


"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
			import jax.numpy as jnp

if is_tf_available():
			import tensorflow as tf

if is_torch_available():
			import torch



class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				{
			    '''task_specific_params''': {
			        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_28, '''min_length''': 12, '''num_beams''': 4},
			        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_42, '''min_length''': 56, '''num_beams''': 4},
			        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
			    }
			}
			__snake_case	:						Dict						=				{
			    '''task_specific_params.summarization.length_penalty''': 1.0,
			    '''task_specific_params.summarization.max_length''': 1_28,
			    '''task_specific_params.summarization.min_length''': 12,
			    '''task_specific_params.summarization.num_beams''': 4,
			    '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
			    '''task_specific_params.summarization_cnn.max_length''': 1_42,
			    '''task_specific_params.summarization_cnn.min_length''': 56,
			    '''task_specific_params.summarization_cnn.num_beams''': 4,
			    '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
			    '''task_specific_params.summarization_xsum.max_length''': 62,
			    '''task_specific_params.summarization_xsum.min_length''': 11,
			    '''task_specific_params.summarization_xsum.num_beams''': 6,
			}

			self.assertEqual(flatten_dict(a_					)				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				np.random.randn(3				,       4					)
			self.assertTrue(np.allclose(transpose(a_					)				,       x.transpose()					)					)

			__snake_case	:						str						=				np.random.randn(3				,       4				,       5					)
			self.assertTrue(np.allclose(transpose(a_				,       axes=(1, 2, 0)					)				,       x.transpose((1, 2, 0)					)					)					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				np.random.randn(3				,       4					)
			__snake_case	:						str						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(transpose(a_					)				,       transpose(a_					).numpy()					)					)

			__snake_case	:						List[Any]						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						List[Any]						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(transpose(a_				,       axes=(1, 2, 0)					)				,       transpose(a_				,       axes=(1, 2, 0)					).numpy()					)					)



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				np.random.randn(3				,       4					)
			__snake_case	:						Optional[Any]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(transpose(a_					)				,       transpose(a_					).numpy()					)					)

			__snake_case	:						Optional[int]						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						Optional[int]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(transpose(a_				,       axes=(1, 2, 0)					)				,       transpose(a_				,       axes=(1, 2, 0)					).numpy()					)					)



		@require_flax
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				np.random.randn(3				,       4					)
			__snake_case	:						Optional[Any]						=				jnp.array(a_					)
			self.assertTrue(np.allclose(transpose(a_					)				,       np.asarray(transpose(a_					)					)					)					)

			__snake_case	:						Any						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						Tuple						=				jnp.array(a_					)
			self.assertTrue(np.allclose(transpose(a_				,       axes=(1, 2, 0)					)				,       np.asarray(transpose(a_				,       axes=(1, 2, 0)					)					)					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				np.random.randn(3				,       4					)
			self.assertTrue(np.allclose(reshape(a_				,       (4, 3)					)				,       np.reshape(a_				,       (4, 3)					)					)					)

			__snake_case	:						Dict						=				np.random.randn(3				,       4				,       5					)
			self.assertTrue(np.allclose(reshape(a_				,       (12, 5)					)				,       np.reshape(a_				,       (12, 5)					)					)					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				np.random.randn(3				,       4					)
			__snake_case	:						int						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (4, 3)					)				,       reshape(a_				,       (4, 3)					).numpy()					)					)

			__snake_case	:						Optional[int]						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						Optional[Any]						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (12, 5)					)				,       reshape(a_				,       (12, 5)					).numpy()					)					)



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				np.random.randn(3				,       4					)
			__snake_case	:						Dict						=				tf.constant(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (4, 3)					)				,       reshape(a_				,       (4, 3)					).numpy()					)					)

			__snake_case	:						Optional[Any]						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						List[Any]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (12, 5)					)				,       reshape(a_				,       (12, 5)					).numpy()					)					)



		@require_flax
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				np.random.randn(3				,       4					)
			__snake_case	:						Any						=				jnp.array(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (4, 3)					)				,       np.asarray(reshape(a_				,       (4, 3)					)					)					)					)

			__snake_case	:						int						=				np.random.randn(3				,       4				,       5					)
			__snake_case	:						Dict						=				jnp.array(a_					)
			self.assertTrue(np.allclose(reshape(a_				,       (12, 5)					)				,       np.asarray(reshape(a_				,       (12, 5)					)					)					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				np.random.randn(1				,       3				,       4					)
			self.assertTrue(np.allclose(squeeze(a_					)				,       np.squeeze(a_					)					)					)

			__snake_case	:						List[str]						=				np.random.randn(1				,       4				,       1				,       5					)
			self.assertTrue(np.allclose(squeeze(a_				,       axis=2					)				,       np.squeeze(a_				,       axis=2					)					)					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				np.random.randn(1				,       3				,       4					)
			__snake_case	:						Any						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(squeeze(a_					)				,       squeeze(a_					).numpy()					)					)

			__snake_case	:						Optional[Any]						=				np.random.randn(1				,       4				,       1				,       5					)
			__snake_case	:						Optional[int]						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(squeeze(a_				,       axis=2					)				,       squeeze(a_				,       axis=2					).numpy()					)					)



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				np.random.randn(1				,       3				,       4					)
			__snake_case	:						Optional[int]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(squeeze(a_					)				,       squeeze(a_					).numpy()					)					)

			__snake_case	:						List[Any]						=				np.random.randn(1				,       4				,       1				,       5					)
			__snake_case	:						Dict						=				tf.constant(a_					)
			self.assertTrue(np.allclose(squeeze(a_				,       axis=2					)				,       squeeze(a_				,       axis=2					).numpy()					)					)



		@require_flax
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				np.random.randn(1				,       3				,       4					)
			__snake_case	:						Union[str, Any]						=				jnp.array(a_					)
			self.assertTrue(np.allclose(squeeze(a_					)				,       np.asarray(squeeze(a_					)					)					)					)

			__snake_case	:						Union[str, Any]						=				np.random.randn(1				,       4				,       1				,       5					)
			__snake_case	:						Optional[Any]						=				jnp.array(a_					)
			self.assertTrue(np.allclose(squeeze(a_				,       axis=2					)				,       np.asarray(squeeze(a_				,       axis=2					)					)					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				np.random.randn(3				,       4					)
			self.assertTrue(np.allclose(expand_dims(a_				,       axis=1					)				,       np.expand_dims(a_				,       axis=1					)					)					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				np.random.randn(3				,       4					)
			__snake_case	:						Tuple						=				torch.tensor(a_					)
			self.assertTrue(np.allclose(expand_dims(a_				,       axis=1					)				,       expand_dims(a_				,       axis=1					).numpy()					)					)



		@require_tf
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				np.random.randn(3				,       4					)
			__snake_case	:						Optional[int]						=				tf.constant(a_					)
			self.assertTrue(np.allclose(expand_dims(a_				,       axis=1					)				,       expand_dims(a_				,       axis=1					).numpy()					)					)



		@require_flax
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				np.random.randn(3				,       4					)
			__snake_case	:						int						=				jnp.array(a_					)
			self.assertTrue(np.allclose(expand_dims(a_				,       axis=1					)				,       np.asarray(expand_dims(a_				,       axis=1					)					)					)					)









"""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





