




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 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						def __init__(		self				:	int			,      __lowercase				:	List[Any]			,      __lowercase				:	int=7			,      __lowercase				:	Optional[int]=3			,      __lowercase				:	str=30			,      __lowercase				:	Union[str, Any]=4_00			,      __lowercase				:	Any=True			,      __lowercase				:	Tuple=None			,      __lowercase				:	Tuple=True			,      __lowercase				:	Dict=[0.5, 0.5, 0.5]			,      __lowercase				:	List[Any]=[0.5, 0.5, 0.5]			,      __lowercase				:	Any=True			,      __lowercase				:	Optional[Any]=1 / 2_55			,      __lowercase				:	Tuple=True			,      ):






							"""simple docstring"""






							snake_case_						       =	size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	num_channels
							snake_case_						       =	min_resolution
							snake_case_						       =	max_resolution
							snake_case_						       =	do_resize
							snake_case_						       =	size
							snake_case_						       =	do_normalize
							snake_case_						       =	image_mean
							snake_case_						       =	image_std
							snake_case_						       =	do_rescale
							snake_case_						       =	rescale_factor
							snake_case_						       =	do_pad






						def 			snake_case__		(		self				:	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 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	Optional[int]			,      __lowercase				:	List[str]=False		):






							"""simple docstring"""






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

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

							return expected_height, expected_width







@require_torch
@require_vision
class 						UpperCAmelCase  (							snake_case_			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  DetaImageProcessor if is_vision_available() else None






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	DetaImageProcessingTester(self		)






						@property
						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(A_			,      "image_mean"		)		)
							self.assertTrue(hasattr(A_			,      "image_std"		)		)
							self.assertTrue(hasattr(A_			,      "do_normalize"		)		)
							self.assertTrue(hasattr(A_			,      "do_resize"		)		)
							self.assertTrue(hasattr(A_			,      "do_rescale"		)		)
							self.assertTrue(hasattr(A_			,      "do_pad"		)		)
							self.assertTrue(hasattr(A_			,      "size"		)		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	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			,      A_		)






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							pass






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






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

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

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(A_		)

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

							# Test batched
							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(A_			,      batched=A_		)

							snake_case_						       =	image_processing(A_			,      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				:	Union[str, Any]		):






							"""simple docstring"""






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

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

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(A_		)

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

							# Test batched
							snake_case_						       =	image_processing(A_			,      return_tensors="pt"		).pixel_values

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(A_			,      batched=A_		)

							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				:	int		):






							"""simple docstring"""






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

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

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(A_		)

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

							# Test batched
							snake_case_						       =	image_processing(A_			,      return_tensors="pt"		).pixel_values

							snake_case_       , snake_case_						       =	self.image_processor_tester.get_expected_values(A_			,      batched=A_		)

							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				:	Any		):






							"""simple docstring"""






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

							snake_case_						       =	{"image_id": 3_97_69, "annotations": target}

							# encode them
							snake_case_						       =	DetaImageProcessor()
							snake_case_						       =	image_processing(images=A_			,      annotations=A_			,      return_tensors="pt"		)

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

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

							# verify area
							snake_case_						       =	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"]			,      A_		)		)
							# verify boxes
							snake_case_						       =	torch.Size([6, 4]		)
							self.assertEqual(encoding["labels"][0]["boxes"].shape			,      A_		)
							snake_case_						       =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,      A_			,      atol=1E-3		)		)
							# verify image_id
							snake_case_						       =	torch.tensor([3_97_69]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,      A_		)		)
							# verify is_crowd
							snake_case_						       =	torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,      A_		)		)
							# verify class_labels
							snake_case_						       =	torch.tensor([75, 75, 63, 65, 17, 17]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,      A_		)		)
							# verify orig_size
							snake_case_						       =	torch.tensor([4_80, 6_40]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,      A_		)		)
							# verify size
							snake_case_						       =	torch.tensor([8_00, 10_66]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,      A_		)		)






						@slow
						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							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:
								snake_case_						       =	json.loads(f.read()		)

							snake_case_						       =	{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

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

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

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

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

							# verify area
							snake_case_						       =	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"]			,      A_		)		)
							# verify boxes
							snake_case_						       =	torch.Size([6, 4]		)
							self.assertEqual(encoding["labels"][0]["boxes"].shape			,      A_		)
							snake_case_						       =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,      A_			,      atol=1E-3		)		)
							# verify image_id
							snake_case_						       =	torch.tensor([3_97_69]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,      A_		)		)
							# verify is_crowd
							snake_case_						       =	torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,      A_		)		)
							# verify class_labels
							snake_case_						       =	torch.tensor([17, 17, 63, 75, 75, 93]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,      A_		)		)
							# verify masks
							snake_case_						       =	82_28_73
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()			,      A_		)
							# verify orig_size
							snake_case_						       =	torch.tensor([4_80, 6_40]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,      A_		)		)
							# verify size
							snake_case_						       =	torch.tensor([8_00, 10_66]		)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,      A_		)		)


'''simple docstring'''
import copy
import os

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


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ''
											UpperCamelCase   			=     ''
											UpperCamelCase   			=     []
											UpperCamelCase   			=     0
											UpperCamelCase   			=     256
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     cva.imread(A_						,				0     )
											UpperCamelCase   			=     copy.deepcopy(self.img     )
											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     plt.hist(self.img.ravel()						,				256						,				[0, 256]						,				label='x'     )
											UpperCamelCase   			=     np.sum(A_     )
											for i in range(len(A_     )     ):
																		UpperCamelCase   			=     x[i] / self.k
																		self.sk += prk
																		UpperCamelCase   			=     (self.L - 1) * self.sk
																		if self.rem != 0:
																									UpperCamelCase   			=     int(last % last     )
																		UpperCamelCase   			=     int(last + 1 if self.rem >= 0.5 else last     )
																		self.last_list.append(A_     )
																		UpperCamelCase   			=     int(np.ma.count(self.img     ) / self.img[1].size     )
																		UpperCamelCase   			=     self.img[1].size
											for i in range(self.number_of_cols     ):
																		for j in range(self.number_of_rows     ):
																									UpperCamelCase   			=     self.img[j][i]
																									if num != self.last_list[num]:
																																UpperCamelCase   			=     self.last_list[num]
											cva.imwrite('output_data/output.jpg'						,				self.img     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											plt.hist(self.img.ravel()						,				256						,				[0, 256]     )







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											cva.imshow('Output-Image'						,				self.img     )
											cva.imshow('Input-Image'						,				self.original_image     )
											cva.waitKey(5000     )
											cva.destroyAllWindows()


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, Any]           = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
						lowerCAmelCase					:    str           = ConstantStretch()
						stretcher.stretch(file_path)
						stretcher.plot_histogram()
						stretcher.show_image()




