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 DetrImageProcessor


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       int  ,     __magic_name__ :       Optional[int]=7  ,     __magic_name__ :       Union[str, Any]=3  ,     __magic_name__ :       str=30  ,     __magic_name__ :       Optional[int]=400  ,     __magic_name__ :       str=True  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       List[Any]=True  ,     __magic_name__ :       Any=1 / 255  ,     __magic_name__ :       List[Any]=True  ,     __magic_name__ :       Tuple=[0.5, 0.5, 0.5]  ,     __magic_name__ :       Any=[0.5, 0.5, 0.5]  ,     __magic_name__ :       str=True  ,     )  ->	Dict:
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        SCREAMING_SNAKE_CASE_          =  size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  batch_size
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  min_resolution
        SCREAMING_SNAKE_CASE_          =  max_resolution
        SCREAMING_SNAKE_CASE_          =  do_resize
        SCREAMING_SNAKE_CASE_          =  size
        SCREAMING_SNAKE_CASE_          =  do_rescale
        SCREAMING_SNAKE_CASE_          =  rescale_factor
        SCREAMING_SNAKE_CASE_          =  do_normalize
        SCREAMING_SNAKE_CASE_          =  image_mean
        SCREAMING_SNAKE_CASE_          =  image_std
        SCREAMING_SNAKE_CASE_          =  do_pad







  def    __A     (							self :       List[Any]   )  ->	Tuple:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }







  def    __A     (							self :       Tuple  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[Any]=False   )  ->	Dict:
        if not batched:
              SCREAMING_SNAKE_CASE_          =  image_inputs[0]
              if isinstance(__magic_name__  ,     Image.Image   ):
                    SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  image.size
              else:
                    SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  image.shape[1], image.shape[2]
              if w < h:
                    SCREAMING_SNAKE_CASE_          =  int(self.size["shortest_edge"] * h / w   )
                    SCREAMING_SNAKE_CASE_          =  self.size["shortest_edge"]
              elif w > h:
                    SCREAMING_SNAKE_CASE_          =  self.size["shortest_edge"]
                    SCREAMING_SNAKE_CASE_          =  int(self.size["shortest_edge"] * w / h   )
              else:
                    SCREAMING_SNAKE_CASE_          =  self.size["shortest_edge"]
                    SCREAMING_SNAKE_CASE_          =  self.size["shortest_edge"]

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

        return expected_height, expected_width



@require_torch
@require_vision
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						DetrImageProcessor if is_vision_available() else None







  def    __A     (							self :       str   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  DetrImageProcessingTester(self   )







  @property
  def    __A     (							self :       Any   )  ->	Optional[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()







  def    __A     (							self :       Dict   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(__magic_name__  ,     "image_mean"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "image_std"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_normalize"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_rescale"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "rescale_factor"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_resize"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "size"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_pad"   )   )







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size  ,     {"shortest_edge": 18, "longest_edge": 1_333}   )
        self.assertEqual(image_processor.do_pad  ,     __magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.image_processing_class.from_dict(
            self.image_processor_dict  ,     size=42  ,     max_size=84  ,     pad_and_return_pixel_mask=__magic_name__   )
        self.assertEqual(image_processor.size  ,     {"shortest_edge": 42, "longest_edge": 84}   )
        self.assertEqual(image_processor.do_pad  ,     __magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        pass







  def    __A     (							self :       Any   )  ->	List[Any]:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     Image.Image   )

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

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__magic_name__   )

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

        # Test batched
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__magic_name__  ,     batched=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  image_processing(__magic_name__  ,     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    __A     (							self :       Any   )  ->	Tuple:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__  ,     numpify=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     np.ndarray   )

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

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__magic_name__   )

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

        # Test batched
        SCREAMING_SNAKE_CASE_          =  image_processing(__magic_name__  ,     return_tensors="pt"   ).pixel_values

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__magic_name__  ,     batched=__magic_name__   )

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







  def    __A     (							self :       Tuple   )  ->	Union[str, Any]:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__  ,     torchify=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     torch.Tensor   )

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

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__magic_name__   )

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

        # Test batched
        SCREAMING_SNAKE_CASE_          =  image_processing(__magic_name__  ,     return_tensors="pt"   ).pixel_values

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__magic_name__  ,     batched=__magic_name__   )

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







  @slow
  def    __A     (							self :       Union[str, Any]   )  ->	Optional[Any]:
        # prepare image and target
        SCREAMING_SNAKE_CASE_          =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"  ,     "r"   ) as f:
              SCREAMING_SNAKE_CASE_          =  json.loads(f.read()   )

        SCREAMING_SNAKE_CASE_          =  {"image_id": 39_769, "annotations": target}

        # encode them
        SCREAMING_SNAKE_CASE_          =  DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"   )
        SCREAMING_SNAKE_CASE_          =  image_processing(images=__magic_name__  ,     annotations=__magic_name__  ,     return_tensors="pt"   )

        # verify pixel values
        SCREAMING_SNAKE_CASE_          =  torch.Size([1, 3, 800, 1_066]   )
        self.assertEqual(encoding["pixel_values"].shape  ,     __magic_name__   )

        SCREAMING_SNAKE_CASE_          =  torch.tensor([0.2796, 0.3138, 0.3481]   )
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,     __magic_name__  ,     atol=1e-4   )   )

        # verify area
        SCREAMING_SNAKE_CASE_          =  torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]  ,     __magic_name__   )   )
        # verify boxes
        SCREAMING_SNAKE_CASE_          =  torch.Size([6, 4]   )
        self.assertEqual(encoding["labels"][0]["boxes"].shape  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,     __magic_name__  ,     atol=1e-3   )   )
        # verify image_id
        SCREAMING_SNAKE_CASE_          =  torch.tensor([39_769]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,     __magic_name__   )   )
        # verify is_crowd
        SCREAMING_SNAKE_CASE_          =  torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,     __magic_name__   )   )
        # verify class_labels
        SCREAMING_SNAKE_CASE_          =  torch.tensor([75, 75, 63, 65, 17, 17]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,     __magic_name__   )   )
        # verify orig_size
        SCREAMING_SNAKE_CASE_          =  torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,     __magic_name__   )   )
        # verify size
        SCREAMING_SNAKE_CASE_          =  torch.tensor([800, 1_066]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,     __magic_name__   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	Tuple:
        # prepare image, target and masks_path
        SCREAMING_SNAKE_CASE_          =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"  ,     "r"   ) as f:
              SCREAMING_SNAKE_CASE_          =  json.loads(f.read()   )

        SCREAMING_SNAKE_CASE_          =  {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

        # encode them
        SCREAMING_SNAKE_CASE_          =  DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"   )
        SCREAMING_SNAKE_CASE_          =  image_processing(images=__magic_name__  ,     annotations=__magic_name__  ,     masks_path=__magic_name__  ,     return_tensors="pt"   )

        # verify pixel values
        SCREAMING_SNAKE_CASE_          =  torch.Size([1, 3, 800, 1_066]   )
        self.assertEqual(encoding["pixel_values"].shape  ,     __magic_name__   )

        SCREAMING_SNAKE_CASE_          =  torch.tensor([0.2796, 0.3138, 0.3481]   )
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,     __magic_name__  ,     atol=1e-4   )   )

        # verify area
        SCREAMING_SNAKE_CASE_          =  torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]  ,     __magic_name__   )   )
        # verify boxes
        SCREAMING_SNAKE_CASE_          =  torch.Size([6, 4]   )
        self.assertEqual(encoding["labels"][0]["boxes"].shape  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,     __magic_name__  ,     atol=1e-3   )   )
        # verify image_id
        SCREAMING_SNAKE_CASE_          =  torch.tensor([39_769]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,     __magic_name__   )   )
        # verify is_crowd
        SCREAMING_SNAKE_CASE_          =  torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,     __magic_name__   )   )
        # verify class_labels
        SCREAMING_SNAKE_CASE_          =  torch.tensor([17, 17, 63, 75, 75, 93]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,     __magic_name__   )   )
        # verify masks
        SCREAMING_SNAKE_CASE_          =  822_873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()  ,     __magic_name__   )
        # verify orig_size
        SCREAMING_SNAKE_CASE_          =  torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,     __magic_name__   )   )
        # verify size
        SCREAMING_SNAKE_CASE_          =  torch.tensor([800, 1_066]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,     __magic_name__   )   )



from __future__ import annotations


def      a__ (		__UpperCamelCase				):

      # preprocessing the first row
      for i in range(1     ,      len(matrix[0]				)				):
            matrix[0][i] += matrix[0][i - 1]

      # preprocessing the first column
      for i in range(1     ,      len(__UpperCamelCase				)				):
            matrix[i][0] += matrix[i - 1][0]

      # updating the path cost for current position
      for i in range(1     ,      len(__UpperCamelCase				)				):
            for j in range(1     ,      len(matrix[0]				)				):
                  matrix[i][j] += min(matrix[i - 1][j]     ,      matrix[i][j - 1]				)

      return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()


