

lowerCAmelCase							:  Union[str, Any]            =						"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""







def       A_				(   _UpperCAmelCase   ):



						if not isinstance(_lowerCAmelCase    ,     _lowerCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	f"a bytes-like object is required, not '{data.__class__.__name__}'"
												raise TypeError(_lowerCAmelCase   )

						SCREAMING_SNAKE_CASE_:			Any  		=	"".join(bin(_lowerCAmelCase   )[2:].zfill(8   ) for byte in data   )

						SCREAMING_SNAKE_CASE_:			int  		=	len(_lowerCAmelCase   ) % 6 != 0

						if padding_needed:
												# The padding that will be added later
												SCREAMING_SNAKE_CASE_:			Any  		=	B"=" * ((6 - len(_lowerCAmelCase   ) % 6) // 2)

												# Append binary_stream with arbitrary binary digits (0's by default) to make its
												# length a multiple of 6.
												binary_stream += "0" * (6 - len(_lowerCAmelCase   ) % 6)
						else:
												SCREAMING_SNAKE_CASE_:			Any  		=	B""

						# Encode every 6 binary digits to their corresponding Base64 character
						return (
						    "".join(
						        B64_CHARSET[int(binary_stream[index : index + 6]    ,     2   )]
						        for index in range(0    ,     len(_lowerCAmelCase   )    ,     6   )   ).encode()
						    + padding
						)







def       A_				(   _UpperCAmelCase   ):



						if not isinstance(_lowerCAmelCase    ,     _lowerCAmelCase   ) and not isinstance(_lowerCAmelCase    ,     _lowerCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	(
												    "argument should be a bytes-like object or ASCII string, "
												    f"not '{encoded_data.__class__.__name__}'"
												)
												raise TypeError(_lowerCAmelCase   )

						# In case encoded_data is a bytes-like object, make sure it contains only
						# ASCII characters so we convert it to a string object
						if isinstance(_lowerCAmelCase    ,     _lowerCAmelCase   ):
												try:
																		SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	encoded_data.decode("utf-8"   )
												except UnicodeDecodeError:
																		raise ValueError("base64 encoded data should only contain ASCII characters"   )

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	encoded_data.count("="   )

						# Check if the encoded string contains non base64 characters
						if padding:
												assert all(
												    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
						else:
												assert all(
												    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

						# Check the padding
						assert len(_lowerCAmelCase   ) % 4 == 0 and padding < 3, "Incorrect padding"

						if padding:
												# Remove padding if there is one
												SCREAMING_SNAKE_CASE_:			Tuple  		=	encoded_data[:-padding]

												SCREAMING_SNAKE_CASE_:			str  		=	"".join(
												    bin(B64_CHARSET.index(_lowerCAmelCase   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
						else:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"".join(
												    bin(B64_CHARSET.index(_lowerCAmelCase   )   )[2:].zfill(6   ) for char in encoded_data   )

						SCREAMING_SNAKE_CASE_:			List[Any]  		=	[
						    int(binary_stream[index : index + 8]    ,     2   )
						    for index in range(0    ,     len(_lowerCAmelCase   )    ,     8   )
						]

						return bytes(_lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()







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   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[Any]=7       ,						lowerCAmelCase__  :					Tuple=3       ,						lowerCAmelCase__  :					Optional[int]=30       ,						lowerCAmelCase__  :					Dict=400       ,						lowerCAmelCase__  :					int=True       ,						lowerCAmelCase__  :					Dict=None       ,						lowerCAmelCase__  :					Union[str, Any]=True       ,						lowerCAmelCase__  :					Any=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					Optional[Any]=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					Tuple=1 / 255       ,						lowerCAmelCase__  :					int=True       ,						):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
									SCREAMING_SNAKE_CASE_:			str  		=	parent
									SCREAMING_SNAKE_CASE_:			Tuple  		=	batch_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	num_channels
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	min_resolution
									SCREAMING_SNAKE_CASE_:			Tuple  		=	max_resolution
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Any  		=	image_mean
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_std
									SCREAMING_SNAKE_CASE_:			Tuple  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			int  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			int  		=	do_pad



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

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

									return expected_height, expected_width







@require_torch
@require_vision
class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					DeformableDetrImageProcessor if is_vision_available() else None



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	DeformableDetrImageProcessingTester(self)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self.image_processor_tester.prepare_image_processor_dict()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"image_mean"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"image_std"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_normalize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_resize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_rescale"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_pad"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"size"))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size       ,						{"shortest_edge": 18, "longest_edge": 1333})
									self.assertEqual(image_processor.do_pad       ,						lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									pass



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

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

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

									# Test batched
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_processing(lowerCAmelCase__       ,						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 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__       ,						numpify=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						np.ndarray)

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

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

									# Test batched
									SCREAMING_SNAKE_CASE_:			Any  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE_:			int  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__       ,						torchify=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						torch.Tensor)

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__)

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

									# Test batched
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

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



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# prepare image and target
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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_:			str  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{"image_id": 3_9769, "annotations": target}

									# encode them
									SCREAMING_SNAKE_CASE_:			str  		=	DeformableDetrImageProcessor()
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						return_tensors="pt")

									# verify pixel values
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.Size([1, 3, 800, 1066])
									self.assertEqual(encoding["pixel_values"].shape       ,						lowerCAmelCase__)

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

									# verify area
									SCREAMING_SNAKE_CASE_:			int  		=	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"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			str  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			int  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.tensor([75, 75, 63, 65, 17, 17])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# prepare image, target and masks_path
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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_:			List[Any]  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

									# encode them
									SCREAMING_SNAKE_CASE_:			Any  		=	DeformableDetrImageProcessor(format="coco_panoptic")
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						masks_path=lowerCAmelCase__       ,						return_tensors="pt")

									# verify pixel values
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.Size([1, 3, 800, 1066])
									self.assertEqual(encoding["pixel_values"].shape       ,						lowerCAmelCase__)

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

									# verify area
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			Any  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor([17, 17, 63, 75, 75, 93])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify masks
									SCREAMING_SNAKE_CASE_:			Tuple  		=	82_2873
									self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,						lowerCAmelCase__)
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))






