
'''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


a_											=						logging.get_logger(__name__)


a_											=						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_											=						{
    "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",
    },
}

a_											=						{
    "allenai/led-base-16384": 1_6384,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						LEDTokenizer
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_="replace" ,       lowercase_="<s>" ,       lowercase_="</s>" ,       lowercase_="</s>" ,       lowercase_="<s>" ,       lowercase_="<unk>" ,       lowercase_="<pad>" ,       lowercase_="<mask>" ,       lowercase_=False ,       lowercase_=True ,       **lowercase_ ,       ):
     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_ ,       )

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

     snake_case_ :    List[str]							=						add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     snake_case_ :    str							=						"post_processor"
     snake_case_ :    Tuple							=						getattr(self.backend_tokenizer ,       lowercase_ ,       lowercase_)
     if tokenizer_component_instance:
         snake_case_ :    Dict							=						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:
             snake_case_ :    str							=						tuple(state["sep"])
         if "cls" in state:
             snake_case_ :    Optional[Any]							=						tuple(state["cls"])

         snake_case_ :    Tuple							=						False

         if state.get("add_prefix_space" ,       lowercase_) != add_prefix_space:
             snake_case_ :    int							=						add_prefix_space
             snake_case_ :    int							=						True

         if state.get("trim_offsets" ,       lowercase_) != trim_offsets:
             snake_case_ :    Optional[Any]							=						trim_offsets
             snake_case_ :    Dict							=						True

         if changes_to_apply:
             snake_case_ :    Tuple							=						getattr(lowercase_ ,       state.pop("type"))
             snake_case_ :    int							=						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        snake_case__		(    self):
     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        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Tuple							=						AddedToken(lowercase_ ,       lstrip=lowercase_ ,       rstrip=lowercase_) if isinstance(lowercase_ ,       lowercase_) else value
     snake_case_ :    Tuple							=						value





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     snake_case_ :    Union[str, 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        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     snake_case_ :    Optional[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()._encode_plus(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     snake_case_ :    Any							=						self._tokenizer.model.save(lowercase_ ,       name=lowercase_)
     return tuple(lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     snake_case_ :    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        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     snake_case_ :    int							=						[self.sep_token_id]
     snake_case_ :    List[Any]							=						[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        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = PaddingStrategy.DO_NOT_PAD ,       lowercase_ = None ,       lowercase_ = None ,       ):
     snake_case_ :    str							=						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:
         snake_case_ :    str							=						"attention_mask" in self.model_input_names

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

         if needs_to_be_padded:
             snake_case_ :    Optional[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`
                 snake_case_ :    Union[str, Any]							=						(
                     encoded_inputs["global_attention_mask"] + [-1] * difference
                 )
             elif self.padding_side == "left":
                 snake_case_ :    List[str]							=						[-1] * difference + encoded_inputs[
                     "global_attention_mask"
                 ]
             else:
                 raise ValueError("Invalid padding strategy:" + str(self.padding_side))

     return encoded_inputs






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a_											=						TypeVar("T")
a_											=						TypeVar("U")






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Any     			=						key
     snake_case_ :    List[Any]     			=						val
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None





 def __repr__(    self):
     return (
         F'Node: key: {self.key}, val: {self.val}, '
         F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
     )






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self):
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_			, snake_case_ :    Union[str, Any]     			=						self.rear, self.head





 def __repr__(    self):
     snake_case_ :    Dict     			=						["DoubleLinkedList"]
     snake_case_ :    Dict     			=						self.head
     while node.next is not None:
         rep.append(str(lowercase_))
         snake_case_ :    List[str]     			=						node.next
     rep.append(str(self.rear))
     return ",\n    ".join(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[str]     			=						self.rear.prev

     # All nodes other than self.head are guaranteed to have non-None previous
     assert previous is not None

     snake_case_ :    Tuple     			=						node
     snake_case_ :    str     			=						previous
     snake_case_ :    Optional[Any]     			=						node
     snake_case_ :    Any     			=						self.rear





 def        snake_case__		(    self ,       lowercase_):

     if node.prev is None or node.next is None:
         return None

     snake_case_ :    Union[str, Any]     			=						node.next
     snake_case_ :    Optional[int]     			=						node.prev
     snake_case_ :    str     			=						None
     snake_case_ :    int     			=						None
     return node






class    UpperCAmelCase_				(	Generic[T, U]					):
 UpperCAmelCase_		    =						{}





 def __init__(    self ,       lowercase_):
     snake_case_ :    DoubleLinkedList[T, U]     			=						DoubleLinkedList()
     snake_case_ :    List[str]     			=						capacity
     snake_case_ :    Any     			=						0
     snake_case_ :    Dict     			=						0
     snake_case_ :    Union[str, Any]     			=						0
     snake_case_ :    dict[T, DoubleLinkedListNode[T, U]]     			=						{}





 def __repr__(    self):
     return (
         F'CacheInfo(hits={self.hits}, misses={self.miss}, '
         F'capacity={self.capacity}, current size={self.num_keys})'
     )





 def __contains__(    self ,       lowercase_):
     return key in self.cache





 def        snake_case__		(    self ,       lowercase_):
     # Note: pythonic interface would throw KeyError rather than return None

     if key in self.cache:
         self.hits += 1
         snake_case_ :    DoubleLinkedListNode[T, U]     			=						self.cache[key]
         snake_case_ :    Optional[Any]     			=						self.list.remove(self.cache[key])
         assert node == value_node

         # node is guaranteed not None because it is in self.cache
         assert node is not None
         self.list.add(lowercase_)
         return node.val
     self.miss += 1
     return None





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):

     if key not in self.cache:
         if self.num_keys >= self.capacity:
             # delete first node (oldest) when over capacity
             snake_case_ :    Optional[Any]     			=						self.list.head.next

             # guaranteed to have a non-None first node when num_keys > 0
             # explain to type checker via assertions
             assert first_node is not None
             assert first_node.key is not None
             assert (
                 self.list.remove(lowercase_) is not None
             )  # node guaranteed to be in list assert node.key is not None

             del self.cache[first_node.key]
             self.num_keys -= 1
         snake_case_ :    Dict     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
         self.list.add(self.cache[key])
         self.num_keys += 1

     else:
         # bump node to the end of the list, update value
         snake_case_ :    List[Any]     			=						self.list.remove(self.cache[key])
         assert node is not None  # node guaranteed to be in list
         snake_case_ :    Optional[Any]     			=						value
         self.list.add(lowercase_)





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ = 1_28):

     def cache_decorator_inner(lowercase_) -> Callable[..., U]:
         def cache_decorator_wrapper(*lowercase_) -> U:
             if func not in cls.decorator_function_to_instance_map:
                 snake_case_ :    List[str]     			=						LRUCache(lowercase_)

             snake_case_ :    Optional[Any]     			=						cls.decorator_function_to_instance_map[func].get(args[0])
             if result is None:
                 snake_case_ :    Any     			=						func(*lowercase_)
                 cls.decorator_function_to_instance_map[func].put(args[0] ,       lowercase_)
             return result

         def cache_info() -> LRUCache[T, U]:
             return cls.decorator_function_to_instance_map[func]

         setattr(lowercase_ ,       "cache_info" ,       lowercase_)  # noqa: B010

         return cache_decorator_wrapper

     return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()




