



"""simple docstring"""

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


lowercase__    :  List[Any]							    =	logging.get_logger(__name__)

lowercase__    :  Any							    =	{"tokenizer_file": "tokenizer.json"}

lowercase__    :  Optional[int]							    =	{
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}




class        _UpperCAmelCase       (			_SCREAMING_SNAKE_CASE):
 _lowerCAmelCase   :			List[str]							=		VOCAB_FILES_NAMES
 _lowerCAmelCase   :			Dict							=		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase   :			Optional[Any]							=		["input_ids", "attention_mask"]
 _lowerCAmelCase   :			Union[str, Any]							=		None

 def __init__(     self		:       int  ,     lowercase_		:       Optional[Any]=None  ,     lowercase_		:       Any=None  ,     lowercase_		:       Dict=None  ,     lowercase_		:       str="<unk>"  ,     lowercase_		:       Union[str, Any]="<s>"  ,     lowercase_		:       Optional[Any]="</s>"  ,     lowercase_		:       str="<pad>"  ,     lowercase_		:       Optional[int]=False  ,     lowercase_		:       Tuple=False  ,     **lowercase_		:       List[Any]  ,     ):
     super().__init__(
         A_  ,     A_  ,     tokenizer_file=A_  ,     unk_token=A_  ,     bos_token=A_  ,     eos_token=A_  ,     pad_token=A_  ,     add_prefix_space=A_  ,     clean_up_tokenization_spaces=A_  ,     **A_  ,     )
     snake_case_ :     Dict				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
     if pre_tok_state.get('''add_prefix_space'''  ,     A_				) != add_prefix_space:
         snake_case_ :     List[str]				=	getattr(A_  ,     pre_tok_state.pop('''type'''				)				)
         snake_case_ :     Optional[Any]				=	add_prefix_space
         snake_case_ :     Union[str, Any]				=	pre_tok_class(**A_				)

     snake_case_ :     Tuple				=	add_prefix_space

 def 						_snake_case					(     self		:       Any  ,     *lowercase_		:       Dict  ,     **lowercase_		:       Optional[int]				):
     snake_case_ :     List[Any]				=	kwargs.get('''is_split_into_words'''  ,     A_				)
     if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
             ''' pretokenized inputs.'''				)

     return super()._batch_encode_plus(*A_  ,     **A_				)

 def 						_snake_case					(     self		:       Optional[int]  ,     *lowercase_		:       Optional[Any]  ,     **lowercase_		:       str				):
     snake_case_ :     Union[str, Any]				=	kwargs.get('''is_split_into_words'''  ,     A_				)

     if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
             ''' pretokenized inputs.'''				)

     return super()._encode_plus(*A_  ,     **A_				)

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Dict  ,     lowercase_		:       Any = None				):
     snake_case_ :     str				=	self._tokenizer.model.save(A_  ,     name=A_				)
     return tuple(A_				)






 def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       List[Any]				):
     snake_case_ :     Tuple				=	[]
     for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(A_  ,     add_special_tokens=A_				) + [self.eos_token_id]				)

     if len(A_				) > self.model_max_length:
         snake_case_ :     Optional[Any]				=	input_ids[-self.model_max_length :]
     return input_ids







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


_UpperCAmelCase					: List[Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: Dict      =       {"vocab_file": "spiece.model"}

_UpperCAmelCase					: Dict      =       {
    "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"
        ),
    }
}

_UpperCAmelCase					: int      =       {
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				VOCAB_FILES_NAMES
		__lowercase       :       int	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       List[Any]	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       List[Any]	     =				["input_ids", "attention_mask"]
		__lowercase       :       List[int]	     =				[]



		def __init__( self				,      A_				,      A_="<unk>"				,      A_="<s>"				,      A_="</s>"				,      A_="<pad>"				,      A_="[SEP]"				,      A_="[MASK]"				,      A_="[CLS]"				,      A_ = None				,      **A_				,      )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else bos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else eos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else unk_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else pad_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else cls_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else sep_token

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

				UpperCamelCase		=     {} if sp_model_kwargs is None else sp_model_kwargs

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

				UpperCamelCase		=     vocab_file

				UpperCamelCase		=     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(A_       )



		@property
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				return self.sp_model.get_piece_size()



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     {self.convert_ids_to_tokens(A_       ): i for i in range(self.vocab_size       )}
				vocab.update(self.added_tokens_encoder       )
				return vocab



		def __getstate__( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     self.__dict__.copy()
				UpperCamelCase		=     None
				return state



		def __setstate__( self				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     d

				# for backward compatibility
				if not hasattr(self				,      'sp_model_kwargs'       ):
						UpperCamelCase		=     {}

				UpperCamelCase		=     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(self.vocab_file       )



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				return self.sp_model.encode(A_				,      out_type=A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return self.sp_model.piece_to_id(A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.sp_model.IdToPiece(A_       )
				return token



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     []
				UpperCamelCase		=     ''
				UpperCamelCase		=     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
								UpperCamelCase		=     True
								UpperCamelCase		=     []
						else:
								current_sub_tokens.append(A_       )
								UpperCamelCase		=     False
				out_string += self.sp_model.decode(A_       )
				return out_string.strip()



		def __UpperCamelCase ( self				,      A_				,      A_ = False				,      A_ = None				,      A_ = True				,      **A_				,      )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     kwargs.pop('use_source_tokenizer'				,      A_       )

				UpperCamelCase		=     self.convert_ids_to_tokens(A_				,      skip_special_tokens=A_       )

				# 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
				UpperCamelCase		=     []
				UpperCamelCase		=     []
				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(A_       )       )
										UpperCamelCase		=     []
								sub_texts.append(A_       )
						else:
								current_sub_text.append(A_       )
				if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(A_       )       )

				# Mimic the behavior of the Rust tokenizer:
				# No space before [MASK] and [SEP]
				if spaces_between_special_tokens:
						UpperCamelCase		=     re.sub(r' (\[(MASK|SEP)\])'				,      r'\1'				,      ' '.join(A_       )       )
				else:
						UpperCamelCase		=     ''.join(A_       )

				UpperCamelCase		=     (
				    clean_up_tokenization_spaces
				    if clean_up_tokenization_spaces is not None
				    else self.clean_up_tokenization_spaces
				)
				if clean_up_tokenization_spaces:
						UpperCamelCase		=     self.clean_up_tokenization(A_       )
						return clean_text
				else:
						return text



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_       ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
						return
				UpperCamelCase		=     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:
								UpperCamelCase		=     self.sp_model.serialized_model_proto()
								fi.write(A_       )

				return (out_vocab_file,)



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase		=     [self.cls_token_id]
				UpperCamelCase		=     [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      A_ = False       )  ->					List[int]:
				"""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] + ([0] * len(A_       )) + [1]



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				UpperCamelCase		=     [self.sep_token_id]
				UpperCamelCase		=     [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]
