





import os
import re
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							=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							=						{"""vocab_file""": """spiece.model"""}

_SCREAMING_SNAKE_CASE							=						{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

_SCREAMING_SNAKE_CASE							=						{
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}
class SCREAMING_SNAKE_CASE_  (    snake_case_    ):
						__magic_name__:	Dict       					=				VOCAB_FILES_NAMES
						__magic_name__:	List[str]       					=				PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:	Optional[int]       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:	Tuple       					=				['input_ids', 'attention_mask']
						__magic_name__:	List[int]       					=				[]






						def __init__(					self					:					List[Any]						,      _A					:					Any						,      _A					:					str="<unk>"						,      _A					:					Optional[int]="<s>"						,      _A					:					Tuple="</s>"						,      _A					:					str="<pad>"						,      _A					:					Optional[int]="[SEP]"						,      _A					:					int="[MASK]"						,      _A					:					int="[CLS]"						,      _A					:					Optional[int] = None						,      **_A					:					Optional[Any]						,      )							->			Tuple:







							"""simple docstring"""


							snake_case_						:	Optional[int]      =		AddedToken(__lowerCAmelCase						,      lstrip=__lowerCAmelCase						,      rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,      __lowerCAmelCase		) else bos_token
							snake_case_						:	List[Any]      =		AddedToken(__lowerCAmelCase						,      lstrip=__lowerCAmelCase						,      rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,      __lowerCAmelCase		) else eos_token
							snake_case_						:	Optional[Any]      =		AddedToken(__lowerCAmelCase						,      lstrip=__lowerCAmelCase						,      rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,      __lowerCAmelCase		) else unk_token
							snake_case_						:	List[str]      =		AddedToken(__lowerCAmelCase						,      lstrip=__lowerCAmelCase						,      rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,      __lowerCAmelCase		) else pad_token
							snake_case_						:	Optional[int]      =		AddedToken(__lowerCAmelCase						,      lstrip=__lowerCAmelCase						,      rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,      __lowerCAmelCase		) else cls_token
							snake_case_						:	Tuple      =		AddedToken(__lowerCAmelCase						,      lstrip=__lowerCAmelCase						,      rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,      __lowerCAmelCase		) else sep_token

							# Mask token behave like a normal word, i.e. include the space before it
							snake_case_						:	int      =		AddedToken(__lowerCAmelCase						,      lstrip=__lowerCAmelCase						,      rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,      __lowerCAmelCase		) else mask_token

							snake_case_						:	List[Any]      =		{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__lowerCAmelCase						,      eos_token=__lowerCAmelCase						,      unk_token=__lowerCAmelCase						,      pad_token=__lowerCAmelCase						,      sep_token=__lowerCAmelCase						,      mask_token=__lowerCAmelCase						,      cls_token=__lowerCAmelCase						,      sp_model_kwargs=self.sp_model_kwargs						,      **__lowerCAmelCase						,      )

							snake_case_						:	Optional[int]      =		vocab_file

							snake_case_						:	Optional[Any]      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(__lowerCAmelCase		)






						@property
						def UpperCAmelCase_ (					self					:					Dict		)							->			int:







							"""simple docstring"""


							return self.sp_model.get_piece_size()






						def UpperCAmelCase_ (					self					:					List[Any]		)							->			Optional[int]:







							"""simple docstring"""


							snake_case_						:	Union[str, Any]      =		{self.convert_ids_to_tokens(__lowerCAmelCase		): i for i in range(self.vocab_size		)}
							vocab.update(self.added_tokens_encoder		)
							return vocab






						def __getstate__(					self					:					Tuple		)							->			Dict:







							"""simple docstring"""


							snake_case_						:	List[str]      =		self.__dict__.copy()
							snake_case_						:	List[str]      =		None
							return state






						def __setstate__(					self					:					Any						,      _A					:					Any		)							->			Dict:







							"""simple docstring"""


							snake_case_						:	Any      =		d

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

							snake_case_						:	Tuple      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(self.vocab_file		)






						def UpperCAmelCase_ (					self					:					int						,      _A					:					Tuple		)							->			int:







							"""simple docstring"""


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






						def UpperCAmelCase_ (					self					:					int						,      _A					:					Union[str, Any]		)							->			Optional[int]:







							"""simple docstring"""


							return self.sp_model.piece_to_id(__lowerCAmelCase		)






						def UpperCAmelCase_ (					self					:					List[str]						,      _A					:					Tuple		)							->			str:







							"""simple docstring"""


							snake_case_						:	int      =		self.sp_model.IdToPiece(__lowerCAmelCase		)
							return token






						def UpperCAmelCase_ (					self					:					List[str]						,      _A					:					Dict		)							->			Optional[int]:







							"""simple docstring"""


							snake_case_						:	Optional[int]      =		[]
							snake_case_						:	List[Any]      =		''
							snake_case_						:	Tuple      =		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(__lowerCAmelCase		) + token
									snake_case_						:	int      =		True
									snake_case_						:	List[str]      =		[]
								else:
									current_sub_tokens.append(__lowerCAmelCase		)
									snake_case_						:	Union[str, Any]      =		False
							out_string += self.sp_model.decode(__lowerCAmelCase		)
							return out_string.strip()






						def UpperCAmelCase_ (					self					:					str						,      _A					:					List[str]						,      _A					:					Dict = False						,      _A					:					Optional[int] = None						,      _A					:					Optional[int] = True						,      **_A					:					str						,      )							->			Tuple:







							"""simple docstring"""


							snake_case_						:	Dict      =		kwargs.pop('use_source_tokenizer'						,      __lowerCAmelCase		)

							snake_case_						:	Tuple      =		self.convert_ids_to_tokens(__lowerCAmelCase						,      skip_special_tokens=__lowerCAmelCase		)

							# To avoid mixing byte-level and unicode for byte-level BPT
							# we need to build string separately for added tokens and byte-level tokens
							# cf. https://github.com/huggingface/transformers/issues/1133
							snake_case_						:	List[str]      =		[]
							snake_case_						:	int      =		[]
							for token in filtered_tokens:
								if skip_special_tokens and token in self.all_special_ids:
									continue
								if token in self.added_tokens_encoder:
									if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase		)		)
										snake_case_						:	List[Any]      =		[]
									sub_texts.append(__lowerCAmelCase		)
								else:
									current_sub_text.append(__lowerCAmelCase		)
							if current_sub_text:
								sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase		)		)

							# Mimic the behavior of the Rust tokenizer:
							# No space before [MASK] and [SEP]
							if spaces_between_special_tokens:
								snake_case_						:	Union[str, Any]      =		re.sub(R' (\[(MASK|SEP)\])'						,      R'\1'						,      ' '.join(__lowerCAmelCase		)		)
							else:
								snake_case_						:	List[Any]      =		''.join(__lowerCAmelCase		)

							snake_case_						:	List[str]      =		(
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
								snake_case_						:	Any      =		self.clean_up_tokenization(__lowerCAmelCase		)
								return clean_text
							else:
								return text






						def UpperCAmelCase_ (					self					:					Union[str, Any]						,      _A					:					Any						,      _A					:					List[Any] = None		)							->			Dict:







							"""simple docstring"""


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

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

							return (out_vocab_file,)






						def UpperCAmelCase_ (					self					:					Union[str, Any]						,      _A					:					Optional[int]						,      _A					:					Optional[Any] = None		)							->			Any:







							"""simple docstring"""


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






						def UpperCAmelCase_ (					self					:					int						,      _A					:					int						,      _A					:					Union[str, Any] = None						,      _A					:					Any = False		)							->			Dict:







							"""simple docstring"""


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

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






						def UpperCAmelCase_ (					self					:					int						,      _A					:					Optional[int]						,      _A					:					str = None		)							->			Optional[Any]:







							"""simple docstring"""


							snake_case_						:	Dict      =		[self.sep_token_id]
							snake_case_						:	List[Any]      =		[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		) * [0] + len(token_ids_a + sep		) * [1]


"""simple docstring"""






def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Optional[Any]		) ->    int:
      '''simple docstring'''


      lowercase														=	[0 for i in range(r + 1		)]
      # nc0 = 1
      lowercase														=	1
      for i in range(1					,		n + 1		):
            # to compute current row from previous row.
            lowercase														=	min(lowerCAmelCase__					,		lowerCAmelCase__		)
            while j > 0:
                  c[j] += c[j - 1]
                  j -= 1
      return c[r]






print(binomial_coefficient(n=1_0, r=5))



