
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 ConditionalDetrImageProcessor

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def __init__(       self :       List[Any]						,	snake_case_ :       Dict						,	snake_case_ :       List[Any]=7						,	snake_case_ :       str=3						,	snake_case_ :       Optional[Any]=30						,	snake_case_ :       Dict=400						,	snake_case_ :       Any=True						,	snake_case_ :       List[str]=None						,	snake_case_ :       Union[str, Any]=True						,	snake_case_ :       List[Any]=[0.5, 0.5, 0.5]						,	snake_case_ :       List[str]=[0.5, 0.5, 0.5]						,	snake_case_ :       List[Any]=True						,	snake_case_ :       Tuple=1 / 255						,	snake_case_ :       Any=True						,	):
							UpperCamelCase_:							Optional[int]	=						size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
							UpperCamelCase_:							Tuple	=						parent
							UpperCamelCase_:							Union[str, Any]	=						batch_size
							UpperCamelCase_:							str	=						num_channels
							UpperCamelCase_:							Union[str, Any]	=						min_resolution
							UpperCamelCase_:							List[Any]	=						max_resolution
							UpperCamelCase_:							List[Any]	=						do_resize
							UpperCamelCase_:							List[Any]	=						size
							UpperCamelCase_:							Optional[int]	=						do_normalize
							UpperCamelCase_:							Dict	=						image_mean
							UpperCamelCase_:							List[str]	=						image_std
							UpperCamelCase_:							Optional[Any]	=						do_rescale
							UpperCamelCase_:							Optional[Any]	=						rescale_factor
							UpperCamelCase_:							Optional[int]	=						do_pad

			def       lowerCAmelCase__	(       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       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       Union[str, Any]=False      ):




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

							else:
											UpperCamelCase_:							Dict	=						[]
											for image in image_inputs:
															UpperCamelCase_:							Tuple	=						self.get_expected_values([image]      )
															expected_values.append((expected_height, expected_width)      )
											UpperCamelCase_:							Union[str, Any]	=						max(lowerCamelCase_						,	key=lambda snake_case_      : item[0]      )[0]
											UpperCamelCase_:							List[str]	=						max(lowerCamelCase_						,	key=lambda snake_case_      : item[1]      )[1]

							return expected_height, expected_width




@require_torch
@require_vision
class        _UpperCamelCase				(					_UpperCAmelCase     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = ConditionalDetrImageProcessor if is_vision_available() else None

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							List[str]	=						ConditionalDetrImageProcessingTester(self      )

			@property
			def       lowerCAmelCase__	(       self :       Dict      ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Union[str, Any]	=						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_						,	"""size"""      )      )

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							str	=						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_      )

							UpperCamelCase_:							List[Any]	=						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       lowerCAmelCase__	(       self :       Optional[Any]      ):
							pass

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							int	=						self.image_processing_class(**self.image_processor_dict      )
							# create random PIL images
							UpperCamelCase_:							Optional[int]	=						prepare_image_inputs(self.image_processor_tester						,	equal_resolution=lowerCamelCase_      )
							for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_						,	Image.Image      )

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

							UpperCamelCase_:							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
							UpperCamelCase_:							Tuple	=						self.image_processor_tester.get_expected_values(lowerCamelCase_						,	batched=lowerCamelCase_      )

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

							UpperCamelCase_:							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
							UpperCamelCase_:							int	=						image_processing(lowerCamelCase_						,	return_tensors="""pt"""      ).pixel_values

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

							UpperCamelCase_:							Union[str, 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
							UpperCamelCase_:							str	=						image_processing(lowerCamelCase_						,	return_tensors="""pt"""      ).pixel_values

							UpperCamelCase_:							Optional[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,
							    )						,	)

			@slow
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Dict	=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""						,	"""r"""      ) as f:
											UpperCamelCase_:							Optional[int]	=						json.loads(f.read()      )

							UpperCamelCase_:							int	=						{'''image_id''': 3_9769, '''annotations''': target}

							# encode them
							UpperCamelCase_:							Any	=						ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""      )
							UpperCamelCase_:							List[str]	=						image_processing(images=lowerCamelCase_						,	annotations=lowerCamelCase_						,	return_tensors="""pt"""      )

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

							UpperCamelCase_:							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
							UpperCamelCase_:							Dict	=						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
							UpperCamelCase_:							List[Any]	=						torch.Size([6, 4]      )
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,	lowerCamelCase_      )
							UpperCamelCase_:							Any	=						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
							UpperCamelCase_:							Dict	=						torch.tensor([3_9769]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,	lowerCamelCase_      )      )
							# verify is_crowd
							UpperCamelCase_:							Optional[Any]	=						torch.tensor([0, 0, 0, 0, 0, 0]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,	lowerCamelCase_      )      )
							# verify class_labels
							UpperCamelCase_:							List[Any]	=						torch.tensor([75, 75, 63, 65, 17, 17]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,	lowerCamelCase_      )      )
							# verify orig_size
							UpperCamelCase_:							int	=						torch.tensor([480, 640]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,	lowerCamelCase_      )      )
							# verify size
							UpperCamelCase_:							List[str]	=						torch.tensor([800, 1066]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,	lowerCamelCase_      )      )




			@slow
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							str	=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""						,	"""r"""      ) as f:
											UpperCamelCase_:							List[Any]	=						json.loads(f.read()      )

							UpperCamelCase_:							List[str]	=						{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

							UpperCamelCase_:							List[str]	=						pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""      )

							# encode them
							UpperCamelCase_:							int	=						ConditionalDetrImageProcessor(format="""coco_panoptic"""      )
							UpperCamelCase_:							str	=						image_processing(images=lowerCamelCase_						,	annotations=lowerCamelCase_						,	masks_path=lowerCamelCase_						,	return_tensors="""pt"""      )

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

							UpperCamelCase_:							Dict	=						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
							UpperCamelCase_:							Tuple	=						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
							UpperCamelCase_:							Optional[int]	=						torch.Size([6, 4]      )
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,	lowerCamelCase_      )
							UpperCamelCase_:							Optional[int]	=						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
							UpperCamelCase_:							Union[str, Any]	=						torch.tensor([3_9769]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,	lowerCamelCase_      )      )
							# verify is_crowd
							UpperCamelCase_:							Optional[Any]	=						torch.tensor([0, 0, 0, 0, 0, 0]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,	lowerCamelCase_      )      )
							# verify class_labels
							UpperCamelCase_:							Dict	=						torch.tensor([17, 17, 63, 75, 75, 93]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,	lowerCamelCase_      )      )
							# verify masks
							UpperCamelCase_:							Union[str, Any]	=						82_2873
							self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()						,	lowerCamelCase_      )
							# verify orig_size
							UpperCamelCase_:							List[Any]	=						torch.tensor([480, 640]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,	lowerCamelCase_      )      )
							# verify size
							UpperCamelCase_:							List[str]	=						torch.tensor([800, 1066]      )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,	lowerCamelCase_      )      )






from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = "x"     ,    lowerCamelCase = 10**-10     ,    lowerCamelCase = 1     ,    )  ->	complex:
				UpperCamelCase_:							Optional[Any]	=						symbols(lowerCamelCase						)
				UpperCamelCase_:							int	=						lambdify(lowerCamelCase     ,    lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						lambdify(lowerCamelCase     ,    diff(lowerCamelCase     ,    lowerCamelCase						)						)

				UpperCamelCase_:							Tuple	=						starting_point

				while True:
								if diff_function(lowerCamelCase						) != 0:
												UpperCamelCase_:							List[Any]	=						prev_guess - multiplicity * func(lowerCamelCase						) / diff_function(
												    lowerCamelCase						)
								else:
												raise ZeroDivisionError("""Could not find root"""						) from None

								# Precision is checked by comparing the difference of consecutive guesses
								if abs(next_guess - prev_guess						) < precision:
												return next_guess

								UpperCamelCase_:							Any	=						next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

							# Find value of e
							print(
							    """The root of log(y) - 1 = 0 is """,
							    F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
							)

							# Exponential Roots
							print(
							    """The root of exp(x) - 1 = 0 is""",
							    F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")






