




from .number_theory.prime_numbers import next_prime


class 							__SCREAMING_SNAKE_CASE				:

					def __init__(							self					,			__lowercase					,			__lowercase = None					,			__lowercase = None					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				size_table
						lowerCamelCase_				:						int				  =				[None] * self.size_table
						lowerCamelCase_				:						Any				  =				0.75 if lim_charge is None else lim_charge
						lowerCamelCase_				:						str				  =				1 if charge_factor is None else charge_factor
						lowerCamelCase_				:						list				  =				[]
						lowerCamelCase_				:						dict				  =				{}

					def       lowercase_				(							self   ):







						'''simple docstring'''




						return self._keys

					def       lowercase_				(							self   ):







						'''simple docstring'''




						return sum(1 for slot in self.values if slot is not None   ) / (
						    self.size_table * self.charge_factor
						)

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						return key % self.size_table

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						print(f"step {step_ord}"   )
						print(list(range(len(self.values   )   )   )   )
						print(self.values   )

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				1
						lowerCamelCase_				:						Dict				  =				values
						for value in values:
							self.insert_data(__lowercase   )
							self._step_by_step(__lowercase   )
							i += 1

					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				data
						lowerCamelCase_				:						str				  =				data

					def       lowercase_				(							self					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				self.hash_function(key + 1   )

						while self.values[new_key] is not None and self.values[new_key] != key:
							if self.values.count(__lowercase   ) > 0:
								lowerCamelCase_				:						Dict				  =				self.hash_function(new_key + 1   )
							else:
								lowerCamelCase_				:						str				  =				None
								break

						return new_key

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				[value for value in self.values if value is not None]
						lowerCamelCase_				:						str				  =				next_prime(self.size_table					,			factor=2   )
						self._keys.clear()
						lowerCamelCase_				:						Any				  =				[None] * self.size_table  # hell's pointers D: don't DRY ;/
						for value in survivor_values:
							self.insert_data(__lowercase   )





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				self.hash_function(__lowercase   )

						if self.values[key] is None:
							self._set_value(__lowercase					,			__lowercase   )

						elif self.values[key] == data:
							pass

						else:
							lowerCamelCase_				:						Optional[int]				  =				self._collision_resolution(__lowercase					,			__lowercase   )
							if collision_resolution is not None:
								self._set_value(__lowercase					,			__lowercase   )
							else:
								self.rehashing()
								self.insert_data(__lowercase   )






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							UpperCamelCase__						=		None


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__						=		{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase__						=		{
    'xlm-roberta-base': 5_1_2,
    'xlm-roberta-large': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-english': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-german': 5_1_2,
}


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					VOCAB_FILES_NAMES
					_lowerCamelCase						    =					PRETRAINED_VOCAB_FILES_MAP
					_lowerCamelCase						    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCamelCase						    =					["""input_ids""", """attention_mask"""]
					_lowerCamelCase						    =					XLMRobertaTokenizer

					def __init__(							self					,			__lowercase=None					,			__lowercase=None					,			__lowercase="<s>"					,			__lowercase="</s>"					,			__lowercase="</s>"					,			__lowercase="<s>"					,			__lowercase="<unk>"					,			__lowercase="<pad>"					,			__lowercase="<mask>"					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				AddedToken(__lowercase					,			lstrip=__lowercase					,			rstrip=__lowercase   ) if isinstance(__lowercase					,			__lowercase   ) else mask_token

						super().__init__(
						    __lowercase					,			tokenizer_file=__lowercase					,			bos_token=__lowercase					,			eos_token=__lowercase					,			sep_token=__lowercase					,			cls_token=__lowercase					,			unk_token=__lowercase					,			pad_token=__lowercase					,			mask_token=__lowercase					,			**__lowercase					,			)

						lowerCamelCase_				:						str				  =				vocab_file
						lowerCamelCase_				:						str				  =				False if not self.vocab_file else True

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCamelCase_				:						Any				  =				[self.cls_token_id]
						lowerCamelCase_				:						List[Any]				  =				[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				[self.sep_token_id]
						lowerCamelCase_				:						Dict				  =				[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]





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'   )

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

						if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowercase   ):
							copyfile(self.vocab_file					,			__lowercase   )

						return (out_vocab_file,)
