





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


__A									=				logging.get_logger(__name__)

__A									=				{"vocab_file": "spiece.model"}

__A									=				{
    "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"
        ),
    }
}

__A									=				{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			["input_ids", "attention_mask"]
 lowercase_		=			[]

 def __init__(self				:   Tuple       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   List[str]="<unk>"       , UpperCAmelCase_				:   Union[str, Any]="<s>"       , UpperCAmelCase_				:   int="</s>"       , UpperCAmelCase_				:   List[str]="<pad>"       , UpperCAmelCase_				:   str="[SEP]"       , UpperCAmelCase_				:   List[str]="[MASK]"       , UpperCAmelCase_				:   Dict="[CLS]"       , UpperCAmelCase_				:   Optional[Dict[str, Any]] = None       , **UpperCAmelCase_				:   int       , )       ->None:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =AddedToken(UpperCAmelCase_       , lstrip=UpperCAmelCase_       , rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_       , UpperCAmelCase_) else bos_token
   lowerCamelCase__:      int			  =AddedToken(UpperCAmelCase_       , lstrip=UpperCAmelCase_       , rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_       , UpperCAmelCase_) else eos_token
   lowerCamelCase__:      Any			  =AddedToken(UpperCAmelCase_       , lstrip=UpperCAmelCase_       , rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_       , UpperCAmelCase_) else unk_token
   lowerCamelCase__:      Union[str, Any]			  =AddedToken(UpperCAmelCase_       , lstrip=UpperCAmelCase_       , rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_       , UpperCAmelCase_) else pad_token
   lowerCamelCase__:      Any			  =AddedToken(UpperCAmelCase_       , lstrip=UpperCAmelCase_       , rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_       , UpperCAmelCase_) else cls_token
   lowerCamelCase__:      Any			  =AddedToken(UpperCAmelCase_       , lstrip=UpperCAmelCase_       , rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_       , UpperCAmelCase_) else sep_token

   # Mask token behave like a normal word, i.e. include the space before it
   lowerCamelCase__:      Optional[int]			  =AddedToken(UpperCAmelCase_       , lstrip=UpperCAmelCase_       , rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_       , UpperCAmelCase_) else mask_token

   lowerCamelCase__:      Optional[int]			  ={} if sp_model_kwargs is None else sp_model_kwargs

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

   lowerCamelCase__:      Union[str, Any]			  =vocab_file

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

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->str:



   '''simple docstring'''



   return self.sp_model.get_piece_size()

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  ={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				:   Union[str, Any])       ->str:



   '''simple docstring'''



   lowerCamelCase__:      int			  =self.__dict__.copy()
   lowerCamelCase__:      Any			  =None
   return state

 def __setstate__(self				:   Dict       , UpperCAmelCase_				:   Any)       ->int:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =d

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

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   str)       ->List[str]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   Optional[Any])       ->Dict:



   '''simple docstring'''



   return self.sp_model.piece_to_id(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   List[str])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.sp_model.IdToPiece(UpperCAmelCase_)
   return token

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   Dict)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =[]
   lowerCamelCase__:      Any			  =""
   lowerCamelCase__:      Any			  =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
       lowerCamelCase__:      str			  =True
       lowerCamelCase__:      List[str]			  =[]
     else:
       current_sub_tokens.append(UpperCAmelCase_)
       lowerCamelCase__:      Optional[Any]			  =False
   out_string += self.sp_model.decode(UpperCAmelCase_)
   return out_string.strip()

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   bool = False       , UpperCAmelCase_				:   bool = None       , UpperCAmelCase_				:   bool = True       , **UpperCAmelCase_				:   Any       , )       ->str:



   '''simple docstring'''



   lowerCamelCase__:      str			  =kwargs.pop("use_source_tokenizer"       , UpperCAmelCase_)

   lowerCamelCase__:      List[str]			  =self.convert_ids_to_tokens(UpperCAmelCase_       , skip_special_tokens=UpperCAmelCase_)

   # 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
   lowerCamelCase__:      Union[str, Any]			  =[]
   lowerCamelCase__:      List[Any]			  =[]
   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(UpperCAmelCase_))
         lowerCamelCase__:      Tuple			  =[]
       sub_texts.append(UpperCAmelCase_)
     else:
       current_sub_text.append(UpperCAmelCase_)
   if current_sub_text:
     sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase_))

   # Mimic the behavior of the Rust tokenizer:
   # No space before [MASK] and [SEP]
   if spaces_between_special_tokens:
     lowerCamelCase__:      int			  =re.sub(R" (\[(MASK|SEP)\])"       , R"\1"       , " ".join(UpperCAmelCase_))
   else:
     lowerCamelCase__:      Any			  ="".join(UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =(
       clean_up_tokenization_spaces
       if clean_up_tokenization_spaces is not None
       else self.clean_up_tokenization_spaces
   )
   if clean_up_tokenization_spaces:
     lowerCamelCase__:      int			  =self.clean_up_tokenization(UpperCAmelCase_)
     return clean_text
   else:
     return text

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   if not os.path.isdir(UpperCAmelCase_):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
     return
   lowerCamelCase__:      int			  =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__:      Any			  =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   return (out_vocab_file,)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   lowerCamelCase__:      List[Any]			  =[self.cls_token_id]
   lowerCamelCase__:      List[str]			  =[self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None       , UpperCAmelCase_				:   bool = False)       ->List[int]:



   '''simple docstring'''



   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 None:
     return [1] + ([0] * len(UpperCAmelCase_)) + [1]
   return [1] + ([0] * len(UpperCAmelCase_)) + [1] + ([0] * len(UpperCAmelCase_)) + [1]







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =[self.sep_token_id]
   lowerCamelCase__:      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]








from typing import Any





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		list:







  """simple docstring"""





  _validation(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  # Creates data structures and fill initial step
  lowerCamelCase__:      dict			  ={}
  lowerCamelCase__:      dict			  ={}
  for state in states_space:
    lowerCamelCase__:      Optional[Any]			  =observations_space[0]
    lowerCamelCase__:      List[Any]			  =(
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    lowerCamelCase__:      int			  =None

  # Fills the data structure with the probabilities of
  # different transitions and pointers to previous states
  for o in range(1	,    len(__a		)		):
    lowerCamelCase__:      Tuple			  =observations_space[o]
    lowerCamelCase__:      Optional[Any]			  =observations_space[o - 1]
    for state in states_space:
      # Calculates the argmax for probability function
      lowerCamelCase__:      Tuple			  =""
      lowerCamelCase__:      Optional[Any]			  =-1
      for k_state in states_space:
        lowerCamelCase__:      int			  =(
            probabilities[(k_state, prior_observation)]
            * transition_probabilities[k_state][state]
            * emission_probabilities[state][observation]
        )
        if probability > max_probability:
          lowerCamelCase__:      List[str]			  =probability
          lowerCamelCase__:      int			  =k_state

            # Update probabilities and pointers dicts
      lowerCamelCase__:      Any			  =(
          probabilities[(arg_max, prior_observation)]
          * transition_probabilities[arg_max][state]
          * emission_probabilities[state][observation]
      )

      lowerCamelCase__:      int			  =arg_max

    # The final observation
  lowerCamelCase__:      Any			  =observations_space[len(__a		) - 1]

  # argmax for given final observation
  lowerCamelCase__:      Optional[Any]			  =""
  lowerCamelCase__:      int			  =-1
  for k_state in states_space:
    lowerCamelCase__:      Tuple			  =probabilities[(k_state, final_observation)]
    if probability > max_probability:
      lowerCamelCase__:      List[Any]			  =probability
      lowerCamelCase__:      Dict			  =k_state
  lowerCamelCase__:      str			  =arg_max

  # Process pointers backwards
  lowerCamelCase__:      Union[str, Any]			  =last_state
  lowerCamelCase__:      List[str]			  =[]
  for o in range(len(__a		) - 1	,    -1	,    -1		):
    result.append(__a		)
    lowerCamelCase__:      Union[str, Any]			  =pointers[previous, observations_space[o]]
  result.reverse()

  return result





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_not_empty(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  _validate_lists(__a	,    __a		)
  _validate_dicts(
      __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]		):
    raise ValueError("There's an empty parameter"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_list(__a	,    "observations_space"		)
  _validate_list(__a	,    "states_space"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} must be a list"""
    raise ValueError(__a		)
  else:
    for x in _object:
      if not isinstance(__a	,    __a		):
        lowerCamelCase__:      str			  =F"""{var_name} must be a list of strings"""
        raise ValueError(__a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_dict(__a	,    "initial_probabilities"	,    __a		)
  _validate_nested_dict(__a	,    "transition_probabilities"		)
  _validate_nested_dict(__a	,    "emission_probabilities"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_dict(_object	,    __a	,    __a		)
  for x in _object.values():
    _validate_dict(__a	,    __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a = False		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Optional[int]			  =F"""{var_name} must be a dict"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} all keys must be strings"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object.values()		):
    lowerCamelCase__:      Dict			  ="nested dictionary " if nested else ""
    lowerCamelCase__:      List[str]			  =F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
    raise ValueError(__a		)


if __name__ == "__main__":
     from doctest import testmod

     testmod()

