

"""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 DetaImageProcessor





class    snake_case     (    unittest.TestCase  ):

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



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						__lowerCAmelCase:	List[Any]   				=      parent
						__lowerCAmelCase:	Dict   				=      batch_size
						__lowerCAmelCase:	Optional[Any]   				=      num_channels
						__lowerCAmelCase:	Union[str, Any]   				=      min_resolution
						__lowerCAmelCase:	Dict   				=      max_resolution
						__lowerCAmelCase:	Dict   				=      do_resize
						__lowerCAmelCase:	List[str]   				=      size
						__lowerCAmelCase:	Dict   				=      do_normalize
						__lowerCAmelCase:	Any   				=      image_mean
						__lowerCAmelCase:	Tuple   				=      image_std
						__lowerCAmelCase:	int   				=      do_rescale
						__lowerCAmelCase:	List[Any]   				=      rescale_factor
						__lowerCAmelCase:	Any   				=      do_pad

				def 							lowercase_					(     self							:    List[str])->       List[str]:



						'''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							:    Dict					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Any=False)->       Tuple:



						'''simple docstring'''


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

						else:
								__lowerCAmelCase:	List[str]   				=      []
								for image in image_inputs:
										__lowerCAmelCase:	List[Any]   				=      self.get_expected_values([image])
										expected_values.append((expected_height, expected_width))
								__lowerCAmelCase:	Tuple   				=      max(snake_case__					,  key=lambda UpperCamelCase__: item[0])[0]
								__lowerCAmelCase:	Tuple   				=      max(snake_case__					,  key=lambda UpperCamelCase__: item[1])[1]

						return expected_height, expected_width





@require_torch
@require_vision
class    snake_case     (    A_,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  DetaImageProcessor if is_vision_available() else None

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      DetaImageProcessingTester(self)

				@property
				def 							lowercase_					(     self							:    List[str])->       int:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    Optional[int])->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      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__					,  "do_rescale"))
						self.assertTrue(hasattr(snake_case__					,  "do_pad"))
						self.assertTrue(hasattr(snake_case__					,  "size"))

				def 							lowercase_					(     self							:    str)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      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					,  snake_case__)

				def 							lowercase_					(     self							:    int)->       Optional[int]:



						'''simple docstring'''


						pass

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	Optional[Any]   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=snake_case__)
						for image in image_inputs:
								self.assertIsInstance(snake_case__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values

						__lowerCAmelCase:	str   				=      self.image_processor_tester.get_expected_values(snake_case__)

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

						# Test batched
						__lowerCAmelCase:	Dict   				=      self.image_processor_tester.get_expected_values(snake_case__					,  batched=snake_case__)

						__lowerCAmelCase:	int   				=      image_processing(snake_case__					,  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							:    List[Any])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	Optional[int]   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=snake_case__					,  numpify=snake_case__)
						for image in image_inputs:
								self.assertIsInstance(snake_case__					,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:	Optional[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values

						__lowerCAmelCase:	Optional[Any]   				=      self.image_processor_tester.get_expected_values(snake_case__)

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

						# Test batched
						__lowerCAmelCase:	Tuple   				=      image_processing(snake_case__					,  return_tensors="pt").pixel_values

						__lowerCAmelCase:	Optional[int]   				=      self.image_processor_tester.get_expected_values(snake_case__					,  batched=snake_case__)

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

				def 							lowercase_					(     self							:    str)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	str   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=snake_case__					,  torchify=snake_case__)
						for image in image_inputs:
								self.assertIsInstance(snake_case__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	Optional[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values

						__lowerCAmelCase:	List[str]   				=      self.image_processor_tester.get_expected_values(snake_case__)

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

						# Test batched
						__lowerCAmelCase:	List[Any]   				=      image_processing(snake_case__					,  return_tensors="pt").pixel_values

						__lowerCAmelCase:	str   				=      self.image_processor_tester.get_expected_values(snake_case__					,  batched=snake_case__)

						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							:    List[Any])->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,  "r") as f:
								__lowerCAmelCase:	Tuple   				=      json.loads(f.read())

						__lowerCAmelCase:	int   				=      {"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						__lowerCAmelCase:	Union[str, Any]   				=      DetaImageProcessor()
						__lowerCAmelCase:	Optional[int]   				=      image_processing(images=snake_case__					,  annotations=snake_case__					,  return_tensors="pt")

						# verify pixel values
						__lowerCAmelCase:	Union[str, Any]   				=      torch.Size([1, 3, 8_0_0, 1_0_6_6])
						self.assertEqual(encoding["pixel_values"].shape					,  snake_case__)

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

						# verify area
						__lowerCAmelCase:	List[str]   				=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,  snake_case__))
						# verify boxes
						__lowerCAmelCase:	Any   				=      torch.Size([6, 4])
						self.assertEqual(encoding["labels"][0]["boxes"].shape					,  snake_case__)
						__lowerCAmelCase:	Tuple   				=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,  snake_case__					,  atol=1e-3))
						# verify image_id
						__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor([3_9_7_6_9])
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,  snake_case__))
						# verify is_crowd
						__lowerCAmelCase:	Optional[Any]   				=      torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,  snake_case__))
						# verify class_labels
						__lowerCAmelCase:	int   				=      torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,  snake_case__))
						# verify orig_size
						__lowerCAmelCase:	Dict   				=      torch.tensor([4_8_0, 6_4_0])
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,  snake_case__))
						# verify size
						__lowerCAmelCase:	Dict   				=      torch.tensor([8_0_0, 1_0_6_6])
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,  snake_case__))




				@slow
				def 							lowercase_					(     self							:    Optional[int])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,  "r") as f:
								__lowerCAmelCase:	List[Any]   				=      json.loads(f.read())

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

						__lowerCAmelCase:	Any   				=      pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

						# encode them
						__lowerCAmelCase:	Tuple   				=      DetaImageProcessor(format="coco_panoptic")
						__lowerCAmelCase:	int   				=      image_processing(images=snake_case__					,  annotations=snake_case__					,  masks_path=snake_case__					,  return_tensors="pt")

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

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

						# verify area
						__lowerCAmelCase:	str   				=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,  snake_case__))
						# verify boxes
						__lowerCAmelCase:	Any   				=      torch.Size([6, 4])
						self.assertEqual(encoding["labels"][0]["boxes"].shape					,  snake_case__)
						__lowerCAmelCase:	List[Any]   				=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,  snake_case__					,  atol=1e-3))
						# verify image_id
						__lowerCAmelCase:	List[Any]   				=      torch.tensor([3_9_7_6_9])
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,  snake_case__))
						# verify is_crowd
						__lowerCAmelCase:	int   				=      torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,  snake_case__))
						# verify class_labels
						__lowerCAmelCase:	List[Any]   				=      torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,  snake_case__))
						# verify masks
						__lowerCAmelCase:	List[str]   				=      8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,  snake_case__)
						# verify orig_size
						__lowerCAmelCase:	Optional[int]   				=      torch.tensor([4_8_0, 6_4_0])
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,  snake_case__))
						# verify size
						__lowerCAmelCase:	int   				=      torch.tensor([8_0_0, 1_0_6_6])
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,  snake_case__))





"""simple docstring"""


from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


if __name__ == "__main__":
			# read original image
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			__A						       =      plt.figure(1)
			__A						       =      ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
						plt.title(titles[i])
						plt.axis("off")
						plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
			plt.show()


