
import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A   =     '''▁'''

_A   =     get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
class 	_lowerCAmelCase					(	a__					,      unittest.TestCase       ):
				_lowercase					=BertGenerationTokenizer
				_lowercase					=False
				_lowercase					=True




				def      __a						(					self     )				->      Optional[int]:
							super().setUp()

							lowerCAmelCase_						=	BertGenerationTokenizer(lowerCAmelCase__				,				keep_accents=lowerCAmelCase__     )
							tokenizer.save_pretrained(self.tmpdirname     )




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	"<s>"
							lowerCAmelCase_						=	1

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




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	list(self.get_tokenizer().get_vocab().keys()     )

							self.assertEqual(vocab_keys[0]				,				"<unk>"     )
							self.assertEqual(vocab_keys[1]				,				"<s>"     )
							self.assertEqual(vocab_keys[-1]				,				"<pad>"     )
							self.assertEqual(len(lowerCAmelCase__     )				,				1_002     )




				def      __a						(					self     )				->      List[Any]:
							self.assertEqual(self.get_tokenizer().vocab_size				,				1_000     )




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	BertGenerationTokenizer(lowerCAmelCase__				,				keep_accents=lowerCAmelCase__     )

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

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )				,				[285, 46, 10, 170, 382]				,				)

							lowerCAmelCase_						=	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",
							        ".",
							    ]				,				)
							lowerCAmelCase_						=	tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )
							self.assertListEqual(
							    lowerCAmelCase__				,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,				)

							lowerCAmelCase_						=	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      __a						(					self     )				->      Tuple:
							return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"     )




				@slow
				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	"Hello World!"
							lowerCAmelCase_						=	[18_536, 2_260, 101]

							self.assertListEqual(lowerCAmelCase__				,				self.big_tokenizer.encode(lowerCAmelCase__     )     )




				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	(
							    "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"
							)
							lowerCAmelCase_						=	[
							    871,
							    419,
							    358,
							    946,
							    991,
							    2_521,
							    452,
							    358,
							    1_357,
							    387,
							    7_751,
							    3_536,
							    112,
							    985,
							    456,
							    126,
							    865,
							    938,
							    5_400,
							    5_734,
							    458,
							    1_368,
							    467,
							    786,
							    2_462,
							    5_246,
							    1_159,
							    633,
							    865,
							    4_519,
							    457,
							    582,
							    852,
							    2_557,
							    427,
							    916,
							    508,
							    405,
							    34_324,
							    497,
							    391,
							    408,
							    11_342,
							    1_244,
							    385,
							    100,
							    938,
							    985,
							    456,
							    574,
							    362,
							    12_597,
							    3_200,
							    3_129,
							    1_172,
							]

							self.assertListEqual(lowerCAmelCase__				,				self.big_tokenizer.encode(lowerCAmelCase__     )     )




				@require_torch
				@slow
				def      __a						(					self     )				->      Union[str, Any]:
							import torch

							from transformers import BertGenerationConfig, BertGenerationEncoder

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

							lowerCAmelCase_						=	BertGenerationConfig()
							lowerCAmelCase_						=	BertGenerationEncoder(lowerCAmelCase__     )

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

							with torch.no_grad():
										model(**lowerCAmelCase__     )
										model(**lowerCAmelCase__     )




				@slow
				def      __a						(					self     )				->      Tuple:
							# fmt: off
							lowerCAmelCase_						=	{"input_ids": [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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/bert_for_seq_generation_L-24_bbc_encoder"				,				revision="c817d1fd1be2ffa69431227a1fe320544943d4db"				,				)










"""simple docstring"""




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->list[list[list[float] | float]]:






	'''simple docstring'''






	if dataset.ndim != value_array.ndim:
		a      :		str	=				(
		    "Wrong input data's dimensions... "
		    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
		)
		raise ValueError(_lowercase					)

	try:
		if dataset.shape[1] != value_array.shape[1]:
			a      :		int	=				(
			    "Wrong input data's shape... "
			    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
			)
			raise ValueError(_lowercase					)
	except IndexError:
		if dataset.ndim != value_array.ndim:
			raise TypeError("Wrong shape"					)

	if dataset.dtype != value_array.dtype:
		a      :		Union[str, Any]	=				(
		    "Input data have different datatype... "
		    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
		)
		raise TypeError(_lowercase					)

	a      :		str	=				[]

	for value in value_array:
		a      :		List[Any]	=				euclidean(_lowercase    ,       dataset[0]					)
		a      :		str	=				dataset[0].tolist()

		for dataset_value in dataset[1:]:
			a      :		Tuple	=				euclidean(_lowercase    ,       _lowercase					)

			if dist > temp_dist:
				a      :		Dict	=				temp_dist
				a      :		Optional[int]	=				dataset_value.tolist()

		answer.append([vector, dist]					)

	return answer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






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


if __name__ == "__main__":
		import doctest

		doctest.testmod()


