


"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
			import torch


SCREAMING_SNAKE_CASE :    List[Any]									=				logging.get_logger(__name__)



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=['pixel_values']



		def __init__(self				,       a_ = True				,       a_ = None				,       a_ = PILImageResampling.BILINEAR				,       a_ = True				,       a_ = None				,       a_ = True				,       a_ = 1 / 2_55				,       a_ = True				,       a_ = None				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)
			__snake_case	:						Any						=				size if size is not None else {'''shortest_edge''': 2_56}
			__snake_case	:						Tuple						=				get_size_dict(a_				,       default_to_square=a_					)
			__snake_case	:						Dict						=				crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
			__snake_case	:						Union[str, Any]						=				get_size_dict(a_				,       param_name='''crop_size'''					)
			__snake_case	:						Optional[int]						=				do_resize
			__snake_case	:						Tuple						=				size
			__snake_case	:						Optional[int]						=				resample
			__snake_case	:						str						=				do_center_crop
			__snake_case	:						str						=				crop_size
			__snake_case	:						Tuple						=				do_rescale
			__snake_case	:						Tuple						=				rescale_factor
			__snake_case	:						Dict						=				do_normalize
			__snake_case	:						List[Any]						=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__snake_case	:						Union[str, Any]						=				image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = PILImageResampling.BICUBIC				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Dict						=				get_size_dict(a_				,       default_to_square=a_					)
			if "shortest_edge" not in size:
				raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
			__snake_case	:						Tuple						=				get_resize_output_image_size(a_				,       size=size['''shortest_edge''']				,       default_to_square=a_					)
			return resize(a_				,       size=a_				,       resample=a_				,       data_format=a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				get_size_dict(a_					)
			if "height" not in size or "width" not in size:
				raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""					)
			return center_crop(a_				,       size=(size['''height'''], size['''width'''])				,       data_format=a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = None				,       **a_					):





			'''simple docstring'''

			return rescale(a_				,       scale=a_				,       data_format=a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			return normalize(a_				,       mean=a_				,       std=a_				,       data_format=a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = ChannelDimension.FIRST				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						int						=				do_resize if do_resize is not None else self.do_resize
			__snake_case	:						Optional[Any]						=				size if size is not None else self.size
			__snake_case	:						Optional[Any]						=				get_size_dict(a_				,       default_to_square=a_					)
			__snake_case	:						str						=				resample if resample is not None else self.resample
			__snake_case	:						Dict						=				do_center_crop if do_center_crop is not None else self.do_center_crop
			__snake_case	:						Optional[int]						=				crop_size if crop_size is not None else self.crop_size
			__snake_case	:						List[Any]						=				get_size_dict(a_				,       param_name='''crop_size'''					)
			__snake_case	:						int						=				do_rescale if do_rescale is not None else self.do_rescale
			__snake_case	:						Optional[int]						=				rescale_factor if rescale_factor is not None else self.rescale_factor
			__snake_case	:						List[str]						=				do_normalize if do_normalize is not None else self.do_normalize
			__snake_case	:						Dict						=				image_mean if image_mean is not None else self.image_mean
			__snake_case	:						Tuple						=				image_std if image_std is not None else self.image_std

			__snake_case	:						Dict						=				make_list_of_images(a_					)

			if not valid_images(a_					):
				raise ValueError(
				    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
				    '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

			if do_resize and size is None:
				raise ValueError('''Size must be specified if do_resize is True.'''					)

			if do_center_crop and crop_size is None:
				raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

			if do_rescale and rescale_factor is None:
				raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

			if do_normalize and (image_mean is None or image_std is None):
				raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

			# All transformations expect numpy arrays.
			__snake_case	:						List[str]						=				[to_numpy_array(a_					) for image in images]

			if do_resize:
				__snake_case	:						List[Any]						=				[self.resize(image=a_				,       size=a_				,       resample=a_					) for image in images]

			if do_center_crop:
				__snake_case	:						Dict						=				[self.center_crop(image=a_				,       size=a_					) for image in images]

			if do_rescale:
				__snake_case	:						List[Any]						=				[self.rescale(image=a_				,       scale=a_					) for image in images]

			if do_normalize:
				__snake_case	:						Any						=				[self.normalize(image=a_				,       mean=a_				,       std=a_					) for image in images]

			__snake_case	:						Tuple						=				[to_channel_dimension_format(a_				,       a_					) for image in images]

			__snake_case	:						str						=				{'''pixel_values''': images}
			return BatchFeature(data=a_				,       tensor_type=a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				outputs.logits

			# Resize logits and compute semantic segmentation maps
			if target_sizes is not None:
				if len(a_					) != len(a_					):
					raise ValueError(
					    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''					)

				if is_torch_tensor(a_					):
					__snake_case	:						Optional[Any]						=				target_sizes.numpy()

				__snake_case	:						Dict						=				[]

				for idx in range(len(a_					)					):
					__snake_case	:						Any						=				torch.nn.functional.interpolate(
					    logits[idx].unsqueeze(dim=0					)				,       size=target_sizes[idx]				,       mode='''bilinear'''				,       align_corners=a_					)
					__snake_case	:						int						=				resized_logits[0].argmax(dim=0					)
					semantic_segmentation.append(a_					)
			else:
				__snake_case	:						int						=				logits.argmax(dim=1					)
				__snake_case	:						Optional[int]						=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

			return semantic_segmentation









"""simple docstring"""
from __future__ import annotations

from scipy.special import comb  # type: ignore



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				list_of_points
			# Degree determines the flexibility of the curve.
			# Degree = 1 will produce a straight line.
			__snake_case	:						Optional[Any]						=				len(a_					) - 1



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			assert 0 <= t <= 1, "Time t must be between 0 and 1."
			__snake_case	:						list[float]						=				[]
			for i in range(len(self.list_of_points					)					):
				# basis function for each i
				output_values.append(
				    comb(self.degree				,       a_					) * ((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(a_					)				,       5					) == 1
			return output_values



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

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

			__snake_case	:						List[str]						=				self.basis_function(a_					)
			__snake_case	:						str						=				0.0
			__snake_case	:						Union[str, Any]						=				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 				SCREAMING_SNAKE_CASE  (self				,       a_ = 0.01					):





			'''simple docstring'''

			from matplotlib import pyplot as plt  # type: ignore

			__snake_case	:						list[float]						=				[]  # x coordinates of points to plot
			__snake_case	:						list[float]						=				[]  # y coordinates of points to plot

			__snake_case	:						int						=				0.0
			while t <= 1:
				__snake_case	:						Union[str, Any]						=				self.bezier_curve_function(a_					)
				to_plot_x.append(value[0]					)
				to_plot_y.append(value[1]					)
				t += step_size

			__snake_case	:						List[Any]						=				[i[0] for i in self.list_of_points]
			__snake_case	:						Any						=				[i[1] for i in self.list_of_points]

			plt.plot(
			    a_				,       a_				,       color='''blue'''				,       label='''Curve of Degree ''' + str(self.degree					)				,       )
			plt.scatter(a_				,       a_				,       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





