


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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor

class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=7      , __snake_case=3      , __snake_case=1_8      , __snake_case=3_0      , __snake_case=4_0_0      , __snake_case=True      , __snake_case=None      , __snake_case=True      , __snake_case=None      , __snake_case=True      , __snake_case=[0.4814_5466, 0.457_8275, 0.4082_1073]      , __snake_case=[0.2686_2954, 0.2613_0258, 0.2757_7711]      , __snake_case=True      , ):
         snake_case					=    size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         snake_case					=    crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
         snake_case					=    parent
         snake_case					=    batch_size
         snake_case					=    num_channels
         snake_case					=    image_size
         snake_case					=    min_resolution
         snake_case					=    max_resolution
         snake_case					=    do_resize
         snake_case					=    size
         snake_case					=    do_center_crop
         snake_case					=    crop_size
         snake_case					=    do_normalize
         snake_case					=    image_mean
         snake_case					=    image_std
         snake_case					=    do_convert_rgb
    def 		a_      (	self   ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_convert_rgb": self.do_convert_rgb,
         }




    def 		a_      (	self      , __snake_case=False      , __snake_case=False      , __snake_case=False   ):
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
              snake_case					=    []
              for i in range(self.batch_size   ):
                   image_inputs.append(
                       np.random.randint(
                           2_5_5      , size=(self.num_channels, self.max_resolution, self.max_resolution)      , dtype=np.uinta   )   )
         else:
              snake_case					=    []
              for i in range(self.batch_size   ):
                   snake_case			,  snake_case					=    np.random.choice(np.arange(self.min_resolution      , self.max_resolution   )      , 2   )
                   image_inputs.append(np.random.randint(2_5_5      , size=(self.num_channels, width, height)      , dtype=np.uinta   )   )

         if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              snake_case					=    [Image.fromarray(np.moveaxis(__snake_case      , 0      , -1   )   ) for x in image_inputs]

         if torchify:
              snake_case					=    [torch.from_numpy(__snake_case   ) for x in image_inputs]

         return image_inputs






@require_torch
@require_vision
class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		ChineseCLIPImageProcessor if is_vision_available() else None
    def 		a_      (	self   ):
         snake_case					=    ChineseCLIPImageProcessingTester(self      , do_center_crop=__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.image_processor_tester.prepare_image_processor_dict()
    def 		a_      (	self   ):
         snake_case					=    self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(__snake_case      , '''do_resize'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''size'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''do_center_crop'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''center_crop'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''do_normalize'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''image_mean'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''image_std'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''do_convert_rgb'''   )   )
    def 		a_      (	self   ):
         snake_case					=    self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size      , {'''height''': 2_2_4, '''width''': 2_2_4}   )
         self.assertEqual(image_processor.crop_size      , {'''height''': 1_8, '''width''': 1_8}   )

         snake_case					=    self.image_processing_class.from_dict(self.image_processor_dict      , size=4_2      , crop_size=8_4   )
         self.assertEqual(image_processor.size      , {'''shortest_edge''': 4_2}   )
         self.assertEqual(image_processor.crop_size      , {'''height''': 8_4, '''width''': 8_4}   )
    def 		a_      (	self   ):
         pass
    def 		a_      (	self   ):
         # Initialize image_processing
         snake_case					=    self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         snake_case					=    self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case   )
         for image in image_inputs:
              self.assertIsInstance(__snake_case      , Image.Image   )

         # Test not batched input
         snake_case					=    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
         snake_case					=    image_processing(__snake_case      , 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   ):
         # Initialize image_processing
         snake_case					=    self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         snake_case					=    self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case      , numpify=__snake_case   )
         for image in image_inputs:
              self.assertIsInstance(__snake_case      , np.ndarray   )

         # Test not batched input
         snake_case					=    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
         snake_case					=    image_processing(__snake_case      , 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   ):
         # Initialize image_processing
         snake_case					=    self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         snake_case					=    self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case      , torchify=__snake_case   )
         for image in image_inputs:
              self.assertIsInstance(__snake_case      , torch.Tensor   )

         # Test not batched input
         snake_case					=    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
         snake_case					=    image_processing(__snake_case      , 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'''],
             )      , )






@require_torch
@require_vision
class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		ChineseCLIPImageProcessor if is_vision_available() else None
    def 		a_      (	self   ):
         snake_case					=    ChineseCLIPImageProcessingTester(self      , num_channels=4      , do_center_crop=__snake_case   )
         snake_case					=    3
    @property
    def 		a_      (	self   ):
         return self.image_processor_tester.prepare_image_processor_dict()
    def 		a_      (	self   ):
         snake_case					=    self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(__snake_case      , '''do_resize'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''size'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''do_center_crop'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''center_crop'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''do_normalize'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''image_mean'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''image_std'''   )   )
         self.assertTrue(hasattr(__snake_case      , '''do_convert_rgb'''   )   )
    def 		a_      (	self   ):
         pass




    def 		a_      (	self   ):
         # Initialize image_processing
         snake_case					=    self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         snake_case					=    self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case   )
         for image in image_inputs:
              self.assertIsInstance(__snake_case      , Image.Image   )

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

         # Test batched
         snake_case					=    image_processing(__snake_case      , return_tensors='''pt'''   ).pixel_values
         self.assertEqual(
             encoded_images.shape      , (
                 self.image_processor_tester.batch_size,
                 self.expected_encoded_image_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   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    list_of_points
         # Degree determines the flexibility of the curve.
         # Degree = 1 will produce a straight line.
         snake_case					=    len(__snake_case   ) - 1
    def 		a_      (	self      , __snake_case   ):
         assert 0 <= t <= 1, "Time t must be between 0 and 1."
         snake_case					=    []
         for i in range(len(self.list_of_points   )   ):
              # basis function for each i
              output_values.append(
                  comb(self.degree      , __snake_case   ) * ((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(__snake_case   )      , 5   ) == 1
         return output_values
    def 		a_      (	self      , __snake_case   ):
         assert 0 <= t <= 1, "Time t must be between 0 and 1."

         snake_case					=    self.basis_function(__snake_case   )
         snake_case					=    0.0
         snake_case					=    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      , __snake_case = 0.01   ):
         from matplotlib import pyplot as plt  # type: ignore

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

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

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

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




