






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 MobileNetVaImageProcessor


class        __UpperCamelCase    (     unittest.TestCase   ):







	def __init__(  self      :							Tuple       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict=7       ,				_lowerCAmelCase      :							Optional[Any]=3       ,				_lowerCAmelCase      :							Dict=18       ,				_lowerCAmelCase      :							str=30       ,				_lowerCAmelCase      :							Tuple=400       ,				_lowerCAmelCase      :							List[Any]=True       ,				_lowerCAmelCase      :							Tuple=None       ,				_lowerCAmelCase      :							Tuple=True       ,				_lowerCAmelCase      :							List[Any]=None       ,				)		-> str:
		"""simple docstring"""







		__lowercase             =  size if size is not None else {"""shortest_edge""": 20}
		__lowercase             =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  num_channels
		__lowercase             =  image_size
		__lowercase             =  min_resolution
		__lowercase             =  max_resolution
		__lowercase             =  do_resize
		__lowercase             =  size
		__lowercase             =  do_center_crop
		__lowercase             =  crop_size







	def  _a   (  self      :							Dict			)		-> List[str]:
		"""simple docstring"""







		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_center_crop": self.do_center_crop,
		    "crop_size": self.crop_size,
		}


@require_torch
@require_vision
class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:List[Any]   =       MobileNetVaImageProcessor if is_vision_available() else None







	def  _a   (  self      :							Tuple			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  MobileNetVaImageProcessingTester(self			)







	@property
	def  _a   (  self      :							Tuple			)		-> Optional[int]:
		"""simple docstring"""







		return self.image_processor_tester.prepare_image_processor_dict()







	def  _a   (  self      :							Optional[int]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		self.assertTrue(hasattr(_lowerCAmelCase       ,				"""do_resize"""			)			)
		self.assertTrue(hasattr(_lowerCAmelCase       ,				"""size"""			)			)
		self.assertTrue(hasattr(_lowerCAmelCase       ,				"""do_center_crop"""			)			)
		self.assertTrue(hasattr(_lowerCAmelCase       ,				"""crop_size"""			)			)







	def  _a   (  self      :							int			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class.from_dict(self.image_processor_dict			)
		self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 20}			)
		self.assertEqual(image_processor.crop_size       ,				{"""height""": 18, """width""": 18}			)

		__lowercase             =  self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42       ,				crop_size=84			)
		self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 42}			)
		self.assertEqual(image_processor.crop_size       ,				{"""height""": 84, """width""": 84}			)







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







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

		# Test not batched input
		__lowercase             =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)

		# Test batched
		__lowercase             =  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,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)







	def  _a   (  self      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random numpy tensors
		__lowercase             =  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
		__lowercase             =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)

		# Test batched
		__lowercase             =  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,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)







	def  _a   (  self      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random PyTorch tensors
		__lowercase             =  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
		__lowercase             =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)

		# Test batched
		__lowercase             =  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,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)









from __future__ import annotations

from scipy.special import comb  # type: ignore


class        __UpperCamelCase    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							list[tuple[float, float]]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  list_of_points
		# Degree determines the flexibility of the curve.
		# Degree = 1 will produce a straight line.
		__lowercase             =  len(_lowerCAmelCase			) - 1







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							float			)		-> list[float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."
		__lowercase             =  []
		for i in range(len(self.list_of_points			)			):
			# basis function for each i
			output_values.append(
			    comb(self.degree       ,				_lowerCAmelCase			) * ((1 - t) ** (self.degree - i)) * (t**i)			)
		# the basis must sum up to 1 for it to produce a valid Bezier curve.
		assert round(sum(_lowerCAmelCase			)       ,				5			) == 1
		return output_values







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							float			)		-> tuple[float, float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."

		__lowercase             =  self.basis_function(_lowerCAmelCase			)
		__lowercase             =  0.0
		__lowercase             =  0.0
		for i in range(len(self.list_of_points			)			):
			# For all points, sum up the product of i-th basis function and i-th point.
			x += basis_function[i] * self.list_of_points[i][0]
			y += basis_function[i] * self.list_of_points[i][1]
		return (x, y)







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							float = 0.01			)		-> Union[str, Any]:
		"""simple docstring"""







		from matplotlib import pyplot as plt  # type: ignore

		__lowercase             =  []  # x coordinates of points to plot
		__lowercase             =  []  # y coordinates of points to plot

		__lowercase             =  0.0
		while t <= 1:
			__lowercase             =  self.bezier_curve_function(_lowerCAmelCase			)
			to_plot_x.append(value[0]			)
			to_plot_y.append(value[1]			)
			t += step_size

		__lowercase             =  [i[0] for i in self.list_of_points]
		__lowercase             =  [i[1] for i in self.list_of_points]

		plt.plot(
		    _lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""blue"""       ,				label="""Curve of Degree """ + str(self.degree			)       ,				)
		plt.scatter(_lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""red"""       ,				label="""Control Points"""			)
		plt.legend()
		plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
						BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
						BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

