


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class        _a							(			UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			str        = DebertaTokenizer
							_lowercase	:			Any        = True
							_lowercase	:			Union[str, Any]        = DebertaTokenizerFast

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Optional[Any]:





									"""simple docstring"""







									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									lowercase__            =      [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''[UNK]''',
									]
									lowercase__            =      dict(zip(UpperCamelCase_    ,						range(len(UpperCamelCase_							)							)							)							)
									lowercase__            =      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									lowercase__            =      {'''unk_token''': '''[UNK]'''}

									lowercase__            =      os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES['''vocab_file''']							)
									lowercase__            =      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(UpperCamelCase_							) + '''\n'''							)
									with open(self.merges_file    ,						'''w'''    ,						encoding='''utf-8'''							) as fp:
											fp.write('''\n'''.join(UpperCamelCase_							)							)

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						**UpperCamelCase_:	Union[str, Any]							)			->   Dict:





									"""simple docstring"""







									kwargs.update(self.special_tokens_map							)
									return self.tokenizer_class.from_pretrained(self.tmpdirname    ,						**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	int							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      '''lower newer'''
									lowercase__            =      '''lower newer'''
									return input_text, output_text

							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Any:





									"""simple docstring"""







									lowercase__            =      self.get_tokenizer()
									lowercase__            =      '''lower newer'''
									lowercase__            =      ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
									lowercase__            =      tokenizer.tokenize(UpperCamelCase_							)
									self.assertListEqual(UpperCamelCase_    ,						UpperCamelCase_							)

									lowercase__            =      tokens + [tokenizer.unk_token]
									lowercase__            =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_							)    ,						UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[Any]							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      self.get_tokenizer()
									lowercase__            =      tokenizer('''Hello'''    ,						'''World'''							)
									lowercase__            =      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
									self.assertListEqual(tokd['''token_type_ids''']    ,						UpperCamelCase_							)

							@slow
							def       lowerCamelCase_ (							self:	int							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''							)

									lowercase__            =      tokenizer.encode('''sequence builders'''    ,						add_special_tokens=UpperCamelCase_							)
									lowercase__            =      tokenizer.encode('''multi-sequence build'''    ,						add_special_tokens=UpperCamelCase_							)

									lowercase__            =      tokenizer.encode(
									    '''sequence builders'''    ,						add_special_tokens=UpperCamelCase_    ,						add_prefix_space=UpperCamelCase_							)
									lowercase__            =      tokenizer.encode(
									    '''sequence builders'''    ,						'''multi-sequence build'''    ,						add_special_tokens=UpperCamelCase_    ,						add_prefix_space=UpperCamelCase_							)

									lowercase__            =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase_							)
									lowercase__            =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    ,						UpperCamelCase_							)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode


							@slow
							def       lowerCamelCase_ (							self:	str							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      [self.tokenizer_class]
									if self.test_rust_tokenizer:
											tokenizer_classes.append(self.rust_tokenizer_class							)

									for tokenizer_class in tokenizer_classes:
											lowercase__            =      tokenizer_class.from_pretrained('''microsoft/deberta-base'''							)

											lowercase__            =      [
											    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
											    '''ALBERT incorporates two parameter reduction techniques''',
											    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
											    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
											    ''' vocabulary embedding.''',
											]

											lowercase__            =      tokenizer(UpperCamelCase_    ,						padding=UpperCamelCase_							)
											lowercase__            =      [tokenizer.decode(UpperCamelCase_    ,						skip_special_tokens=UpperCamelCase_							) for seq in encoding['''input_ids''']]

											# fmt: off
											lowercase__            =      {
											    '''input_ids''': [
											        [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
											    ],
											    '''token_type_ids''': [
											        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
											    ],
											    '''attention_mask''': [
											        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
											    ]
											}
											# fmt: on

											lowercase__            =      [
											    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
											    '''ALBERT incorporates two parameter reduction techniques''',
											    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
											    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
											    ''' vocabulary embedding.''',
											]

											self.assertDictEqual(encoding.data    ,						UpperCamelCase_							)

											for expected, decoded in zip(UpperCamelCase_    ,						UpperCamelCase_							):
													self.assertEqual(UpperCamelCase_    ,						UpperCamelCase_							)



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



