from .number_theory.prime_numbers import next_prime






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     )	->None:
        a  :    List[Any]				=						size_table
        a  :    Union[str, Any]				=						[None] * self.size_table
        a  :    List[Any]				=						0.75 if lim_charge is None else lim_charge
        a  :    List[str]				=						1 if charge_factor is None else charge_factor
        a  :    list				=						[]
        a  :    dict				=						{}
   def _lowercase(    self    )	->Optional[int]:
        return self._keys
   def _lowercase(    self    )	->Dict:
        return sum(1 for slot in self.values if slot is not None    ) / (
            self.size_table * self.charge_factor
        )
   def _lowercase(    self							,     lowerCAmelCase_    )	->int:
        return key % self.size_table
   def _lowercase(    self							,     lowerCAmelCase_    )	->Optional[Any]:
        print(F"""step {step_ord}"""    )
        print(list(range(len(self.values    )    )    )    )
        print(self.values    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->Optional[int]:
        a  :    Optional[Any]				=						1
        a  :    Dict				=						values
        for value in values:
             self.insert_data(lowerCAmelCase_    )
             self._step_by_step(lowerCAmelCase_    )
             i += 1
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Optional[int]:
        a  :    str				=						data
        a  :    Optional[int]				=						data
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=None    )	->int:
        a  :    str				=						self.hash_function(key + 1    )

        while self.values[new_key] is not None and self.values[new_key] != key:
             if self.values.count(lowerCAmelCase_    ) > 0:
                  a  :    int				=						self.hash_function(new_key + 1    )
             else:
                  a  :    Tuple				=						None
                  break

        return new_key
   def _lowercase(    self    )	->List[str]:
        a  :    Dict				=						[value for value in self.values if value is not None]
        a  :    Union[str, Any]				=						next_prime(self.size_table							,     factor=2    )
        self._keys.clear()
        a  :    Dict				=						[None] * self.size_table  # hell's pointers D: don't DRY ;/
        for value in survivor_values:
             self.insert_data(lowerCAmelCase_    )


   def _lowercase(    self							,     lowerCAmelCase_    )	->Tuple:
        a  :    List[Any]				=						self.hash_function(lowerCAmelCase_    )

        if self.values[key] is None:
             self._set_value(lowerCAmelCase_							,     lowerCAmelCase_    )

        elif self.values[key] == data:
             pass

        else:
             a  :    Union[str, Any]				=						self._collision_resolution(lowerCAmelCase_							,     lowerCAmelCase_    )
             if collision_resolution is not None:
                  self._set_value(lowerCAmelCase_							,     lowerCAmelCase_    )
             else:
                  self.rehashing()
                  self.insert_data(lowerCAmelCase_    )
# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/