




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      			=						logging.get_logger(__name__)


_lowerCamelCase      			=						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase      			=						{
    '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      			=						{
    'allenai/led-base-16384': 1_63_84,
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Tuple       		=  VOCAB_FILES_NAMES
     lowerCAmelCase    :	Optional[int]       		=  PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase    :	Dict       		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase    :	Optional[int]       		=  LEDTokenizer
     lowerCAmelCase    :	List[str]       		=  ['input_ids', 'attention_mask']
     def __init__(     self       :      Union[str, Any] ,   __snake_case       :      List[str]=None ,   __snake_case       :      Union[str, Any]=None ,   __snake_case       :      int=None ,   __snake_case       :      Dict="replace" ,   __snake_case       :      Tuple="<s>" ,   __snake_case       :      Optional[Any]="</s>" ,   __snake_case       :      Optional[int]="</s>" ,   __snake_case       :      int="<s>" ,   __snake_case       :      Any="<unk>" ,   __snake_case       :      Union[str, Any]="<pad>" ,   __snake_case       :      Optional[Any]="<mask>" ,   __snake_case       :      Optional[int]=False ,   __snake_case       :      Optional[Any]=True ,   **__snake_case       :      List[str] ,   ):
         super().__init__(
             __snake_case ,   __snake_case ,   tokenizer_file=__snake_case ,   errors=__snake_case ,   bos_token=__snake_case ,   eos_token=__snake_case ,   sep_token=__snake_case ,   cls_token=__snake_case ,   unk_token=__snake_case ,   pad_token=__snake_case ,   mask_token=__snake_case ,   add_prefix_space=__snake_case ,   trim_offsets=__snake_case ,   **__snake_case ,   )

         UpperCAmelCase_										=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('''add_prefix_space''' ,   __snake_case    ) != add_prefix_space:
             UpperCAmelCase_										=					getattr(__snake_case ,   pre_tok_state.pop('''type'''    )    )
             UpperCAmelCase_										=					add_prefix_space
             UpperCAmelCase_										=					pre_tok_class(**__snake_case    )

         UpperCAmelCase_										=					add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         UpperCAmelCase_										=					'''post_processor'''
         UpperCAmelCase_										=					getattr(self.backend_tokenizer ,   __snake_case ,   __snake_case    )
         if tokenizer_component_instance:
             UpperCAmelCase_										=					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:
                 UpperCAmelCase_										=					tuple(state['''sep''']    )
             if "cls" in state:
                 UpperCAmelCase_										=					tuple(state['''cls''']    )

             UpperCAmelCase_										=					False

             if state.get('''add_prefix_space''' ,   __snake_case    ) != add_prefix_space:
                 UpperCAmelCase_										=					add_prefix_space
                 UpperCAmelCase_										=					True

             if state.get('''trim_offsets''' ,   __snake_case    ) != trim_offsets:
                 UpperCAmelCase_										=					trim_offsets
                 UpperCAmelCase_										=					True

             if changes_to_apply:
                 UpperCAmelCase_										=					getattr(__snake_case ,   state.pop('''type'''    )    )
                 UpperCAmelCase_										=					component_class(**__snake_case    )
                 setattr(self.backend_tokenizer ,   __snake_case ,   __snake_case    )
     @property
     # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
     def   lowerCamelCase_       (     self       :      Tuple    ):
         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   lowerCamelCase_       (     self       :      int ,   __snake_case       :      Any    ):
         UpperCAmelCase_										=					AddedToken(__snake_case ,   lstrip=__snake_case ,   rstrip=__snake_case    ) if isinstance(__snake_case ,   __snake_case    ) else value
         UpperCAmelCase_										=					value
     def   lowerCamelCase_       (     self       :      Tuple ,   *__snake_case       :      Union[str, Any] ,   **__snake_case       :      int    ):
         UpperCAmelCase_										=					kwargs.get('''is_split_into_words''' ,   __snake_case    )

         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(*__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   *__snake_case       :      Optional[Any] ,   **__snake_case       :      Optional[Any]    ):
         UpperCAmelCase_										=					kwargs.get('''is_split_into_words''' ,   __snake_case    )

         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(*__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      str ,   __snake_case       :      Optional[str] = None    ):
         UpperCAmelCase_										=					self._tokenizer.model.save(__snake_case ,   name=__snake_case    )
         return tuple(__snake_case    )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Any ,   __snake_case       :      str=None    ):
         UpperCAmelCase_										=					[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   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None    ):
         UpperCAmelCase_										=					[self.sep_token_id]
         UpperCAmelCase_										=					[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   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      Union[Dict[str, EncodedInput], BatchEncoding] ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[bool] = None ,   ):
         UpperCAmelCase_										=					super()._pad(
             encoded_inputs=__snake_case ,   max_length=__snake_case ,   padding_strategy=__snake_case ,   pad_to_multiple_of=__snake_case ,   return_attention_mask=__snake_case ,   )

         # Load from model defaults
         if return_attention_mask is None:
             UpperCAmelCase_										=					'''attention_mask''' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
             UpperCAmelCase_										=					encoded_inputs[self.model_input_names[0]]
             # `global_attention_mask` need to have the same length as other (sequential) inputs.
             UpperCAmelCase_										=					len(encoded_inputs['''global_attention_mask''']    ) != len(__snake_case    )

             if needs_to_be_padded:
                 UpperCAmelCase_										=					len(__snake_case    ) - 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`
                     UpperCAmelCase_										=					(
                         encoded_inputs['''global_attention_mask'''] + [-1] * difference
                     )
                 elif self.padding_side == "left":
                     UpperCAmelCase_										=					[-1] * difference + encoded_inputs[
                         '''global_attention_mask'''
                     ]
                 else:
                     raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

         return encoded_inputs











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    while cur > 1:
        # Find the maximum number in arr
        UpperCAmelCase_										=					arr.index(max(arr[0:cur]				)				)
        # Reverse from 0 to mi
        UpperCAmelCase_										=					arr[mi::-1] + arr[mi + 1 : len(__UpperCamelCase				)]
        # Reverse whole list
        UpperCAmelCase_										=					arr[cur - 1 :: -1] + arr[cur : len(__UpperCamelCase				)]
        cur -= 1
    return arr


if __name__ == "__main__":
 _lowerCamelCase      			=						input('Enter numbers separated by a comma:\n').strip()
 _lowerCamelCase      			=						[int(item) for item in user_input.split(',')]
 print(pancake_sort(unsorted))





