

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


snake_case_        =			'''▁'''

snake_case_        =			{'''vocab_file''': '''spiece.model'''}

snake_case_        =			{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

snake_case_        =			{
    '''google/pegasus-xsum''': 512,
}


snake_case_        =			logging.get_logger(__name__)


class 				SCREAMING_SNAKE_CASE__ (lowerCamelCase__					):
    __lowerCamelCase						:	Optional[Any]														=					VOCAB_FILES_NAMES

    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	List[str]														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Optional[Any]														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	Tuple														=					["""input_ids""", """attention_mask"""]





    def __init__(				self						,       a						,       a="<pad>"						,       a="</s>"						,       a="<unk>"						,       a="<mask_2>"						,       a="<mask_1>"						,       a=None						,       a=103						,       a = None						,       **a						,       ):
         lowercase__   :       str				=	offset
         if additional_special_tokens is not None:
              if not isinstance(snake_case__						,       snake_case__):
                   raise TypeError(
                       f"""additional_special_tokens should be of type {type(snake_case__)}, but is"""
                       f""" {type(snake_case__)}""")

              lowercase__   :       Any				=	(
                  ([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(snake_case__)						,       self.offset - 1)
              ]

              if len(set(snake_case__)) != len(snake_case__):
                   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}.""")
              lowercase__   :       Tuple				=	additional_special_tokens_extended
         else:
              lowercase__   :       List[Any]				=	[mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [f"""<unk_{i}>""" for i in range(2						,       self.offset)]

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

         super().__init__(
             eos_token=snake_case__						,       unk_token=snake_case__						,       mask_token=snake_case__						,       pad_token=snake_case__						,       mask_token_sent=snake_case__						,       offset=snake_case__						,       additional_special_tokens=snake_case__						,       sp_model_kwargs=self.sp_model_kwargs						,       **snake_case__						,       )
         lowercase__   :       Dict				=	mask_token_sent
         lowercase__   :       str				=	vocab_file
         lowercase__   :       int				=	spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(snake_case__)

         # add special tokens to encoder dict
         lowercase__   :       Dict[int, str]				=	{
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
              self.encoder.update(
                  {
                      2: self.mask_token_sent,
                      3: self.mask_token,
                  })

         if self.offset > 0:
              # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
              # mask_token_sent is already added to list -> so start at 1
              self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1						,       self.offset - 1)})

         lowercase__   :       Dict[str, int]				=	{v: k for k, v in self.encoder.items()}





    @property
    def 				snake_case_    (				self):
         return len(self.sp_model) + self.offset





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	{self.convert_ids_to_tokens(snake_case__): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab





    def __getstate__(				self):
         lowercase__   :       Any				=	self.__dict__.copy()
         lowercase__   :       str				=	None
         return state





    def __setstate__(				self						,       a):
         lowercase__   :       Optional[Any]				=	d

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

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





    def 				snake_case_    (				self						,       a):
         return self.sp_model.encode(snake_case__						,       out_type=snake_case__)





    def 				snake_case_    (				self						,       a):





         if token in self.decoder:
              return self.decoder[token]
         elif token in self.added_tokens_decoder:
              return self.added_tokens_decoder[token]
         lowercase__   :       Optional[Any]				=	self.sp_model.piece_to_id(snake_case__)
         return sp_id + self.offset





    def 				snake_case_    (				self						,       a):





         if index in self.encoder:
              return self.encoder[index]
         elif index in self.added_tokens_encoder:
              return self.added_tokens_encoder[index]
         else:
              lowercase__   :       Optional[int]				=	self.sp_model.IdToPiece(index - self.offset)
         return token





    def 				snake_case_    (				self						,       a):
         lowercase__   :       str				=	[]
         lowercase__   :       int				=	''''''
         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(snake_case__) + token
                   lowercase__   :       Any				=	[]
              else:
                   current_sub_tokens.append(snake_case__)
         out_string += self.sp_model.decode(snake_case__)
         return out_string.strip()





    def 				snake_case_    (				self						,       a=False):
         return 1





    def 				snake_case_    (				self						,       a):
         lowercase__   :       int				=	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

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





    def 				snake_case_    (				self						,       a						,       a = None						,       a = False):





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





    def 				snake_case_    (				self						,       a						,       a=None):





         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 				snake_case_    (				self						,       a						,       a = None):





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

         if os.path.abspath(self.vocab_file) != os.path.abspath(snake_case__) and os.path.isfile(self.vocab_file):
              copyfile(self.vocab_file						,       snake_case__)
         elif not os.path.isfile(self.vocab_file):
              with open(snake_case__						,       'wb') as fi:
                   lowercase__   :       str				=	self.sp_model.serialized_model_proto()
                   fi.write(snake_case__)

         return (out_vocab_file,)




import numpy as np

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	int(np.ceil((x_end - xa) / h      )      )
     lowercase__   :       Dict				=	np.zeros((n + 1,)      )
     lowercase__   :       Dict				=	ya
     lowercase__   :       Union[str, Any]				=	xa

     for k in range(SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Optional[int]				=	f(SCREAMING_SNAKE_CASE_     ,					y[k]      )
          lowercase__   :       Dict				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       Any				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       List[Any]				=	f(x + h     ,					y[k] + h * ka      )
          lowercase__   :       Tuple				=	y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
          x += h

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()

