






import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


__magic_name__:					List[Any]							=     "▁"

__magic_name__:					Tuple							=     get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class 	snake_case__ (							_lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					Optional[Any]											=   BigBirdTokenizer
	lowercase__						:					Union[str, Any]											=   BigBirdTokenizerFast
	lowercase__						:					str											=   True
	lowercase__						:					List[Any]											=   True
	def       __magic_name__    (    self			)							->				str:
							super().setUp()

							__magic_name__      :       Union[str, Any]       						= self.tokenizer_class(lowerCAmelCase__      ,       keep_accents=lowerCAmelCase__			)
							tokenizer.save_pretrained(self.tmpdirname			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       List[Any]       						= """<s>"""
							__magic_name__      :       str       						= 1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__			)      ,       lowerCAmelCase__			)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__			)      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       List[Any]       						= list(self.get_tokenizer().get_vocab().keys()			)

							self.assertEqual(vocab_keys[0]      ,       """<unk>"""			)
							self.assertEqual(vocab_keys[1]      ,       """<s>"""			)
							self.assertEqual(vocab_keys[-1]      ,       """[MASK]"""			)
							self.assertEqual(len(lowerCAmelCase__			)      ,       10_04			)
	def       __magic_name__    (    self			)							->				int:
							self.assertEqual(self.get_tokenizer().vocab_size      ,       10_00			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							if not self.test_rust_tokenizer:
													return

							__magic_name__      :       int       						= self.get_tokenizer()
							__magic_name__      :       List[Any]       						= self.get_rust_tokenizer()

							__magic_name__      :       Optional[int]       						= """I was born in 92000, and this is falsé."""

							__magic_name__      :       Union[str, Any]       						= tokenizer.tokenize(lowerCAmelCase__			)
							__magic_name__      :       Any       						= rust_tokenizer.tokenize(lowerCAmelCase__			)
							self.assertListEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)

							__magic_name__      :       Dict       						= tokenizer.encode(lowerCAmelCase__      ,       add_special_tokens=lowerCAmelCase__			)
							__magic_name__      :       Optional[Any]       						= rust_tokenizer.encode(lowerCAmelCase__      ,       add_special_tokens=lowerCAmelCase__			)
							self.assertListEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= self.get_rust_tokenizer()
							__magic_name__      :       List[Any]       						= tokenizer.encode(lowerCAmelCase__			)
							__magic_name__      :       Tuple       						= rust_tokenizer.encode(lowerCAmelCase__			)
							self.assertListEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Union[str, Any]       						= BigBirdTokenizer(lowerCAmelCase__      ,       keep_accents=lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= tokenizer.tokenize("""This is a test"""			)
							self.assertListEqual(lowerCAmelCase__      ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(lowerCAmelCase__			)      ,       [2_85, 46, 10, 1_70, 3_82]      ,       )

							__magic_name__      :       int       						= tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
							self.assertListEqual(
							    lowerCAmelCase__      ,       [
							        SPIECE_UNDERLINE + """I""",
							        SPIECE_UNDERLINE + """was""",
							        SPIECE_UNDERLINE + """b""",
							        """or""",
							        """n""",
							        SPIECE_UNDERLINE + """in""",
							        SPIECE_UNDERLINE + """""",
							        """9""",
							        """2""",
							        """0""",
							        """0""",
							        """0""",
							        """,""",
							        SPIECE_UNDERLINE + """and""",
							        SPIECE_UNDERLINE + """this""",
							        SPIECE_UNDERLINE + """is""",
							        SPIECE_UNDERLINE + """f""",
							        """al""",
							        """s""",
							        """é""",
							        """.""",
							    ]      ,       )
							__magic_name__      :       Optional[int]       						= tokenizer.convert_tokens_to_ids(lowerCAmelCase__			)
							self.assertListEqual(
							    lowerCAmelCase__      ,       [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,       )

							__magic_name__      :       Optional[Any]       						= tokenizer.convert_ids_to_tokens(lowerCAmelCase__			)
							self.assertListEqual(
							    lowerCAmelCase__      ,       [
							        SPIECE_UNDERLINE + """I""",
							        SPIECE_UNDERLINE + """was""",
							        SPIECE_UNDERLINE + """b""",
							        """or""",
							        """n""",
							        SPIECE_UNDERLINE + """in""",
							        SPIECE_UNDERLINE + """""",
							        """<unk>""",
							        """2""",
							        """0""",
							        """0""",
							        """0""",
							        """,""",
							        SPIECE_UNDERLINE + """and""",
							        SPIECE_UNDERLINE + """this""",
							        SPIECE_UNDERLINE + """is""",
							        SPIECE_UNDERLINE + """f""",
							        """al""",
							        """s""",
							        """<unk>""",
							        """.""",
							    ]      ,       )
	@cached_property
	def       __magic_name__    (    self			)							->				Dict:
							return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""			)
	@slow
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       List[str]       						= """Hello World!"""
							__magic_name__      :       List[str]       						= [65, 1_85_36, 22_60, 1_01, 66]

							self.assertListEqual(lowerCAmelCase__      ,       self.big_tokenizer.encode(lowerCAmelCase__			)			)
	@slow
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Dict       						= (
							    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
							    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
							)
							# fmt: off
							__magic_name__      :       Optional[Any]       						= [65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
							# fmt: on
							self.assertListEqual(lowerCAmelCase__      ,       self.big_tokenizer.encode(lowerCAmelCase__			)			)
	@require_torch
	@slow
	def       __magic_name__    (    self			)							->				Dict:
							import torch

							from transformers import BigBirdConfig, BigBirdModel

							# Build sequence
							__magic_name__      :       Any       						= list(self.big_tokenizer.get_vocab().keys()			)[:10]
							__magic_name__      :       Optional[Any]       						= """ """.join(lowerCAmelCase__			)
							__magic_name__      :       List[Any]       						= self.big_tokenizer.encode_plus(lowerCAmelCase__      ,       return_tensors="""pt"""      ,       return_token_type_ids=lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= self.big_tokenizer.batch_encode_plus(
							    [sequence + """ """ + sequence]      ,       return_tensors="""pt"""      ,       return_token_type_ids=lowerCAmelCase__			)

							__magic_name__      :       int       						= BigBirdConfig(attention_type="""original_full"""			)
							__magic_name__      :       Union[str, Any]       						= BigBirdModel(lowerCAmelCase__			)

							assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

							with torch.no_grad():
													model(**lowerCAmelCase__			)
													model(**lowerCAmelCase__			)
	@slow
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Optional[Any]       						= BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""			)
							__magic_name__      :       Optional[int]       						= tokenizer.decode(tokenizer("""Paris is the [MASK]."""			).input_ids			)

							self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""			)







	@slow
	def       __magic_name__    (    self			)							->				Any:
							# fmt: off
							__magic_name__      :       int       						= {"""input_ids""": [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 0, 0, 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=lowerCAmelCase__      ,       model_name="""google/bigbird-roberta-base"""      ,       revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""      ,       )








from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return math.sqrt(sum(pow(a - b,   2			) for a, b in zip(_A,   _A			)			)			)

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						if dataset.ndim != value_array.ndim:
												__magic_name__      :       str       						= (
												    """Wrong input data's dimensions... """
												    f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
												)
												raise ValueError(_A			)

						try:
												if dataset.shape[1] != value_array.shape[1]:
																		__magic_name__      :       Optional[Any]       						= (
																		    """Wrong input data's shape... """
																		    f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
																		)
																		raise ValueError(_A			)
						except IndexError:
												if dataset.ndim != value_array.ndim:
																		raise TypeError("""Wrong shape"""			)

						if dataset.dtype != value_array.dtype:
												__magic_name__      :       List[Any]       						= (
												    """Input data have different datatype... """
												    f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
												)
												raise TypeError(_A			)

						__magic_name__      :       Dict       						= []

						for value in value_array:
												__magic_name__      :       Tuple       						= euclidean(_A,   dataset[0]			)
												__magic_name__      :       Any       						= dataset[0].tolist()

												for dataset_value in dataset[1:]:
																		__magic_name__      :       Any       						= euclidean(_A,   _A			)

																		if dist > temp_dist:
																								__magic_name__      :       Dict       						= temp_dist
																								__magic_name__      :       Any       						= dataset_value.tolist()

												answer.append([vector, dist]			)

						return answer

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return np.dot(_A,   _A			) / (norm(_A			) * norm(_A			))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
