





from __future__ import annotations

from scipy.special import comb  # type: ignore







class 				__lowerCAmelCase       :







      def __init__(    self		,      lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						list[float]:



        '''simple docstring'''




        assert 0 <= t <= 1, "Time t must be between 0 and 1."
        a__	:					list[float]										=[]
        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    _lowercase  (    self		,      lowerCAmelCase__							)   ->						tuple[float, float]:



        '''simple docstring'''




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

        a__	:					Optional[Any]										=self.basis_function(lowerCAmelCase__							)
        a__	:					Dict										=0.0
        a__	:					Dict										=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    _lowercase  (    self		,      lowerCAmelCase__ = 0.01							)   ->						Any:



        '''simple docstring'''




        from matplotlib import pyplot as plt  # type: ignore

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

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

        a__	:					List[Any]										=[i[0] for i in self.list_of_points]
        a__	:					Dict										=[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













import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import CLIPSegProcessor, ViTImageProcessor







@require_vision
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=tempfile.mkdtemp()

        # fmt: off
        a__	:					List[Any]										=["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
        # fmt: on
        a__	:					str										=dict(zip(lowerCAmelCase__		,      range(len(lowerCAmelCase__							)							)							)							)
        a__	:					List[Any]										=["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        a__	:					Optional[int]										={"unk_token": "<unk>"}

        a__	:					Optional[Any]										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]							)
        a__	:					Tuple										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["merges_file"]							)
        with open(self.vocab_file		,      "w"		,      encoding="utf-8"							) as fp:
          fp.write(json.dumps(lowerCAmelCase__							) + "\n"							)
        with open(self.merges_file		,      "w"		,      encoding="utf-8"							) as fp:
          fp.write("\n".join(lowerCAmelCase__							)							)

        a__	:					Optional[Any]										={
            "do_resize": True,
            "size": 2_0,
            "do_center_crop": True,
            "crop_size": 1_8,
            "do_normalize": True,
            "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        a__	:					Dict										=os.path.join(self.tmpdirname		,      lowerCAmelCase__							)
        with open(self.image_processor_file		,      "w"		,      encoding="utf-8"							) as fp:
          json.dump(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

        a__	:					List[Any]										=[Image.fromarray(np.moveaxis(lowerCAmelCase__		,      0		,      -1							)							) for x in image_inputs]

        return image_inputs







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_tokenizer()
        a__	:					int										=self.get_rust_tokenizer()
        a__	:					List[str]										=self.get_image_processor()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_slow.save_pretrained(self.tmpdirname							)
        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase__							)

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_fast.save_pretrained(self.tmpdirname							)
        a__	:					Dict										=CLIPSegProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor_slow.tokenizer.get_vocab()		,      tokenizer_slow.get_vocab()							)
        self.assertEqual(processor_fast.tokenizer.get_vocab()		,      tokenizer_fast.get_vocab()							)
        self.assertEqual(tokenizer_slow.get_vocab()		,      tokenizer_fast.get_vocab()							)
        self.assertIsInstance(processor_slow.tokenizer		,      lowerCAmelCase__							)
        self.assertIsInstance(processor_fast.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor_slow.image_processor.to_json_string()		,      image_processor.to_json_string()							)
        self.assertEqual(processor_fast.image_processor.to_json_string()		,      image_processor.to_json_string()							)
        self.assertIsInstance(processor_slow.image_processor		,      lowerCAmelCase__							)
        self.assertIsInstance(processor_fast.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        a__	:					str										=self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"							)
        a__	:					int										=self.get_image_processor(do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(
            self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					str										=self.get_image_processor()
        a__	:					Optional[int]										=self.get_tokenizer()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					str										=self.prepare_image_inputs()

        a__	:					Any										=image_processor(lowerCAmelCase__		,      return_tensors="np"							)
        a__	:					Optional[int]										=processor(images=lowerCAmelCase__		,      return_tensors="np"							)

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()		,      input_processor[key].sum()		,      delta=1E-2							)







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					List[Any]										=self.get_tokenizer()

        a__	:					Optional[int]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Union[str, Any]										="lower newer"

        a__	:					List[str]										=processor(text=lowerCAmelCase__							)

        a__	:					str										=tokenizer(lowerCAmelCase__							)

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,      encoded_processor[key]							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										=self.get_image_processor()
        a__	:					Dict										=self.get_tokenizer()

        a__	:					Union[str, Any]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										="lower newer"
        a__	:					int										=self.prepare_image_inputs()

        a__	:					Any										=processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["input_ids", "attention_mask", "pixel_values"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_image_processor()
        a__	:					Optional[Any]										=self.get_tokenizer()

        a__	:					str										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					int										=self.prepare_image_inputs()
        a__	:					Union[str, Any]										=self.prepare_image_inputs()

        a__	:					Tuple										=processor(images=lowerCAmelCase__		,      visual_prompt=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["pixel_values", "conditional_pixel_values"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					Any										=self.get_tokenizer()

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__	:					Optional[Any]										=processor.batch_decode(lowerCAmelCase__							)
        a__	:					Dict										=tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)






