

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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase="shi-labs/oneformer_demo"					)							->     Optional[int]:



  """simple docstring"""






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



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    int      , A__				:    Optional[Any]      , A__				:    str=7      , A__				:    Optional[Any]=3      , A__				:    Union[str, Any]=30      , A__				:    Optional[Any]=400      , A__				:    List[str]=None      , A__				:    Optional[int]=True      , A__				:    int=True      , A__				:    Tuple=[0.5, 0.5, 0.5]      , A__				:    str=[0.5, 0.5, 0.5]      , A__				:    Tuple=10      , A__				:    str=False      , A__				:    Optional[int]=255      , A__				:    Optional[int]="shi-labs/oneformer_demo"      , A__				:    Optional[Any]="ade20k_panoptic.json"      , A__				:    str=10      , )   ->					int:
        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							num_channels
        _snake_case						=							min_resolution
        _snake_case						=							max_resolution
        _snake_case						=							do_resize
        _snake_case						=							{'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
        _snake_case						=							do_normalize
        _snake_case						=							image_mean
        _snake_case						=							image_std
        _snake_case						=							class_info_file
        _snake_case						=							prepare_metadata(A__      , A__		)
        _snake_case						=							num_text
        _snake_case						=							repo_path

        # for the post_process_functions
        _snake_case						=							2
        _snake_case						=							10
        _snake_case						=							10
        _snake_case						=							3
        _snake_case						=							4
        _snake_case						=							num_labels
        _snake_case						=							do_reduce_labels
        _snake_case						=							ignore_index



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

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

        return expected_height, expected_width







      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Dict:
        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_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Dict										=    OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      UpperCamelCase_      :		List[Any]										=    image_processing_class



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					int:
        _snake_case						=							OneFormerImageProcessorTester(self		)



      @property
      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Any:
        return self.image_processing_tester.prepare_image_processor_dict()



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Union[str, Any]:
        _snake_case						=							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				:    Dict		)   ->					int:
        pass



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[Any]:
        # Initialize image_processor
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        _snake_case						=							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						=							image_processor(image_inputs[0]      , ['''semantic''']      , return_tensors='''pt'''		).pixel_values

        _snake_case,					_snake_case						=							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						=							self.image_processing_tester.get_expected_values(A__      , batched=A__		)

        _snake_case						=							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				:    List[Any]		)   ->					List[str]:
        # Initialize image_processor
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        _snake_case						=							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						=							image_processor(image_inputs[0]      , ['''semantic''']      , return_tensors='''pt'''		).pixel_values

        _snake_case,					_snake_case						=							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						=							self.image_processing_tester.get_expected_values(A__      , batched=A__		)

        _snake_case						=							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				:    List[Any]		)   ->					List[Any]:
        # Initialize image_processor
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        _snake_case						=							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						=							image_processor(image_inputs[0]      , ['''semantic''']      , return_tensors='''pt'''		).pixel_values

        _snake_case,					_snake_case						=							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						=							self.image_processing_tester.get_expected_values(A__      , batched=A__		)

        _snake_case						=							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      , A__				:    List[str]=False      , A__				:    Tuple=False      , A__				:    List[str]="np"		)   ->					str:
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # prepare image and target
        _snake_case						=							self.image_processing_tester.num_labels
        _snake_case						=							None
        _snake_case						=							None
        _snake_case						=							prepare_image_inputs(self.image_processing_tester      , equal_resolution=A__		)
        if with_segmentation_maps:
          _snake_case						=							num_labels
          if is_instance_map:
            _snake_case						=							list(range(A__		)		) * 2
            _snake_case						=							dict(enumerate(A__		)		)
          _snake_case						=							[
              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						=							[Image.fromarray(A__		) for annotation in annotations]

        _snake_case						=							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				:    Any		)   ->					Dict:
        pass



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[Any]:
        def common(A__				:    Dict=False      , A__				:    int=None		):
          _snake_case						=							self.comm_get_image_processor_inputs(
              with_segmentation_maps=A__      , is_instance_map=A__      , segmentation_type=A__		)

          _snake_case						=							inputs['''mask_labels''']
          _snake_case						=							inputs['''class_labels''']
          _snake_case						=							inputs['''pixel_values''']
          _snake_case						=							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				:    Optional[int]		)   ->					Tuple:
        _snake_case						=							np.zeros((20, 50)		)
        _snake_case						=							1
        _snake_case						=							1
        _snake_case						=							1

        _snake_case						=							binary_mask_to_rle(A__		)
        self.assertEqual(len(A__		)      , 4		)
        self.assertEqual(rle[0]      , 21		)
        self.assertEqual(rle[1]      , 45		)



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Dict:
        _snake_case						=							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						=							self.image_processing_tester.get_fake_oneformer_outputs()

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

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



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Union[str, Any]:
        _snake_case						=							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						=							self.image_processing_tester.get_fake_oneformer_outputs()
        _snake_case						=							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				:    int		)   ->					Dict:
        _snake_case						=							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						=							self.image_processing_tester.get_fake_oneformer_outputs()
        _snake_case						=							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)		)




import cmath
import math



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     complex:



  """simple docstring"""






  _snake_case						=							math.radians(_UpperCamelCase					)
  _snake_case						=							math.radians(_UpperCamelCase					)

  # Convert voltage and current to rectangular form
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)

  # Calculate apparent power
  return voltage_rect * current_rect


if __name__ == "__main__":
 import doctest

 doctest.testmod()

