


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase   :    List[str]     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[int]     =		"▁"

UpperCAmelCase   :    Optional[int]     =		{"vocab_file": "spiece.model"}

UpperCAmelCase   :    List[Any]     =		{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

UpperCAmelCase   :    int     =		{
    "google/reformer-crime-and-punishment": 52_42_88,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[Any]			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					Optional[int]			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					Optional[int]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Optional[Any]			       =		["input_ids", "attention_mask"]







		def __init__( self		,      A		,      A="</s>"		,      A="<unk>"		,      A=[]		,      A = None		,      **A		,      )  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    eos_token=A		,      unk_token=A		,      additional_special_tokens=A		,      sp_model_kwargs=self.sp_model_kwargs		,      **A		,      )

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







		@property
		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					return self.sp_model.get_piece_size()







		def 					__A   ( self	)  -> Dict[str, int]:







					'''simple docstring'''







					lowerCamelCase    		=  {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	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  self.__dict__.copy()
					lowerCamelCase    		=  None
					return state







		def __setstate__( self		,      A	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  d

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

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







		def 					__A   ( self		,      A	)  -> List[str]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A	)  -> str:







					'''simple docstring'''







					return self.sp_model.piece_to_id(A	)







		def 					__A   ( self		,      A	)  -> str:







					'''simple docstring'''







					if index < self.sp_model.get_piece_size():
								lowerCamelCase    		=  self.sp_model.IdToPiece(A	)
					return token







		def 					__A   ( self		,      A	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  []
					lowerCamelCase    		=  """"""
					for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
											out_string += self.sp_model.decode(A	) + token
											lowerCamelCase    		=  []
								else:
											current_sub_tokens.append(A	)
					out_string += self.sp_model.decode(A	)
					return out_string.strip()







		def 					__A   ( 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
					lowerCamelCase    		=  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:
											lowerCamelCase    		=  self.sp_model.serialized_model_proto()
											fi.write(A	)

					return (out_vocab_file,)





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

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


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCAmelCase   :    Union[str, Any]     =		None


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[Any]     =		"▁"

UpperCAmelCase   :    str     =		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase   :    Dict     =		{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase   :    List[Any]     =		{
    "google/pegasus-xsum": 5_12,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					int			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					List[Any]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Any			       =		PegasusTokenizer
		UpperCamelCase     :					Optional[Any]			       =		["input_ids", "attention_mask"]







		def __init__( self		,      A=None		,      A=None		,      A="<pad>"		,      A="</s>"		,      A="<unk>"		,      A="<mask_2>"		,      A="<mask_1>"		,      A=None		,      A=1_03		,      **A		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  offset

					if additional_special_tokens is not None:
								if not isinstance(A		,      A	):
											raise TypeError(
											    F'additional_special_tokens should be of type {type(A	)}, but is'
											    F' {type(A	)}'	)

								lowerCamelCase    		=  (
								    ([mask_token_sent] + additional_special_tokens)
								    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
								    else additional_special_tokens
								)
								# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
								additional_special_tokens_extended += [
								    F'<unk_{i}>' for i in range(len(A	)		,      self.offset - 1	)
								]

								if len(set(A	)	) != len(A	):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'	)
								lowerCamelCase    		=  additional_special_tokens_extended
					else:
								lowerCamelCase    		=  [mask_token_sent] if mask_token_sent is not None else []
								additional_special_tokens += [F'<unk_{i}>' for i in range(2		,      self.offset	)]

					super().__init__(
					    A		,      tokenizer_file=A		,      pad_token=A		,      eos_token=A		,      unk_token=A		,      mask_token=A		,      mask_token_sent=A		,      offset=A		,      additional_special_tokens=A		,      **A		,      )
					lowerCamelCase    		=  vocab_file
					lowerCamelCase    		=  False if not self.vocab_file else True







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  set(self.all_special_ids	)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
								raise ValueError(
								    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
								    F' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'	)

					return [1 if x in all_special_ids else 0 for x in seq]







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







					if already_has_special_tokens:
								return self._special_token_mask(A	)
					elif token_ids_a is None:
								return self._special_token_mask(A	) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







		def 					__A   ( self		,      A		,      A=None	)  -> List[int]:







					'''simple docstring'''







					if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''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(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  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	):
								copyfile(self.vocab_file		,      A	)

					return (out_vocab_file,)

