


'''simple docstring'''



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A	=get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 		_snake_case					(     UpperCamelCase__       ,   unittest.TestCase	):
						lowerCAmelCase      :str   =					PegasusTokenizer
						lowerCAmelCase      :Optional[int]   =					PegasusTokenizerFast
						lowerCAmelCase      :Union[str, Any]   =					True
						lowerCAmelCase      :Optional[Any]   =					True




						def 				snake_case__						(      self):
									super().setUp()

									# We have a SentencePiece fixture for testing
									UpperCAmelCase__  :					List[Any]        =     PegasusTokenizer(_a)
									tokenizer.save_pretrained(self.tmpdirname)




						@cached_property
						def 				snake_case__						(      self):
									return PegasusTokenizer.from_pretrained("""google/pegasus-large""")




						def 				snake_case__						(      self							,							**_lowerCamelCase):
									return PegasusTokenizer.from_pretrained(self.tmpdirname							,							**_a)




						def 				snake_case__						(      self							,							_lowerCamelCase):
									return ("This is a test", "This is a test")




						def 				snake_case__						(      self):
									UpperCAmelCase__  :					Dict        =     """</s>"""
									UpperCAmelCase__  :					Dict        =     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a)							,							_a)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a)							,							_a)




						def 				snake_case__						(      self):
									UpperCAmelCase__  :					List[str]        =     list(self.get_tokenizer().get_vocab().keys())

									self.assertEqual(vocab_keys[0]							,							"""<pad>""")
									self.assertEqual(vocab_keys[1]							,							"""</s>""")
									self.assertEqual(vocab_keys[-1]							,							"""v""")
									self.assertEqual(len(_a)							,							1103)




						def 				snake_case__						(      self):
									self.assertEqual(self.get_tokenizer().vocab_size							,							1103)




						def 				snake_case__						(      self):
									UpperCAmelCase__  :					Any        =     self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
									UpperCAmelCase__  :					Optional[int]        =     self.tokenizer_class.from_pretrained(self.tmpdirname)
									UpperCAmelCase__  :					int        =     (
									    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
									    """ </s> <pad> <pad> <pad>"""
									)
									UpperCAmelCase__  :					Optional[int]        =     rust_tokenizer([raw_input_str]							,							return_tensors=_a							,							add_special_tokens=_a).input_ids[0]
									UpperCAmelCase__  :					List[Any]        =     py_tokenizer([raw_input_str]							,							return_tensors=_a							,							add_special_tokens=_a).input_ids[0]
									self.assertListEqual(_a							,							_a)




						def 				snake_case__						(      self):
									UpperCAmelCase__  :					str        =     self._large_tokenizer
									# <mask_1> masks whole sentence while <mask_2> masks single word
									UpperCAmelCase__  :					Optional[int]        =     """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
									UpperCAmelCase__  :					Union[str, Any]        =     [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
									UpperCAmelCase__  :					Union[str, Any]        =     tokenizer([raw_input_str]							,							return_tensors=_a).input_ids[0]
									self.assertListEqual(_a							,							_a)




						def 				snake_case__						(      self):
									UpperCAmelCase__  :					Optional[int]        =     self._large_tokenizer
									# The tracebacks for the following asserts are **better** without messages or self.assertEqual
									assert tokenizer.vocab_size == 9_6103
									assert tokenizer.pad_token_id == 0
									assert tokenizer.eos_token_id == 1
									assert tokenizer.offset == 103
									assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
									assert tokenizer.unk_token == "<unk>"
									assert tokenizer.model_max_length == 1024
									UpperCAmelCase__  :					Any        =     """To ensure a smooth flow of bank resolutions."""
									UpperCAmelCase__  :					Optional[int]        =     [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
									UpperCAmelCase__  :					Optional[Any]        =     tokenizer([raw_input_str]							,							return_tensors=_a).input_ids[0]
									self.assertListEqual(_a							,							_a)
									assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




						@require_torch
						def 				snake_case__						(      self):
									UpperCAmelCase__  :					Union[str, Any]        =     ["""This is going to be way too long.""" * 150, """short example"""]
									UpperCAmelCase__  :					Optional[Any]        =     ["""not super long but more than 5 tokens""", """tiny"""]
									UpperCAmelCase__  :					Union[str, Any]        =     self._large_tokenizer(_a							,							padding=_a							,							truncation=_a							,							return_tensors="""pt""")
									UpperCAmelCase__  :					Tuple        =     self._large_tokenizer(
									    text_target=_a							,							max_length=5							,							padding=_a							,							truncation=_a							,							return_tensors="""pt""")

									assert batch.input_ids.shape == (2, 1024)
									assert batch.attention_mask.shape == (2, 1024)
									assert targets["input_ids"].shape == (2, 5)
									assert len(_a) == 2  # input_ids, attention_mask.




						@slow
						def 				snake_case__						(      self):
									# fmt: off
									UpperCAmelCase__  :					List[Any]        =     {"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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, 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, 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, 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, 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, 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, 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]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_a							,							model_name="""google/bigbird-pegasus-large-arxiv"""							,							revision="""ba85d0851d708441f91440d509690f1ab6353415"""							,							)


@require_sentencepiece
@require_tokenizers
class 		_snake_case					(     UpperCamelCase__       ,   unittest.TestCase	):
						lowerCAmelCase      :Tuple   =					PegasusTokenizer
						lowerCAmelCase      :Tuple   =					PegasusTokenizerFast
						lowerCAmelCase      :Dict   =					True
						lowerCAmelCase      :Optional[int]   =					True




						def 				snake_case__						(      self):
									super().setUp()

									# We have a SentencePiece fixture for testing
									UpperCAmelCase__  :					Tuple        =     PegasusTokenizer(_a							,							offset=0							,							mask_token_sent=_a							,							mask_token="""[MASK]""")
									tokenizer.save_pretrained(self.tmpdirname)




						@cached_property
						def 				snake_case__						(      self):
									return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""")




						def 				snake_case__						(      self							,							**_lowerCamelCase):
									return PegasusTokenizer.from_pretrained(self.tmpdirname							,							**_a)




						def 				snake_case__						(      self							,							_lowerCamelCase):
									return ("This is a test", "This is a test")




						def 				snake_case__						(      self):
									UpperCAmelCase__  :					List[Any]        =     self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
									UpperCAmelCase__  :					Dict        =     self.tokenizer_class.from_pretrained(self.tmpdirname)
									UpperCAmelCase__  :					Dict        =     (
									    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
									    """ <pad> <pad> <pad>"""
									)
									UpperCAmelCase__  :					Optional[Any]        =     rust_tokenizer([raw_input_str]							,							return_tensors=_a							,							add_special_tokens=_a).input_ids[0]
									UpperCAmelCase__  :					int        =     py_tokenizer([raw_input_str]							,							return_tensors=_a							,							add_special_tokens=_a).input_ids[0]
									self.assertListEqual(_a							,							_a)




						@require_torch
						def 				snake_case__						(      self):
									UpperCAmelCase__  :					Tuple        =     ["""This is going to be way too long.""" * 1000, """short example"""]
									UpperCAmelCase__  :					Optional[Any]        =     ["""not super long but more than 5 tokens""", """tiny"""]
									UpperCAmelCase__  :					Tuple        =     self._large_tokenizer(_a							,							padding=_a							,							truncation=_a							,							return_tensors="""pt""")
									UpperCAmelCase__  :					str        =     self._large_tokenizer(
									    text_target=_a							,							max_length=5							,							padding=_a							,							truncation=_a							,							return_tensors="""pt""")

									assert batch.input_ids.shape == (2, 4096)
									assert batch.attention_mask.shape == (2, 4096)
									assert targets["input_ids"].shape == (2, 5)
									assert len(_a) == 2  # input_ids, attention_mask.




						def 				snake_case__						(      self):
									UpperCAmelCase__  :					Optional[int]        =     (
									    """This is an example string that is used to test the original TF implementation against the HF"""
									    """ implementation"""
									)

									UpperCAmelCase__  :					Any        =     self._large_tokenizer(_a).input_ids

									self.assertListEqual(
									    _a							,							[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]							,							)



'''simple docstring'''



from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__A	=numpy.array([0, 0])
__A	=numpy.array([0.5, 0.8_6_6_0_2_5_4])
__A	=numpy.array([1, 0])
__A	=[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     initial_vectors
			for _ in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     iteration_step(UpperCamelCase__					)
			return vectors







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     []
			for i, start_vector in enumerate(vectors[:-1]					):
						UpperCAmelCase__  :					Tuple        =     vectors[i + 1]
						new_vectors.append(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     end_vector - start_vector
						new_vectors.append(start_vector + difference_vector / 3					)
						new_vectors.append(
						    start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,      6_0					)					)
						new_vectors.append(start_vector + difference_vector * 2 / 3					)
			new_vectors.append(vectors[-1]					)
			return new_vectors







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     numpy.radians(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     numpy.cos(UpperCamelCase__					), numpy.sin(UpperCamelCase__					)
			UpperCAmelCase__  :					Dict        =     numpy.array(((c, -s), (s, c))					)
			return numpy.dot(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     plt.gca()
			axes.set_aspect("""equal"""					)

			# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
			# y-coordinates as inputs, which are constructed from the vector-list using
			# zip()
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     zip(*UpperCamelCase__					)
			plt.plot(UpperCamelCase__ ,      UpperCamelCase__					)
			plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__A	=iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)