  (							self					):

    '''simple docstring'''







    return np.array(self.__claim_vector					) - np.array(
        self.__processes_resource_summation()					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    return [
        list(np.array(self.__maximum_claim_table[i]					) - np.array(snake_case_					)					)
        for i, allocated_resource in enumerate(self.__allocated_resources_table					)
    ]







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {self.__need().index(snake_case_					): i for i in self.__need()}







  def    lowercase_  (							self   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= self.__need()
    __UpperCAmelCase:					Optional[Any]		= self.__allocated_resources_table
    __UpperCAmelCase:					int		= self.__available_resources()
    __UpperCAmelCase:					List[Any]		= self.__need_index_manager()
    for kw, val in kwargs.items():
      if kw and val is True:
        self.__pretty_data()
    print("""_""" * 50 + """\n"""					)
    while need_list:
      __UpperCAmelCase:					Any		= False
      for each_need in need_list:
        __UpperCAmelCase:					Any		= True
        for index, need in enumerate(snake_case_					):
          if need > available_resources[index]:
            __UpperCAmelCase:					Tuple		= False
            break
        if execution:
          __UpperCAmelCase:					Optional[int]		= True
          # get the original index of the process from ind_ctrl db
          for original_need_index, need_clone in need_index_manager.items():
            if each_need == need_clone:
              __UpperCAmelCase:					Tuple		= original_need_index
          print(F'''Process {process_number + 1} is executing.'''					)
          # remove the process run from stack
          need_list.remove(snake_case_					)
          # update available/freed resources stack
          __UpperCAmelCase:					Dict		= np.array(snake_case_					) + np.array(
              alloc_resources_table[process_number]					)
          print(
              """Updated available resource stack for processes: """
              + """ """.join([str(snake_case_					) for x in available_resources]					)					)
          break
      if safe:
        print("""The process is in a safe state.\n"""					)
      else:
        print("""System in unsafe state. Aborting...\n"""					)
        break







  def    lowercase_  (							self					):

    '''simple docstring'''







    print(""" """ * 9 + """Allocated Resource Table"""					)
    for item in self.__allocated_resources_table:
      print(
          F'''P{self.__allocated_resources_table.index(snake_case_					) + 1}'''
          + """ """.join(F'''{it:>8}''' for it in item					)
          + """\n"""					)
    print(""" """ * 9 + """System Resource Table"""					)
    for item in self.__maximum_claim_table:
      print(
          F'''P{self.__maximum_claim_table.index(snake_case_					) + 1}'''
          + """ """.join(F'''{it:>8}''' for it in item					)
          + """\n"""					)
    print(
        """Current Usage by Active Processes: """
        + """ """.join(str(snake_case_					) for x in self.__claim_vector					)					)
    print(
        """Initial Available Resources:       """
        + """ """.join(str(snake_case_					) for x in self.__available_resources()					)					)
    time.sleep(1					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()