'''simple docstring'''




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 DeformableDetrImageProcessor






class 		__A       (unittest.TestCase   ):

       '''simple docstring'''





       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_=7				,     UpperCamelCase_=3				,     UpperCamelCase_=30				,     UpperCamelCase_=4_00				,     UpperCamelCase_=True				,     UpperCamelCase_=None				,     UpperCamelCase_=True				,     UpperCamelCase_=[0.5, 0.5, 0.5]				,     UpperCamelCase_=[0.5, 0.5, 0.5]				,     UpperCamelCase_=True				,     UpperCamelCase_=1 / 2_55				,     UpperCamelCase_=True				,     ):
              # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
              __UpperCAmelCase  :				Optional[Any]       				=		size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
              __UpperCAmelCase  :				Optional[Any]       				=		parent
              __UpperCAmelCase  :				Tuple       				=		batch_size
              __UpperCAmelCase  :				List[Any]       				=		num_channels
              __UpperCAmelCase  :				Union[str, Any]       				=		min_resolution
              __UpperCAmelCase  :				str       				=		max_resolution
              __UpperCAmelCase  :				Optional[int]       				=		do_resize
              __UpperCAmelCase  :				Any       				=		size
              __UpperCAmelCase  :				str       				=		do_normalize
              __UpperCAmelCase  :				Tuple       				=		image_mean
              __UpperCAmelCase  :				Optional[int]       				=		image_std
              __UpperCAmelCase  :				Any       				=		do_rescale
              __UpperCAmelCase  :				Union[str, Any]       				=		rescale_factor
              __UpperCAmelCase  :				Tuple       				=		do_pad






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

              else:
                     __UpperCAmelCase  :				Optional[int]       				=		[]
                     for image in image_inputs:
                            __UpperCAmelCase  :				Optional[Any]       				=		self.get_expected_values([image]      )
                            expected_values.append((expected_height, expected_width)      )
                     __UpperCAmelCase  :				Optional[Any]       				=		max(UpperCamelCase_				,     key=lambda UpperCamelCase_      : item[0]      )[0]
                     __UpperCAmelCase  :				int       				=		max(UpperCamelCase_				,     key=lambda UpperCamelCase_      : item[1]      )[1]

              return expected_height, expected_width






@require_torch
@require_vision
class 		__A       (__magic_name__ ,					unittest.TestCase   ):

       '''simple docstring'''





       snake_case       :List[Any]				    =					DeformableDetrImageProcessor if is_vision_available() else None






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		DeformableDetrImageProcessingTester(self      )






       @property
       def 			_snake_case						(      self      ):
              return self.image_processor_tester.prepare_image_processor_dict()






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		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 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		self.image_processing_class.from_dict(self.image_processor_dict      )
              self.assertEqual(image_processor.size				,     {"shortest_edge": 18, "longest_edge": 13_33}      )
              self.assertEqual(image_processor.do_pad				,     UpperCamelCase_      )

              __UpperCAmelCase  :				Optional[Any]       				=		self.image_processing_class.from_dict(
                  self.image_processor_dict				,     size=42				,     max_size=84				,     pad_and_return_pixel_mask=UpperCamelCase_      )
              self.assertEqual(image_processor.size				,     {"shortest_edge": 42, "longest_edge": 84}      )
              self.assertEqual(image_processor.do_pad				,     UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              pass






       def 			_snake_case						(      self      ):
              # Initialize image_processing
              __UpperCAmelCase  :				Optional[int]       				=		self.image_processing_class(**self.image_processor_dict      )
              # create random PIL images
              __UpperCAmelCase  :				Dict       				=		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  :				Dict       				=		image_processing(image_inputs[0]				,     return_tensors="pt"      ).pixel_values

              __UpperCAmelCase  :				List[str]       				=		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  :				Optional[Any]       				=		self.image_processor_tester.get_expected_values(UpperCamelCase_				,     batched=UpperCamelCase_      )

              __UpperCAmelCase  :				Union[str, Any]       				=		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 			_snake_case						(      self      ):
              # Initialize image_processing
              __UpperCAmelCase  :				int       				=		self.image_processing_class(**self.image_processor_dict      )
              # create random numpy tensors
              __UpperCAmelCase  :				int       				=		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  :				List[Any]       				=		image_processing(image_inputs[0]				,     return_tensors="pt"      ).pixel_values

              __UpperCAmelCase  :				Union[str, Any]       				=		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  :				List[Any]       				=		image_processing(UpperCamelCase_				,     return_tensors="pt"      ).pixel_values

              __UpperCAmelCase  :				Any       				=		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 			_snake_case						(      self      ):
              # Initialize image_processing
              __UpperCAmelCase  :				Tuple       				=		self.image_processing_class(**self.image_processor_dict      )
              # create random PyTorch tensors
              __UpperCAmelCase  :				Union[str, Any]       				=		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  :				Optional[Any]       				=		image_processing(image_inputs[0]				,     return_tensors="pt"      ).pixel_values

              __UpperCAmelCase  :				List[Any]       				=		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  :				Union[str, Any]       				=		image_processing(UpperCamelCase_				,     return_tensors="pt"      ).pixel_values

              __UpperCAmelCase  :				Dict       				=		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 			_snake_case						(      self      ):
              # prepare image and target
              __UpperCAmelCase  :				Union[str, Any]       				=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
              with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,     "r"      ) as f:
                     __UpperCAmelCase  :				Union[str, Any]       				=		json.loads(f.read()      )

              __UpperCAmelCase  :				Optional[Any]       				=		{"image_id": 3_97_69, "annotations": target}

              # encode them
              __UpperCAmelCase  :				List[str]       				=		DeformableDetrImageProcessor()
              __UpperCAmelCase  :				Union[str, Any]       				=		image_processing(images=UpperCamelCase_				,     annotations=UpperCamelCase_				,     return_tensors="pt"      )

              # verify pixel values
              __UpperCAmelCase  :				List[Any]       				=		torch.Size([1, 3, 8_00, 10_66]      )
              self.assertEqual(encoding["pixel_values"].shape				,     UpperCamelCase_      )

              __UpperCAmelCase  :				List[Any]       				=		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
              self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,     UpperCamelCase_				,     atol=1E-4      )      )

              # verify area
              __UpperCAmelCase  :				List[Any]       				=		torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     UpperCamelCase_      )      )
              # verify boxes
              __UpperCAmelCase  :				Optional[int]       				=		torch.Size([6, 4]      )
              self.assertEqual(encoding["labels"][0]["boxes"].shape				,     UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,     UpperCamelCase_				,     atol=1E-3      )      )
              # verify image_id
              __UpperCAmelCase  :				str       				=		torch.tensor([3_97_69]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     UpperCamelCase_      )      )
              # verify is_crowd
              __UpperCAmelCase  :				Tuple       				=		torch.tensor([0, 0, 0, 0, 0, 0]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     UpperCamelCase_      )      )
              # verify class_labels
              __UpperCAmelCase  :				Any       				=		torch.tensor([75, 75, 63, 65, 17, 17]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     UpperCamelCase_      )      )
              # verify orig_size
              __UpperCAmelCase  :				Union[str, Any]       				=		torch.tensor([4_80, 6_40]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     UpperCamelCase_      )      )
              # verify size
              __UpperCAmelCase  :				List[str]       				=		torch.tensor([8_00, 10_66]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     UpperCamelCase_      )      )







       @slow
       def 			_snake_case						(      self      ):
              # prepare image, target and masks_path
              __UpperCAmelCase  :				Dict       				=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
              with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"				,     "r"      ) as f:
                     __UpperCAmelCase  :				Optional[Any]       				=		json.loads(f.read()      )

              __UpperCAmelCase  :				Optional[int]       				=		{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

              __UpperCAmelCase  :				Tuple       				=		pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"      )

              # encode them
              __UpperCAmelCase  :				int       				=		DeformableDetrImageProcessor(format="coco_panoptic"      )
              __UpperCAmelCase  :				Optional[int]       				=		image_processing(images=UpperCamelCase_				,     annotations=UpperCamelCase_				,     masks_path=UpperCamelCase_				,     return_tensors="pt"      )

              # verify pixel values
              __UpperCAmelCase  :				str       				=		torch.Size([1, 3, 8_00, 10_66]      )
              self.assertEqual(encoding["pixel_values"].shape				,     UpperCamelCase_      )

              __UpperCAmelCase  :				int       				=		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
              self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,     UpperCamelCase_				,     atol=1E-4      )      )

              # verify area
              __UpperCAmelCase  :				str       				=		torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     UpperCamelCase_      )      )
              # verify boxes
              __UpperCAmelCase  :				Any       				=		torch.Size([6, 4]      )
              self.assertEqual(encoding["labels"][0]["boxes"].shape				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,     UpperCamelCase_				,     atol=1E-3      )      )
              # verify image_id
              __UpperCAmelCase  :				List[str]       				=		torch.tensor([3_97_69]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     UpperCamelCase_      )      )
              # verify is_crowd
              __UpperCAmelCase  :				Union[str, Any]       				=		torch.tensor([0, 0, 0, 0, 0, 0]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     UpperCamelCase_      )      )
              # verify class_labels
              __UpperCAmelCase  :				Any       				=		torch.tensor([17, 17, 63, 75, 75, 93]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     UpperCamelCase_      )      )
              # verify masks
              __UpperCAmelCase  :				str       				=		82_28_73
              self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,     UpperCamelCase_      )
              # verify orig_size
              __UpperCAmelCase  :				str       				=		torch.tensor([4_80, 6_40]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     UpperCamelCase_      )      )
              # verify size
              __UpperCAmelCase  :				int       				=		torch.tensor([8_00, 10_66]      )
              self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     UpperCamelCase_      )      )







'''simple docstring'''




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       list[float]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase  :				str       				=		f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if colsa != 1:
              __UpperCAmelCase  :				Optional[Any]       				=		f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if rowsa != rowsa:
              __UpperCAmelCase  :				Optional[int]       				=		(
                  "Coefficient and constant matrices dimensions must be nxn and nx1 but "
                  f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if len(lowerCamelCase__						) != rowsa:
              __UpperCAmelCase  :				List[str]       				=		(
                  "Number of initial values must be equal to number of rows in coefficient "
                  f"""matrix but received {len(lowerCamelCase__						)} and {rowsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if iterations <= 0:
              raise ValueError("Iterations must be at least 1"						)

       __UpperCAmelCase  :				NDArray[floataa]       				=		np.concatenate(
           (coefficient_matrix, constant_matrix)							,    axis=1						)

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		table.shape

       strictly_diagonally_dominant(lowerCamelCase__						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				int       				=		[]
              for row in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				List[str]       				=		0
                     for col in range(lowerCamelCase__						):
                            if col == row:
                                   __UpperCAmelCase  :				int       				=		table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase  :				Any       				=		table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase  :				List[Any]       				=		(temp + val) / denom
                     new_val.append(lowerCamelCase__						)
              __UpperCAmelCase  :				str       				=		new_val

       return [float(lowerCamelCase__						) for i in new_val]

def _lowercase	(      lowerCamelCase__						)					->       bool:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		table.shape

       __UpperCAmelCase  :				str       				=		True

       for i in range(0							,    lowerCamelCase__						):
              __UpperCAmelCase  :				Union[str, Any]       				=		0
              for j in range(0							,    cols - 1						):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("Coefficient matrix is not strictly diagonally dominant"						)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






