




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:
 lowerCamelCase				:		Optional[Any]     =   None


lowerCamelCase				:		Any     =   logging.get_logger(__name__)


lowerCamelCase				:		Tuple     =   "▁"

lowerCamelCase				:		Optional[Any]     =   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase				:		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"
    },
}

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







class   A(						__SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 UpperCamelCase						   =						VOCAB_FILES_NAMES
 UpperCamelCase						   =						PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						   =						PegasusTokenizer
 UpperCamelCase						   =						['''input_ids''', '''attention_mask''']

 def __init__(				self					:  Union[str, Any]						,							A_					:  List[Any]=None						,							A_					:  int=None						,							A_					:  Optional[int]="<pad>"						,							A_					:  str="</s>"						,							A_					:  str="<unk>"						,							A_					:  int="<mask_2>"						,							A_					:  Any="<mask_1>"						,							A_					:  int=None						,							A_					:  int=103						,							**A_					:  Dict						,							)   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    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_		     =    (
                 ([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_		     =    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__(
           UpperCAmelCase_						,							tokenizer_file=UpperCAmelCase_						,							pad_token=UpperCAmelCase_						,							eos_token=UpperCAmelCase_						,							unk_token=UpperCAmelCase_						,							mask_token=UpperCAmelCase_						,							mask_token_sent=UpperCAmelCase_						,							offset=UpperCAmelCase_						,							additional_special_tokens=UpperCAmelCase_						,							**UpperCAmelCase_						,							)
       lowerCamelCase_		     =    vocab_file
       lowerCamelCase_		     =    False if not self.vocab_file else True

 def 				a__ (				self					:  Tuple						,							A_					:  Tuple  )   ->	int:

       """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					:  str						,							A_					:  List						,							A_					:  Optional[List] = None						,							A_					:  bool = False  )   ->	List[int]:

       """simple docstring"""






       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					:  int						,							A_					:  List[Any]						,							A_					:  str=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					:  List[str]						,							A_					:  str						,							A_					:  Optional[str] = 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(UpperCAmelCase_  ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
             return
       lowerCamelCase_		     =    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_  ):
             copyfile(self.vocab_file						,							UpperCAmelCase_  )

       return (out_vocab_file,)










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()

