"""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__       (				lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Optional[int]="shi-labs/oneformer_demo"		) ->    List[str]:
      '''simple docstring'''


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





class  _A						(       unittest.TestCase	):

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







            """simple docstring"""



            lowercase														=	parent
            lowercase														=	batch_size
            lowercase														=	num_channels
            lowercase														=	min_resolution
            lowercase														=	max_resolution
            lowercase														=	do_resize
            lowercase														=	{"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
            lowercase														=	do_normalize
            lowercase														=	image_mean
            lowercase														=	image_std
            lowercase														=	class_info_file
            lowercase														=	prepare_metadata(__lowerCAmelCase		,       __lowerCAmelCase						)
            lowercase														=	num_text
            lowercase														=	repo_path

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

      def 				A__       (						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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase=False						):







            """simple docstring"""



            if not batched:
                  lowercase														=	image_inputs[0]
                  if isinstance(__lowerCAmelCase		,       Image.Image						):
                        lowercase							,							lowercase														=	image.size
                  else:
                        lowercase							,							lowercase														=	image.shape[1], image.shape[2]
                  if w < h:
                        lowercase														=	int(self.size["""shortest_edge"""] * h / w						)
                        lowercase														=	self.size["""shortest_edge"""]
                  elif w > h:
                        lowercase														=	self.size["""shortest_edge"""]
                        lowercase														=	int(self.size["""shortest_edge"""] * w / h						)
                  else:
                        lowercase														=	self.size["""shortest_edge"""]
                        lowercase														=	self.size["""shortest_edge"""]

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

            return expected_height, expected_width







      def 				A__       (						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						(       lowerCAmelCase  ,    unittest.TestCase	):
      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
      snake_case__			:   str							  =	image_processing_class

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	OneFormerImageProcessorTester(self						)

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return self.image_processing_tester.prepare_image_processor_dict()

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.image_processing_class(**self.image_processor_dict						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """image_mean"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """image_std"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """do_normalize"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """do_resize"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """size"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """ignore_index"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """class_info_file"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """num_text"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """repo_path"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """metadata"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """do_reduce_labels"""						)						)

      def 				A__       (						self						):







            """simple docstring"""



            pass

      def 				A__       (						self						):







            """simple docstring"""



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

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

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

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

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

            lowercase														=	image_processor(
                __lowerCAmelCase		,       ["""semantic"""] * len(__lowerCAmelCase						)		,       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						):







            """simple docstring"""



            lowercase														=	self.image_processing_class(**self.image_processor_dict						)
            # create random numpy tensors
            lowercase														=	prepare_image_inputs(self.image_processing_tester		,       equal_resolution=__lowerCAmelCase		,       numpify=__lowerCAmelCase						)
            for image in image_inputs:
                  self.assertIsInstance(__lowerCAmelCase		,       np.ndarray						)

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

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

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

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

            lowercase														=	image_processor(
                __lowerCAmelCase		,       ["""semantic"""] * len(__lowerCAmelCase						)		,       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						):







            """simple docstring"""



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

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

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

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

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

            lowercase														=	image_processor(
                __lowerCAmelCase		,       ["""semantic"""] * len(__lowerCAmelCase						)		,       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		,       __lowerCAmelCase=False		,       __lowerCAmelCase=False		,       __lowerCAmelCase="np"						):







            """simple docstring"""



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

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

            return inputs

      def 				A__       (						self						):







            """simple docstring"""



            pass

      def 				A__       (						self						):







            """simple docstring"""



            def common(__lowerCAmelCase=False		,       __lowerCAmelCase=None						):
                  lowercase														=	self.comm_get_image_processor_inputs(
                      with_segmentation_maps=__lowerCAmelCase		,       is_instance_map=__lowerCAmelCase		,       segmentation_type=__lowerCAmelCase						)

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

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

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

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	np.zeros((20, 50)						)
            lowercase														=	1
            lowercase														=	1
            lowercase														=	1

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

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	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"""		,       )
            lowercase														=	self.image_processing_tester.get_fake_oneformer_outputs()

            lowercase														=	fature_extractor.post_process_semantic_segmentation(__lowerCAmelCase						)

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

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

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

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	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"""		,       )
            lowercase														=	self.image_processing_tester.get_fake_oneformer_outputs()
            lowercase														=	image_processor.post_process_instance_segmentation(__lowerCAmelCase		,       threshold=0						)

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







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	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"""		,       )
            lowercase														=	self.image_processing_tester.get_fake_oneformer_outputs()
            lowercase														=	image_processor.post_process_panoptic_segmentation(__lowerCAmelCase		,       threshold=0						)

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




"""simple docstring"""






from __future__ import annotations

import numpy as np


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[float]		) ->    Optional[Any]:
      '''simple docstring'''


      return np.maximum(0					,		lowerCAmelCase__		)


if __name__ == "__main__":
       print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



