



"""simple docstring"""

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 AlignProcessor, EfficientNetImageProcessor




@require_vision
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Tuple				=	tempfile.mkdtemp()

     snake_case_ :     str				=	[
         '''[UNK]''',
         '''[CLS]''',
         '''[SEP]''',
         '''[PAD]''',
         '''[MASK]''',
         '''want''',
         '''##want''',
         '''##ed''',
         '''wa''',
         '''un''',
         '''runn''',
         '''##ing''',
         ''',''',
         '''low''',
         '''lowest''',
     ]
     snake_case_ :     Optional[int]				=	os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES['''vocab_file''']				)
     with open(self.vocab_file  ,     '''w'''  ,     encoding='''utf-8'''				) as vocab_writer:
         vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)

     snake_case_ :     Any				=	{
         '''do_resize''': True,
         '''size''': 20,
         '''do_center_crop''': True,
         '''crop_size''': 18,
         '''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],
     }
     snake_case_ :     int				=	os.path.join(self.tmpdirname  ,     __UpperCAmelCase				)
     with open(self.image_processor_file  ,     '''w'''  ,     encoding='''utf-8'''				) as fp:
         json.dump(__UpperCAmelCase  ,     __UpperCAmelCase				)

 def 						_snake_case					(     self		:       str  ,     **lowercase_		:       int				):
     return BertTokenizer.from_pretrained(self.tmpdirname  ,     **__UpperCAmelCase				)

 def 						_snake_case					(     self		:       Tuple  ,     **lowercase_		:       List[str]				):
     return BertTokenizerFast.from_pretrained(self.tmpdirname  ,     **__UpperCAmelCase				)

 def 						_snake_case					(     self		:       Dict  ,     **lowercase_		:       List[Any]				):
     return EfficientNetImageProcessor.from_pretrained(self.tmpdirname  ,     **__UpperCAmelCase				)

 def 						_snake_case					(     self		:       Any				):
     shutil.rmtree(self.tmpdirname				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Any				=	[np.random.randint(255  ,     size=(3, 30, 400)  ,     dtype=np.uinta				)]
     snake_case_ :     Any				=	[Image.fromarray(np.moveaxis(__UpperCAmelCase  ,     0  ,     -1				)				) for x in image_inputs]
     return image_inputs

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     List[Any]				=	self.get_tokenizer()
     snake_case_ :     Optional[Any]				=	self.get_rust_tokenizer()
     snake_case_ :     List[Any]				=	self.get_image_processor()

     snake_case_ :     Tuple				=	AlignProcessor(tokenizer=__UpperCAmelCase  ,     image_processor=__UpperCAmelCase				)
     processor_slow.save_pretrained(self.tmpdirname				)
     snake_case_ :     Tuple				=	AlignProcessor.from_pretrained(self.tmpdirname  ,     use_fast=__UpperCAmelCase				)

     snake_case_ :     Tuple				=	AlignProcessor(tokenizer=__UpperCAmelCase  ,     image_processor=__UpperCAmelCase				)
     processor_fast.save_pretrained(self.tmpdirname				)
     snake_case_ :     Tuple				=	AlignProcessor.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  ,     __UpperCAmelCase				)
     self.assertIsInstance(processor_fast.tokenizer  ,     __UpperCAmelCase				)

     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  ,     __UpperCAmelCase				)
     self.assertIsInstance(processor_fast.image_processor  ,     __UpperCAmelCase				)

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Any				=	AlignProcessor(tokenizer=self.get_tokenizer()  ,     image_processor=self.get_image_processor()				)
     processor.save_pretrained(self.tmpdirname				)

     snake_case_ :     List[str]				=	self.get_tokenizer(bos_token='''(BOS)'''  ,     eos_token='''(EOS)'''				)
     snake_case_ :     int				=	self.get_image_processor(do_normalize=__UpperCAmelCase  ,     padding_value=1.0				)

     snake_case_ :     List[str]				=	AlignProcessor.from_pretrained(
         self.tmpdirname  ,     bos_token='''(BOS)'''  ,     eos_token='''(EOS)'''  ,     do_normalize=__UpperCAmelCase  ,     padding_value=1.0				)

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

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

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Tuple				=	self.get_image_processor()
     snake_case_ :     Optional[Any]				=	self.get_tokenizer()

     snake_case_ :     str				=	AlignProcessor(tokenizer=__UpperCAmelCase  ,     image_processor=__UpperCAmelCase				)

     snake_case_ :     Dict				=	self.prepare_image_inputs()

     snake_case_ :     List[str]				=	image_processor(__UpperCAmelCase  ,     return_tensors='''np'''				)
     snake_case_ :     Optional[int]				=	processor(images=__UpperCAmelCase  ,     return_tensors='''np'''				)

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

 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     Optional[Any]				=	self.get_image_processor()
     snake_case_ :     Optional[int]				=	self.get_tokenizer()

     snake_case_ :     int				=	AlignProcessor(tokenizer=__UpperCAmelCase  ,     image_processor=__UpperCAmelCase				)

     snake_case_ :     Optional[int]				=	'''lower newer'''

     snake_case_ :     int				=	processor(text=__UpperCAmelCase				)

     snake_case_ :     Any				=	tokenizer(__UpperCAmelCase  ,     padding='''max_length'''  ,     max_length=64				)

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

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     str				=	self.get_image_processor()
     snake_case_ :     List[str]				=	self.get_tokenizer()

     snake_case_ :     Any				=	AlignProcessor(tokenizer=__UpperCAmelCase  ,     image_processor=__UpperCAmelCase				)

     snake_case_ :     List[Any]				=	'''lower newer'''
     snake_case_ :     Optional[Any]				=	self.prepare_image_inputs()

     snake_case_ :     Optional[Any]				=	processor(text=__UpperCAmelCase  ,     images=__UpperCAmelCase				)

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

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

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Any				=	self.get_image_processor()
     snake_case_ :     Optional[int]				=	self.get_tokenizer()

     snake_case_ :     Any				=	AlignProcessor(tokenizer=__UpperCAmelCase  ,     image_processor=__UpperCAmelCase				)

     snake_case_ :     Optional[int]				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     snake_case_ :     int				=	processor.batch_decode(__UpperCAmelCase				)
     snake_case_ :     Optional[Any]				=	tokenizer.batch_decode(__UpperCAmelCase				)

     self.assertListEqual(__UpperCAmelCase  ,     __UpperCAmelCase				)






 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     List[str]				=	self.get_image_processor()
     snake_case_ :     List[Any]				=	self.get_tokenizer()

     snake_case_ :     Dict				=	AlignProcessor(tokenizer=__UpperCAmelCase  ,     image_processor=__UpperCAmelCase				)

     snake_case_ :     Dict				=	'''lower newer'''
     snake_case_ :     List[Any]				=	self.prepare_image_inputs()

     snake_case_ :     str				=	processor(text=__UpperCAmelCase  ,     images=__UpperCAmelCase				)

     self.assertListEqual(list(inputs.keys()				)  ,     processor.model_input_names				)






from __future__ import annotations

from scipy.special import comb  # type: ignore





class      A							:


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						list[tuple[float, float]]		)						->				List[str]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						float		)						->				list[float]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						float		)						->				tuple[float, float]:
         """simple docstring"""



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

         UpperCAmelCase__								=  self.basis_function(__UpperCAmelCase		)
         UpperCAmelCase__								=  0.0
         UpperCAmelCase__								=  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     :						Optional[int]       ,  __UpperCAmelCase     :						float = 0.01		)						->				Optional[int]:
         """simple docstring"""



         from matplotlib import pyplot as plt  # type: ignore

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

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

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

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



