


"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE :    int									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE :    int									=				{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE :    List[Any]									=				"""▁"""



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=VOCAB_FILES_NAMES
		lowerCamelCase__				=PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__				=['input_ids', 'attention_mask']



		def __init__(self				,       a_				,       a_="<s>"				,       a_="</s>"				,       a_="</s>"				,       a_="<s>"				,       a_="<unk>"				,       a_="<pad>"				,       a_="<mask>"				,       a_=["<s>NOTUSED", "</s>NOTUSED"]				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				AddedToken(a_				,       lstrip=a_				,       rstrip=a_					) if isinstance(a_				,       a_					) else mask_token

			__snake_case	:						str						=				{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=a_				,       eos_token=a_				,       unk_token=a_				,       sep_token=a_				,       cls_token=a_				,       pad_token=a_				,       mask_token=a_				,       additional_special_tokens=a_				,       sp_model_kwargs=self.sp_model_kwargs				,       **a_				,       )
			__snake_case	:						List[str]						=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
			self.sp_model.Load(str(a_					)					)
			__snake_case	:						Optional[Any]						=				vocab_file
			# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
			# sentencepiece vocabulary (this is the case for <s> and </s>
			__snake_case	:						List[Any]						=				{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
			__snake_case	:						str						=				len(self.fairseq_tokens_to_ids					)
			__snake_case	:						List[Any]						=				len(self.sp_model					) + len(self.fairseq_tokens_to_ids					)
			__snake_case	:						List[str]						=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			__snake_case	:						Optional[Any]						=				[self.cls_token_id]
			__snake_case	:						Union[str, Any]						=				[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = False					):





			'''simple docstring'''

			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=a_				,       token_ids_a=a_				,       already_has_special_tokens=a_					)

			if token_ids_a is None:
				return [1] + ([0] * len(a_					)) + [1]
			return [1] + ([0] * len(a_					)) + [1, 1] + ([0] * len(a_					)) + [1]



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				[self.sep_token_id]
			__snake_case	:						str						=				[self.cls_token_id]

			if token_ids_a is None:
				return len(cls + token_ids_a + sep					) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return len(self.fairseq_tokens_to_ids					) + len(self.sp_model					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				{self.convert_ids_to_tokens(a_					): i for i in range(self.vocab_size					)}
			vocab.update(self.added_tokens_encoder					)
			return vocab



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			return self.sp_model.encode(a_				,       out_type=a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			if token in self.fairseq_tokens_to_ids:
				return self.fairseq_tokens_to_ids[token]
			elif self.sp_model.PieceToId(a_					) == 0:
				# Convert sentence piece unk token to fairseq unk token index
				return self.unk_token_id
			return self.fairseq_offset + self.sp_model.PieceToId(a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			if index in self.fairseq_ids_to_tokens:
				return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				[]
			__snake_case	:						Dict						=				''''''
			__snake_case	:						int						=				False
			for token in tokens:
				# make sure that special tokens are not decoded using sentencepiece model
				if token in self.all_special_tokens:
					if not prev_is_special:
						out_string += " "
					out_string += self.sp_model.decode(a_					) + token
					__snake_case	:						Tuple						=				True
					__snake_case	:						Any						=				[]
				else:
					current_sub_tokens.append(a_					)
					__snake_case	:						int						=				False
			out_string += self.sp_model.decode(a_					)
			return out_string.strip()



		def __getstate__(self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.__dict__.copy()
			__snake_case	:						Union[str, Any]						=				None
			return state



		def __setstate__(self				,       a_					):





			'''simple docstring'''

			__snake_case	:						int						=				d

			# for backward compatibility
			if not hasattr(self				,       '''sp_model_kwargs'''					):
				__snake_case	:						Optional[Any]						=				{}

			__snake_case	:						List[Any]						=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
			self.sp_model.Load(self.vocab_file					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if not os.path.isdir(a_					):
				logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
				return
			__snake_case	:						List[Any]						=				os.path.join(
			    a_				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

			if os.path.abspath(self.vocab_file					) != os.path.abspath(a_					) and os.path.isfile(self.vocab_file					):
				copyfile(self.vocab_file				,       a_					)
			elif not os.path.isfile(self.vocab_file					):
				with open(a_				,       '''wb'''					) as fi:
					__snake_case	:						Optional[Any]						=				self.sp_model.serialized_model_proto()
					fi.write(a_					)

			return (out_vocab_file,)









"""simple docstring"""
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


SCREAMING_SNAKE_CASE :    str									=				"""▁"""

SCREAMING_SNAKE_CASE :    List[str]									=				get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase (							__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=BigBirdTokenizer
		lowerCamelCase__				=BigBirdTokenizerFast
		lowerCamelCase__				=True
		lowerCamelCase__				=True



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().setUp()

			__snake_case	:						List[Any]						=				self.tokenizer_class(a_				,       keep_accents=a_					)
			tokenizer.save_pretrained(self.tmpdirname					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				'''<s>'''
			__snake_case	:						Optional[Any]						=				1

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				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(a_					)				,       10_04					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.assertEqual(self.get_tokenizer().vocab_size				,       10_00					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if not self.test_rust_tokenizer:
				return

			__snake_case	:						str						=				self.get_tokenizer()
			__snake_case	:						Dict						=				self.get_rust_tokenizer()

			__snake_case	:						Dict						=				'''I was born in 92000, and this is falsé.'''

			__snake_case	:						int						=				tokenizer.tokenize(a_					)
			__snake_case	:						str						=				rust_tokenizer.tokenize(a_					)
			self.assertListEqual(a_				,       a_					)

			__snake_case	:						Tuple						=				tokenizer.encode(a_				,       add_special_tokens=a_					)
			__snake_case	:						Tuple						=				rust_tokenizer.encode(a_				,       add_special_tokens=a_					)
			self.assertListEqual(a_				,       a_					)

			__snake_case	:						Optional[Any]						=				self.get_rust_tokenizer()
			__snake_case	:						Optional[int]						=				tokenizer.encode(a_					)
			__snake_case	:						Dict						=				rust_tokenizer.encode(a_					)
			self.assertListEqual(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				BigBirdTokenizer(a_				,       keep_accents=a_					)

			__snake_case	:						Optional[int]						=				tokenizer.tokenize('''This is a test'''					)
			self.assertListEqual(a_				,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']					)

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

			__snake_case	:						Union[str, Any]						=				tokenizer.tokenize('''I was born in 92000, and this is falsé.'''					)
			self.assertListEqual(
			    a_				,       [
			        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''',
			        '''.''',
			    ]				,       )
			__snake_case	:						Tuple						=				tokenizer.convert_tokens_to_ids(a_					)
			self.assertListEqual(
			    a_				,       [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]				,       )

			__snake_case	:						Optional[Any]						=				tokenizer.convert_ids_to_tokens(a_					)
			self.assertListEqual(
			    a_				,       [
			        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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				'''Hello World!'''
			__snake_case	:						List[Any]						=				[65, 1_85_36, 22_60, 1_01, 66]

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



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[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'''
			)
			# fmt: off
			__snake_case	:						Optional[int]						=				[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(a_				,       self.big_tokenizer.encode(a_					)					)



		@require_torch
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			import torch

			from transformers import BigBirdConfig, BigBirdModel

			# Build sequence
			__snake_case	:						str						=				list(self.big_tokenizer.get_vocab().keys()					)[:10]
			__snake_case	:						Tuple						=				''' '''.join(a_					)
			__snake_case	:						Tuple						=				self.big_tokenizer.encode_plus(a_				,       return_tensors='''pt'''				,       return_token_type_ids=a_					)
			__snake_case	:						List[Any]						=				self.big_tokenizer.batch_encode_plus(
			    [sequence + ''' ''' + sequence]				,       return_tensors='''pt'''				,       return_token_type_ids=a_					)

			__snake_case	:						Optional[int]						=				BigBirdConfig(attention_type='''original_full'''					)
			__snake_case	:						str						=				BigBirdModel(a_					)

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

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



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''					)
			__snake_case	:						Any						=				tokenizer.decode(tokenizer('''Paris is the [MASK].'''					).input_ids					)

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



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				{'''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=a_				,       model_name='''google/bigbird-roberta-base'''				,       revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''				,       )





