






"""simple docstring"""




import os
import unicodedata
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


_snake_case          =   logging.get_logger(__name__)
_snake_case          =   {'''vocab_file''': '''spiece.model'''}

_snake_case          =   {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

_snake_case          =   {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

_snake_case          =   '''▁'''






class        UpperCamelCase			(	__UpperCAmelCase      ):
 UpperCamelCase					:       Optional[Any]			=					VOCAB_FILES_NAMES
 UpperCamelCase					:       Optional[int]			=					PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase					:       Optional[Any]			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(     self		:						Tuple							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						str=True							,						UpperCAmelCase__		:						List[str]=True							,						UpperCAmelCase__		:						str=False							,						UpperCAmelCase__		:						Tuple="[CLS]"							,						UpperCAmelCase__		:						int="[SEP]"							,						UpperCAmelCase__		:						Dict="<unk>"							,						UpperCAmelCase__		:						Any="[SEP]"							,						UpperCAmelCase__		:						Union[str, Any]="<pad>"							,						UpperCAmelCase__		:						Union[str, Any]="[CLS]"							,						UpperCAmelCase__		:						Any="[MASK]"							,						UpperCAmelCase__		:						List[Any] = None							,						**UpperCAmelCase__		:						Optional[int]							,						)					->       str:
   # Mask token behave like a normal word, i.e. include the space before it and
   # is included in the raw text, there should be a match in a non-normalized sentence.
   _a  :       Tuple             =	(
       AddedToken(UpperCAmelCase__							,						lstrip=UpperCAmelCase__							,						rstrip=UpperCAmelCase__							,						normalized=UpperCAmelCase__							)
       if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							)
       else mask_token
   )

   _a  :       Union[str, Any]             =	{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=UpperCAmelCase__							,						remove_space=UpperCAmelCase__							,						keep_accents=UpperCAmelCase__							,						bos_token=UpperCAmelCase__							,						eos_token=UpperCAmelCase__							,						unk_token=UpperCAmelCase__							,						sep_token=UpperCAmelCase__							,						pad_token=UpperCAmelCase__							,						cls_token=UpperCAmelCase__							,						mask_token=UpperCAmelCase__							,						sp_model_kwargs=self.sp_model_kwargs							,						**UpperCAmelCase__							,						)

   _a  :       Any             =	do_lower_case
   _a  :       List[str]             =	remove_space
   _a  :       Tuple             =	keep_accents
   _a  :       Dict             =	vocab_file

   _a  :       Any             =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.Load(UpperCAmelCase__							)


 @property
 def _lowercase	(     self		:						Union[str, Any]							)					->       Optional[int]:
   return len(self.sp_model							)


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   _a  :       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		:						Any							)					->       Dict:
   _a  :       Tuple             =	self.__dict__.copy()
   _a  :       Optional[Any]             =	None
   return state


 def __setstate__(     self		:						int							,						UpperCAmelCase__		:						Dict							)					->       int:
   _a  :       Optional[int]             =	d

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

   _a  :       Union[str, Any]             =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.Load(self.vocab_file							)


 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						Tuple							)					->       Dict:
   if self.remove_space:
     _a  :       Any             =	""" """.join(inputs.strip().split()							)
   else:
     _a  :       Tuple             =	inputs
   _a  :       str             =	outputs.replace("""``"""							,						"""\""""							).replace("""''"""							,						"""\""""							)

   if not self.keep_accents:
     _a  :       str             =	unicodedata.normalize("""NFKD"""							,						UpperCAmelCase__							)
     _a  :       Dict             =	"""""".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase__							)]							)
   if self.do_lower_case:
     _a  :       Optional[Any]             =	outputs.lower()

   return outputs


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						Optional[int]							)					->       Any:
   _a  :       Optional[Any]             =	self.preprocess_text(UpperCAmelCase__							)
   _a  :       int             =	self.sp_model.encode(UpperCAmelCase__							,						out_type=UpperCAmelCase__							)
   _a  :       List[Any]             =	[]
   for piece in pieces:
     if len(UpperCAmelCase__							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
       _a  :       Dict             =	self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase__							,						""""""							)							)
       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
         if len(cur_pieces[0]							) == 1:
           _a  :       str             =	cur_pieces[1:]
         else:
           _a  :       int             =	cur_pieces[0][1:]
       cur_pieces.append(piece[-1]							)
       new_pieces.extend(UpperCAmelCase__							)
     else:
       new_pieces.append(UpperCAmelCase__							)

   return new_pieces


 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						Optional[int]							)					->       str:
   return self.sp_model.PieceToId(UpperCAmelCase__							)


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						Union[str, Any]							)					->       Tuple:
   return self.sp_model.IdToPiece(UpperCAmelCase__							)


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Any							)					->       List[Any]:
   _a  :       Any             =	[]
   _a  :       Optional[int]             =	""""""
   _a  :       Dict             =	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(UpperCAmelCase__							) + token
       _a  :       Optional[int]             =	True
       _a  :       int             =	[]
     else:
       current_sub_tokens.append(UpperCAmelCase__							)
       _a  :       int             =	False
   out_string += self.sp_model.decode(UpperCAmelCase__							)
   return out_string.strip()


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						List[str] = None							)					->       int:
   _a  :       List[Any]             =	[self.sep_token_id]
   _a  :       Optional[Any]             =	[self.cls_token_id]
   if token_ids_a is None:
     return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Union[str, Any]							,						UpperCAmelCase__		:						Optional[Any] = None							,						UpperCAmelCase__		:						Optional[Any] = False							)					->       str:

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

   if token_ids_a is not None:
     return [1] + ([0] * len(UpperCAmelCase__							)) + [1] + ([0] * len(UpperCAmelCase__							)) + [1]
   return [1] + ([0] * len(UpperCAmelCase__							)) + [1]


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Any = None							)					->       List[Any]:
   _a  :       List[str]             =	[self.sep_token_id]
   _a  :       Optional[int]             =	[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]







 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Optional[int] = None							)					->       Any:
   if not os.path.isdir(UpperCAmelCase__							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
   _a  :       List[Any]             =	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:
       _a  :       Any             =	self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase__							)

   return (out_vocab_file,)






'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""							)


	def     _a	(self							):
				for queue in self.queues:
							if queue:
										return queue.pop(0							)
				raise UnderFlowError("""All queues are empty"""							)


	def __str__(self							):
				return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues							)							)







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


	def     _a	(self       ,      lowercase							):
				if len(self.queue							) == 100:
							raise OverFlowError("""Maximum queue size is 100"""							)
				self.queue.append(lowercase							)


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


	def __str__(self							):
				return str(self.queue							)






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )



def 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()