





'''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							,     lowerCAmelCase="shi-labs/oneformer_demo"  ) ->    Tuple:





      """simple docstring"""


      with open(hf_hub_download(_lowerCamelCase							,     _lowerCamelCase							,     repo_type="""dataset"""  )							,     """r"""  ) as f:
            _lowerCAmelCase												=							json.load(_lowerCamelCase  )
      _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(_lowerCamelCase  )  )
      _lowerCAmelCase												=							thing_ids
      _lowerCAmelCase												=							class_names
      return metadata




class 			UpperCAmelCase					(      unittest.TestCase       ):




      def __init__(      self      :			str      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			int=7      ,      __snake_case      :			Tuple=3      ,      __snake_case      :			int=30      ,      __snake_case      :			str=4_00      ,      __snake_case      :			Tuple=None      ,      __snake_case      :			Optional[int]=True      ,      __snake_case      :			List[str]=True      ,      __snake_case      :			Tuple=[0.5, 0.5, 0.5]      ,      __snake_case      :			List[Any]=[0.5, 0.5, 0.5]      ,      __snake_case      :			Any=10      ,      __snake_case      :			Optional[int]=False      ,      __snake_case      :			Union[str, Any]=2_55      ,      __snake_case      :			str="shi-labs/oneformer_demo"      ,      __snake_case      :			Dict="ade20k_panoptic.json"      ,      __snake_case      :			List[Any]=10      ,      )    ->				List[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(__snake_case      ,      __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     lowercase__   (      self      :			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     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			int      ,      __snake_case      :			Tuple=False  )    ->				str:
            if not batched:
                  _lowerCAmelCase												=							image_inputs[0]
                  if isinstance(__snake_case      ,      Image.Image  ):
                        _lowerCAmelCase												=							image.size
                  else:
                        _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												=							self.get_expected_values([image]  )
                        expected_values.append((expected_height, expected_width)  )
                  _lowerCAmelCase												=							max(__snake_case      ,      key=lambda __snake_case  : item[0]  )[0]
                  _lowerCAmelCase												=							max(__snake_case      ,      key=lambda __snake_case  : item[1]  )[1]

            return expected_height, expected_width




      def     lowercase__   (      self      :			Optional[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 			UpperCAmelCase					(      a__							,		unittest.TestCase       ):
      _lowercase:      Optional[int]											=       OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      _lowercase:      Optional[int]											=       image_processing_class




      def     lowercase__   (      self      :			List[Any]  )    ->				Tuple:
            _lowerCAmelCase												=							OneFormerImageProcessorTester(self  )




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




      def     lowercase__   (      self      :			str  )    ->				List[str]:
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(__snake_case      ,      """image_mean"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """image_std"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """do_normalize"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """do_resize"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """size"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """ignore_index"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """class_info_file"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """num_text"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """repo_path"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """metadata"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """do_reduce_labels"""  )  )




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Union[str, Any]:
            pass




      def     lowercase__   (      self      :			List[Any]  )    ->				int:
            # 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=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      Image.Image  )

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

            _lowerCAmelCase												=							self.image_processing_tester.get_expected_values(__snake_case  )

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

            # Test batched
            _lowerCAmelCase												=							self.image_processing_tester.get_expected_values(__snake_case      ,      batched=__snake_case  )

            _lowerCAmelCase												=							image_processor(
                __snake_case      ,      ["""semantic"""] * len(__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     lowercase__   (      self      :			str  )    ->				List[Any]:
            # 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=__snake_case      ,      numpify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      np.ndarray  )

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

            _lowerCAmelCase												=							self.image_processing_tester.get_expected_values(__snake_case  )

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

            # Test batched
            _lowerCAmelCase												=							self.image_processing_tester.get_expected_values(__snake_case      ,      batched=__snake_case  )

            _lowerCAmelCase												=							image_processor(
                __snake_case      ,      ["""semantic"""] * len(__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     lowercase__   (      self      :			Union[str, Any]  )    ->				Any:
            # 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=__snake_case      ,      torchify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      torch.Tensor  )

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

            _lowerCAmelCase												=							self.image_processing_tester.get_expected_values(__snake_case  )

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

            # Test batched
            _lowerCAmelCase												=							self.image_processing_tester.get_expected_values(__snake_case      ,      batched=__snake_case  )

            _lowerCAmelCase												=							image_processor(
                __snake_case      ,      ["""semantic"""] * len(__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     lowercase__   (      self      :			Tuple      ,      __snake_case      :			List[Any]=False      ,      __snake_case      :			Dict=False      ,      __snake_case      :			Dict="np"  )    ->				List[str]:
            _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=__snake_case  )
            if with_segmentation_maps:
                  _lowerCAmelCase												=							num_labels
                  if is_instance_map:
                        _lowerCAmelCase												=							list(range(__snake_case  )  ) * 2
                        _lowerCAmelCase												=							dict(enumerate(__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(__snake_case  ) for annotation in annotations]

            _lowerCAmelCase												=							image_processor(
                __snake_case      ,      ["""semantic"""] * len(__snake_case  )      ,      __snake_case      ,      return_tensors="""pt"""      ,      instance_id_to_semantic_id=__snake_case      ,      pad_and_return_pixel_mask=__snake_case      ,      )

            return inputs




      def     lowercase__   (      self      :			Dict  )    ->				Dict:
            pass




      def     lowercase__   (      self      :			int  )    ->				Dict:
            def common(__snake_case      :			Tuple=False      ,      __snake_case      :			Optional[int]=None  ):
                  _lowerCAmelCase												=							self.comm_get_image_processor_inputs(
                      with_segmentation_maps=__snake_case      ,      is_instance_map=__snake_case      ,      segmentation_type=__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(__snake_case      ,      __snake_case      ,      __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(__snake_case  )      ,      self.image_processing_tester.num_text  )

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




      def     lowercase__   (      self      :			Optional[Any]  )    ->				Optional[int]:
            _lowerCAmelCase												=							np.zeros((20, 50)  )
            _lowerCAmelCase												=							1
            _lowerCAmelCase												=							1
            _lowerCAmelCase												=							1

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




      def     lowercase__   (      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												=							fature_extractor.post_process_semantic_segmentation(__snake_case  )

            self.assertEqual(len(__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(__snake_case      ,      target_sizes=__snake_case  )

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




      def     lowercase__   (      self      :			Optional[Any]  )    ->				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(__snake_case      ,      threshold=0  )

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




      def     lowercase__   (      self      :			Tuple  )    ->				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												=							image_processor.post_process_panoptic_segmentation(__snake_case      ,      threshold=0  )

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













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if not (isinstance(lowerCAmelCase							,     lowerCAmelCase  ) and isinstance(lowerCAmelCase							,     lowerCAmelCase  )):
            raise ValueError("""longest_common_substring() takes two strings for inputs"""  )

      _lowerCAmelCase												=							len(lowerCAmelCase  )
      _lowerCAmelCase												=							len(lowerCAmelCase  )

      _lowerCAmelCase												=							[[0] * (texta_length + 1) for _ in range(texta_length + 1  )]
      _lowerCAmelCase												=							0
      _lowerCAmelCase												=							0

      for i in range(1							,     texta_length + 1  ):
            for j in range(1							,     texta_length + 1  ):
                  if texta[i - 1] == texta[j - 1]:
                        _lowerCAmelCase												=							1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              _lowerCAmelCase												=							i
                              _lowerCAmelCase												=							dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
      import doctest

      doctest.testmod()






