


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 PIL import Image

 from transformers import DetaImageProcessor






class 	__lowercase					(	unittest.TestCase	):





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       List[Any]=7       ,							__lowerCamelCase    :       str=3       ,							__lowerCamelCase    :       List[str]=3_0       ,							__lowerCamelCase    :       Union[str, Any]=4_0_0       ,							__lowerCamelCase    :       List[Any]=True       ,							__lowerCamelCase    :       Dict=None       ,							__lowerCamelCase    :       Dict=True       ,							__lowerCamelCase    :       List[Any]=[0.5, 0.5, 0.5]       ,							__lowerCamelCase    :       int=[0.5, 0.5, 0.5]       ,							__lowerCamelCase    :       Dict=True       ,							__lowerCamelCase    :       Union[str, Any]=1 / 2_5_5       ,							__lowerCamelCase    :       Any=True       ,							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  min_resolution
         UpperCAmelCase      					=  max_resolution
         UpperCAmelCase      					=  do_resize
         UpperCAmelCase      					=  size
         UpperCAmelCase      					=  do_normalize
         UpperCAmelCase      					=  image_mean
         UpperCAmelCase      					=  image_std
         UpperCAmelCase      					=  do_rescale
         UpperCAmelCase      					=  rescale_factor
         UpperCAmelCase      					=  do_pad





      def 							_lowercase					(		self    :       int							)     -> 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,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       Any=False							)     -> List[Any]:






         """simple docstring"""







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

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

         return expected_height, expected_width






@require_torch
@require_vision
class 	__lowercase					(	a_			,       unittest.TestCase	):
      UpperCamelCase										=							DetaImageProcessor if is_vision_available() else None





      def 							_lowercase					(		self    :       Dict							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  DetaImageProcessingTester(self							)





      @property
      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[Any]:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  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       ,							"""do_rescale"""							)							)
         self.assertTrue(hasattr(__UpperCamelCase       ,							"""do_pad"""							)							)
         self.assertTrue(hasattr(__UpperCamelCase       ,							"""size"""							)							)





      def 							_lowercase					(		self    :       List[str]							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size       ,							{"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}							)
         self.assertEqual(image_processor.do_pad       ,							__UpperCamelCase							)





      def 							_lowercase					(		self    :       Dict							)     -> Tuple:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         UpperCAmelCase      					=  prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCamelCase							)
         for image in image_inputs:
            self.assertIsInstance(__UpperCamelCase       ,							Image.Image							)

         # Test not batched input
         UpperCAmelCase      					=  image_processing(image_inputs[0]       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__UpperCamelCase							)

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

         # Test batched
         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__UpperCamelCase       ,							batched=__UpperCamelCase							)

         UpperCAmelCase      					=  image_processing(__UpperCamelCase       ,							return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Optional[int]:






         """simple docstring"""







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

         # Test not batched input
         UpperCAmelCase      					=  image_processing(image_inputs[0]       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__UpperCamelCase							)

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

         # Test batched
         UpperCAmelCase      					=  image_processing(__UpperCamelCase       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__UpperCamelCase       ,							batched=__UpperCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,							)





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Union[str, Any]:






         """simple docstring"""







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

         # Test not batched input
         UpperCAmelCase      					=  image_processing(image_inputs[0]       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__UpperCamelCase							)

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

         # Test batched
         UpperCAmelCase      					=  image_processing(__UpperCamelCase       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__UpperCamelCase       ,							batched=__UpperCamelCase							)

         self.assertEqual(
             encoded_images.shape       ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,							)





      @slow
      def 							_lowercase					(		self    :       str							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
         with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,							"""r"""							) as f:
            UpperCAmelCase      					=  json.loads(f.read()							)

         UpperCAmelCase      					=  {"""image_id""": 3_9_7_6_9, """annotations""": target}

         # encode them
         UpperCAmelCase      					=  DetaImageProcessor()
         UpperCAmelCase      					=  image_processing(images=__UpperCamelCase       ,							annotations=__UpperCamelCase       ,							return_tensors="""pt"""							)

         # verify pixel values
         UpperCAmelCase      					=  torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
         self.assertEqual(encoding["""pixel_values"""].shape       ,							__UpperCamelCase							)

         UpperCAmelCase      					=  torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,							__UpperCamelCase       ,							atol=1e-4							)							)

         # verify area
         UpperCAmelCase      					=  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,							__UpperCamelCase							)							)
         # verify boxes
         UpperCAmelCase      					=  torch.Size([6, 4]							)
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							__UpperCamelCase							)
         UpperCAmelCase      					=  torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							__UpperCamelCase       ,							atol=1e-3							)							)
         # verify image_id
         UpperCAmelCase      					=  torch.tensor([3_9_7_6_9]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							__UpperCamelCase							)							)
         # verify is_crowd
         UpperCAmelCase      					=  torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							__UpperCamelCase							)							)
         # verify class_labels
         UpperCAmelCase      					=  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							__UpperCamelCase							)							)
         # verify orig_size
         UpperCAmelCase      					=  torch.tensor([4_8_0, 6_4_0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							__UpperCamelCase							)							)
         # verify size
         UpperCAmelCase      					=  torch.tensor([8_0_0, 1_0_6_6]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							__UpperCamelCase							)							)





      @slow
      def 							_lowercase					(		self    :       str							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
         with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""       ,							"""r"""							) as f:
            UpperCAmelCase      					=  json.loads(f.read()							)

         UpperCAmelCase      					=  {"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

         UpperCAmelCase      					=  pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

         # encode them
         UpperCAmelCase      					=  DetaImageProcessor(format="""coco_panoptic"""							)
         UpperCAmelCase      					=  image_processing(images=__UpperCamelCase       ,							annotations=__UpperCamelCase       ,							masks_path=__UpperCamelCase       ,							return_tensors="""pt"""							)

         # verify pixel values
         UpperCAmelCase      					=  torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
         self.assertEqual(encoding["""pixel_values"""].shape       ,							__UpperCamelCase							)

         UpperCAmelCase      					=  torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,							__UpperCamelCase       ,							atol=1e-4							)							)

         # verify area
         UpperCAmelCase      					=  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,							__UpperCamelCase							)							)
         # verify boxes
         UpperCAmelCase      					=  torch.Size([6, 4]							)
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							__UpperCamelCase							)
         UpperCAmelCase      					=  torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							__UpperCamelCase       ,							atol=1e-3							)							)
         # verify image_id
         UpperCAmelCase      					=  torch.tensor([3_9_7_6_9]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							__UpperCamelCase							)							)
         # verify is_crowd
         UpperCAmelCase      					=  torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							__UpperCamelCase							)							)
         # verify class_labels
         UpperCAmelCase      					=  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							__UpperCamelCase							)							)
         # verify masks
         UpperCAmelCase      					=  8_2_2_8_7_3
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,							__UpperCamelCase							)
         # verify orig_size
         UpperCAmelCase      					=  torch.tensor([4_8_0, 6_4_0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							__UpperCamelCase							)							)
         # verify size
         UpperCAmelCase      					=  torch.tensor([8_0_0, 1_0_6_6]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							__UpperCamelCase							)							)











from __future__ import annotations





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       len(__snake_case							)
     # We need to create solution object to save path.
     __lowerCAmelCase             =       [[0 for _ in range(__snake_case							)] for _ in range(__snake_case							)]
     __lowerCAmelCase             =       run_maze(__snake_case       ,			0       ,			0       ,			__snake_case							)
     if solved:
          print("\n".join(str(__snake_case							) for row in solutions							)							)
     else:
          print("No solution exists!"							)
     return solved





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       len(__snake_case							)
     # Final check point.
     if i == j == (size - 1):
          __lowerCAmelCase             =       1
          return True

     __lowerCAmelCase             =       (not i < 0) and (not j < 0)  # Check lower bounds
     __lowerCAmelCase             =       (i < size) and (j < size)  # Check upper bounds

     if lower_flag and upper_flag:
          # check for already visited and block points.
          __lowerCAmelCase             =       (not solutions[i][j]) and (not maze[i][j])
          if block_flag:
               # check visited
               __lowerCAmelCase             =       1

               # check for directions
               if (
                   run_maze(__snake_case       ,			i + 1       ,			__snake_case       ,			__snake_case							)
                   or run_maze(__snake_case       ,			__snake_case       ,			j + 1       ,			__snake_case							)
                   or run_maze(__snake_case       ,			i - 1       ,			__snake_case       ,			__snake_case							)
                   or run_maze(__snake_case       ,			__snake_case       ,			j - 1       ,			__snake_case							)
               ):
                    return True

               __lowerCAmelCase             =       0
               return False
     return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()






