




'''simple docstring'''


import sys



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							List[Any]     						=  len(UpperCamelCase__				)
     a_     :							Dict     						=  [[0 for x in range(UpperCamelCase__				)] for x in range(UpperCamelCase__				)]
     a_     :							int     						=  [[0 for x in range(UpperCamelCase__				)] for x in range(UpperCamelCase__				)]

     for chain_length in range(2 ,    UpperCamelCase__				):
          for a in range(1 ,    n - chain_length + 1				):
               a_     :							Optional[int]     						=  a + chain_length - 1

               a_     :							Optional[int]     						=  sys.maxsize
               for c in range(UpperCamelCase__ ,    UpperCamelCase__				):
                    a_     :							Union[str, Any]     						=  (
                        matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                    )
                    if cost < matrix[a][b]:
                         a_     :							Any     						=  cost
                         a_     :							Tuple     						=  c
     return matrix, sol



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     if i == j:
          print("""A""" + str(UpperCamelCase__				) ,    end=""" """				)
     else:
          print("""(""" ,    end=""" """				)
          print_optiomal_solution(UpperCamelCase__ ,    UpperCamelCase__ ,    optimal_solution[i][j]				)
          print_optiomal_solution(UpperCamelCase__ ,    optimal_solution[i][j] + 1 ,    UpperCamelCase__				)
          print(""")""" ,    end=""" """				)



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							List[Any]     						=  [30, 35, 15, 5, 10, 20, 25]
     a_     :							Optional[int]     						=  len(UpperCamelCase__				)
     # Size of matrix created from above array will be
     # 30*35 35*15 15*5 5*10 10*20 20*25
     a_						,     a_     :							List[str]     						=  matrix_chain_order(UpperCamelCase__				)

     print("""No. of Operation required: """ + str(matrix[1][n - 1]				)				)
     print_optiomal_solution(UpperCamelCase__ ,    1 ,    n - 1				)


if __name__ == "__main__":
 main()






'''simple docstring'''


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCAmelCase_      :  int				     =  logging.get_logger(__name__)


lowerCAmelCase_      :  Optional[Any]				     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_      :  List[str]				     =  {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_      :  Tuple				     =  {
    'allenai/led-base-16384': 1_6384,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       str   							=					VOCAB_FILES_NAMES
      __magic_name__  :       str   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       List[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       Optional[int]   							=					LEDTokenizer
      __magic_name__  :       Optional[int]   							=					['''input_ids''', '''attention_mask''']






      def __init__( self   :  str						,						lowercase__   :  int=None						,						lowercase__   :  Any=None						,						lowercase__   :  Optional[int]=None						,						lowercase__   :  List[Any]="replace"						,						lowercase__   :  Optional[Any]="<s>"						,						lowercase__   :  Optional[Any]="</s>"						,						lowercase__   :  Dict="</s>"						,						lowercase__   :  List[str]="<s>"						,						lowercase__   :  str="<unk>"						,						lowercase__   :  str="<pad>"						,						lowercase__   :  str="<mask>"						,						lowercase__   :  Union[str, Any]=False						,						lowercase__   :  Optional[int]=True						,						**lowercase__   :  str						,						):


           '''simple docstring'''

           super().__init__(
               lowercase__						,						lowercase__						,						tokenizer_file=lowercase__						,						errors=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						add_prefix_space=lowercase__						,						trim_offsets=lowercase__						,						**lowercase__						,						)

           a_     :							str     						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                a_     :							Optional[int]     						=  getattr(lowercase__						,						pre_tok_state.pop("""type"""       )       )
                a_     :							List[str]     						=  add_prefix_space
                a_     :							Union[str, Any]     						=  pre_tok_class(**lowercase__       )

           a_     :							Union[str, Any]     						=  add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           a_     :							Any     						=  """post_processor"""
           a_     :							int     						=  getattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )
           if tokenizer_component_instance:
                a_     :							Optional[int]     						=  json.loads(tokenizer_component_instance.__getstate__()       )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                     a_     :							int     						=  tuple(state["""sep"""]       )
                if "cls" in state:
                     a_     :							Optional[int]     						=  tuple(state["""cls"""]       )

                a_     :							List[Any]     						=  False

                if state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                     a_     :							Optional[Any]     						=  add_prefix_space
                     a_     :							Dict     						=  True

                if state.get("""trim_offsets"""						,						lowercase__       ) != trim_offsets:
                     a_     :							List[Any]     						=  trim_offsets
                     a_     :							Optional[Any]     						=  True

                if changes_to_apply:
                     a_     :							Optional[int]     						=  getattr(lowercase__						,						state.pop("""type"""       )       )
                     a_     :							Any     						=  component_class(**lowercase__       )
                     setattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )






      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           if self._mask_token is None:
                if self.verbose:
                     logger.error("""Using mask_token, but it is not set yet."""       )
                return None
           return str(self._mask_token       )






      @mask_token.setter
      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else value
           a_     :							Optional[int]     						=  value






      def 							lowercase_						( self   :  Optional[int]						,						*lowercase__   :  Any						,						**lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                    """to use it with pretokenized inputs."""       )

           return super()._batch_encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  List[Any]						,						*lowercase__   :  str						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                    """to use it with pretokenized inputs."""       )

           return super()._encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self._tokenizer.model.save(lowercase__						,						name=lowercase__       )
           return tuple(lowercase__       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  int=None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  [self.sep_token_id]
           a_     :							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 + sep + token_ids_a + sep       ) * [0]






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Union[Dict[str, EncodedInput], BatchEncoding]						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  Optional[bool] = None						,						):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  super()._pad(
               encoded_inputs=lowercase__						,						max_length=lowercase__						,						padding_strategy=lowercase__						,						pad_to_multiple_of=lowercase__						,						return_attention_mask=lowercase__						,						)

           # Load from model defaults
           if return_attention_mask is None:
                a_     :							Tuple     						=  """attention_mask""" in self.model_input_names

           if return_attention_mask and "global_attention_mask" in encoded_inputs:
                a_     :							Tuple     						=  encoded_inputs[self.model_input_names[0]]
                # `global_attention_mask` need to have the same length as other (sequential) inputs.
                a_     :							Optional[int]     						=  len(encoded_inputs["""global_attention_mask"""]       ) != len(lowercase__       )

                if needs_to_be_padded:
                     a_     :							int     						=  len(lowercase__       ) - len(encoded_inputs["""global_attention_mask"""]       )

                     if self.padding_side == "right":
                          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                          a_     :							Optional[int]     						=  (
                              encoded_inputs["""global_attention_mask"""] + [-1] * difference
                          )
                     elif self.padding_side == "left":
                          a_     :							Dict     						=  [-1] * difference + encoded_inputs[
                              """global_attention_mask"""
                          ]
                     else:
                          raise ValueError("""Invalid padding strategy:""" + str(self.padding_side       )       )

           return encoded_inputs
