
"""simple docstring"""







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


lowerCamelCase__           =	"""▁"""

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




@require_sentencepiece
class 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: Dict            =			BertGenerationTokenizer
		A_		: List[Any]            =			False
		A_		: Tuple            =			True

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

			__lowerCAmelCase    :		Union[str, Any]		    =   BertGenerationTokenizer(_SCREAMING_SNAKE_CASE ,	keep_accents=_SCREAMING_SNAKE_CASE				)
			tokenizer.save_pretrained(self.tmpdirname				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   '<s>'
			__lowerCAmelCase    :		Dict		    =   1

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		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] ,	'<pad>'				)
			self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	10_02				)

		def 							__lowerCamelCase							(   self				):
			self.assertEqual(self.get_tokenizer().vocab_size ,	10_00				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   BertGenerationTokenizer(_SCREAMING_SNAKE_CASE ,	keep_accents=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[str]		    =   tokenizer.tokenize('This is a test'				)
			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	['▁This', '▁is', '▁a', '▁t', 'est']				)

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

			__lowerCAmelCase    :		List[str]		    =   tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[
			        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    :		Optional[int]		    =   tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[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] ,	)

			__lowerCAmelCase    :		Any		    =   tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[
			        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 							__lowerCamelCase							(   self				):
			return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'				)

		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   'Hello World!'
			__lowerCAmelCase    :		str		    =   [1_85_36, 22_60, 1_01]

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

		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   (
			    '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    :		Union[str, Any]		    =   [
			    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,
			]

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

		@require_torch
		@slow
		def 							__lowerCamelCase							(   self				):
			import torch

			from transformers import BertGenerationConfig, BertGenerationEncoder

			# Build sequence
			__lowerCAmelCase    :		Optional[int]		    =   list(self.big_tokenizer.get_vocab().keys()				)[:10]
			__lowerCAmelCase    :		Optional[int]		    =   ' '.join(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   self.big_tokenizer.encode_plus(_SCREAMING_SNAKE_CASE ,	return_tensors='pt' ,	return_token_type_ids=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		int		    =   self.big_tokenizer.batch_encode_plus(
			    [sequence + ' ' + sequence] ,	return_tensors='pt' ,	return_token_type_ids=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   BertGenerationConfig()
			__lowerCAmelCase    :		Union[str, Any]		    =   BertGenerationEncoder(_SCREAMING_SNAKE_CASE				)

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

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







		@slow
		def 							__lowerCamelCase							(   self				):
			# fmt: off
			__lowerCAmelCase    :		Union[str, Any]		    =   {'input_ids': [[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], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_SCREAMING_SNAKE_CASE ,	model_name='google/bert_for_seq_generation_L-24_bbc_encoder' ,	revision='c817d1fd1be2ffa69431227a1fe320544943d4db' ,	)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()