





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


_lowercase		:  List[str]			       ="▁"

_lowercase		:  int			       ={"vocab_file": "spiece.model"}

_lowercase		:  Any			       ={
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_lowercase		:  Any			       ={
    "google/pegasus-xsum": 512,
}


_lowercase		:  Union[str, Any]			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  lowercase_							):
					_a      :						str						    =					VOCAB_FILES_NAMES

					_a      :						List[str]						    =					VOCAB_FILES_NAMES
					_a      :						Optional[int]						    =					PRETRAINED_VOCAB_FILES_MAP
					_a      :						Optional[int]						    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a      :						List[str]						    =					['input_ids', 'attention_mask']



					def __init__(		self     :     List[str]       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     Dict="<pad>"       ,      lowerCamelCase     :     Dict="</s>"       ,      lowerCamelCase     :     Optional[Any]="<unk>"       ,      lowerCamelCase     :     List[Any]="<mask_2>"       ,      lowerCamelCase     :     List[Any]="<mask_1>"       ,      lowerCamelCase     :     str=None       ,      lowerCamelCase     :     Dict=1_03       ,      lowerCamelCase     :     Optional[Dict[str, Any]] = None       ,      **lowerCamelCase     :     int       ,      ):
							lowerCamelCase_	:     Optional[int] 						=      offset
							if additional_special_tokens is not None:
									if not isinstance(UpperCamelCase_       ,      UpperCamelCase_						):
											raise TypeError(
											    F"additional_special_tokens should be of type {type(UpperCamelCase_						)}, but is"
											    F" {type(UpperCamelCase_						)}"						)

									lowerCamelCase_	:     Tuple 						=      (
									    ([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(UpperCamelCase_						)       ,      self.offset - 1						)
									]

									if len(set(UpperCamelCase_						)						) != len(UpperCamelCase_						):
											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_	:     Optional[int] 						=      additional_special_tokens_extended
							else:
									lowerCamelCase_	:     str 						=      [mask_token_sent] if mask_token_sent is not None else []
									additional_special_tokens += [F"<unk_{i}>" for i in range(2       ,      self.offset						)]

							lowerCamelCase_	:     Any 						=      {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=UpperCamelCase_       ,      unk_token=UpperCamelCase_       ,      mask_token=UpperCamelCase_       ,      pad_token=UpperCamelCase_       ,      mask_token_sent=UpperCamelCase_       ,      offset=UpperCamelCase_       ,      additional_special_tokens=UpperCamelCase_       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **UpperCamelCase_       ,      )
							lowerCamelCase_	:     Union[str, Any] 						=      mask_token_sent
							lowerCamelCase_	:     List[Any] 						=      vocab_file
							lowerCamelCase_	:     Any 						=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(UpperCamelCase_						)

							# add special tokens to encoder dict
							lowerCamelCase_	:     List[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						)}						)

							lowerCamelCase_	:     Optional[int] 						=      {v: k for k, v in self.encoder.items()}



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



					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     List[str] 						=      {self.convert_ids_to_tokens(UpperCamelCase_						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab



					def __getstate__(		self     :     Tuple						):
							lowerCamelCase_	:     int 						=      self.__dict__.copy()
							lowerCamelCase_	:     Any 						=      None
							return state



					def __setstate__(		self     :     Tuple       ,      lowerCamelCase     :     Dict						):
							lowerCamelCase_	:     Tuple 						=      d

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

							lowerCamelCase_	:     Any 						=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(self.vocab_file						)



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     str						):
							return self.sp_model.encode(UpperCamelCase_       ,      out_type=UpperCamelCase_						)



					def   __a     (		self     :     str       ,      lowerCamelCase     :     str						):



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



					def   __a     (		self     :     int       ,      lowerCamelCase     :     int						):



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



					def   __a     (		self     :     Optional[int]       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     Dict 						=      []
							lowerCamelCase_	:     List[str] 						=      ''
							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(UpperCamelCase_						) + token
											lowerCamelCase_	:     Optional[Any] 						=      []
									else:
											current_sub_tokens.append(UpperCamelCase_						)
							out_string += self.sp_model.decode(UpperCamelCase_						)
							return out_string.strip()



					def   __a     (		self     :     Any       ,      lowerCamelCase     :     List[Any]=False						):
							return 1



					def   __a     (		self     :     Optional[int]       ,      lowerCamelCase     :     Tuple						):
							lowerCamelCase_	:     Any 						=      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   __a     (		self     :     int       ,      lowerCamelCase     :     List       ,      lowerCamelCase     :     Optional[List] = None       ,      lowerCamelCase     :     bool = False						):



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



					def   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Optional[Any]=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   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Optional[str] = None						):



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

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

							return (out_vocab_file,)








from __future__ import annotations

from typing import Generic, TypeVar

__a   :							str						= TypeVar("T")


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Any			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						data
         __A       =						self
         __A       =						0


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						{}




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						DisjointSetTreeNode(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						self.map[data]
         if elem_ref != elem_ref.parent:
             __A       =						self.find_set(elem_ref.parent.data					)
         return elem_ref.parent




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      DisjointSetTreeNode[T]			,						UpperCamelCase_							:      DisjointSetTreeNode[T]					):


         """simple docstring"""



         if nodea.rank > nodea.rank:
             __A       =						nodea
         else:
             __A       =						nodea
             if nodea.rank == nodea.rank:
                 nodea.rank += 1





     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         self.link(self.find_set(UpperCamelCase_					)			,						self.find_set(UpperCamelCase_					)					)


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						{}




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         if node not in self.connections:
             __A       =						{}




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         self.add_node(UpperCamelCase_					)
         self.add_node(UpperCamelCase_					)
         __A       =						weight
         __A       =						weight





     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						[]
         __A       =						set()
         for start in self.connections:
             for end in self.connections[start]:
                 if (start, end) not in seen:
                     seen.add((end, start)					)
                     edges.append((start, end, self.connections[start][end])					)
         edges.sort(key=lambda UpperCamelCase_					: x[2]					)

         # creating the disjoint set
         __A       =						DisjointSetTree[T]()
         for node in self.connections:
             disjoint_set.make_set(UpperCamelCase_					)

         # MST generation
         __A       =						0
         __A       =						0
         __A       =						GraphUndirectedWeighted[T]()
         while num_edges < len(self.connections					) - 1:
             __A     ,						__A     ,						__A       =						edges[index]
             index += 1
             __A       =						disjoint_set.find_set(UpperCamelCase_					)
             __A       =						disjoint_set.find_set(UpperCamelCase_					)
             if parent_u != parent_v:
                 num_edges += 1
                 graph.add_edge(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
                 disjoint_set.union(UpperCamelCase_			,						UpperCamelCase_					)
         return graph




