'''simple docstring'''

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







def __UpperCamelCase(   _A					:				Any  ,			_A					:				int  ,			_A					:				Optional[Any]  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   if isinstance(lowerCAmelCase__  ,			lowerCAmelCase__    ):
      UpperCAmelCase__							:				Tuple   				=  np.full((len(lowerCAmelCase__    ), sequence_length, 2)  ,			lowerCAmelCase__    )
   else:
      UpperCAmelCase__							:				List[Any]   				=  np.full((len(lowerCAmelCase__    ), sequence_length)  ,			lowerCAmelCase__    )

   for i, tensor in enumerate(lowerCAmelCase__    ):
      if padding_side == "right":
         if isinstance(lowerCAmelCase__  ,			lowerCAmelCase__    ):
            UpperCAmelCase__							:				Optional[Any]   				=  tensor[:sequence_length]
         else:
            UpperCAmelCase__							:				Optional[int]   				=  tensor[:sequence_length]
      else:
         if isinstance(lowerCAmelCase__  ,			lowerCAmelCase__    ):
            UpperCAmelCase__							:				Dict   				=  tensor[:sequence_length]
         else:
            UpperCAmelCase__							:				Dict   				=  tensor[:sequence_length]

   return out_tensor.tolist()







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  ord(lowerCAmelCase__    )
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   UpperCAmelCase__							:				Optional[int]   				=  unicodedata.category(lowerCAmelCase__    )
   if cat.startswith('''P'''    ):
      return True
   return False


@dataclass
class 				_lowercase	(      __a   ):
      '''simple docstring'''


      UpperCAmelCase_      :      PreTrainedTokenizerBase
      UpperCAmelCase_      :      Union[bool, str, PaddingStrategy]      							=   True
      UpperCAmelCase_      :      Optional[int]      							=   None
      UpperCAmelCase_      :      Optional[int]      							=   None
      UpperCAmelCase_      :      int      							=   -1_00
      UpperCAmelCase_      :      str      							=   "pt"




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         import torch

         UpperCAmelCase__							:				int   				=  '''label''' if '''label''' in features[0].keys() else '''labels'''
         UpperCAmelCase__							:				Union[str, Any]   				=  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
         UpperCAmelCase__							:				int   				=  self.tokenizer.pad(
             snake_case__					,padding=self.padding					,max_length=self.max_length					,pad_to_multiple_of=self.pad_to_multiple_of					,return_tensors='''pt''' if labels is None else None					,)

         if labels is None:
            return batch

         UpperCAmelCase__							:				Any   				=  torch.tensor(batch['''entity_ids''']							).shape[1]
         UpperCAmelCase__							:				Any   				=  self.tokenizer.padding_side
         if padding_side == "right":
            UpperCAmelCase__							:				List[str]   				=  [
                list(snake_case__							) + [self.label_pad_token_id] * (sequence_length - len(snake_case__							)) for label in labels
            ]
         else:
            UpperCAmelCase__							:				List[Any]   				=  [
                [self.label_pad_token_id] * (sequence_length - len(snake_case__							)) + list(snake_case__							) for label in labels
            ]

         UpperCAmelCase__							:				Dict   				=  [feature['''ner_tags'''] for feature in features]
         UpperCAmelCase__							:				List[str]   				=  padding_tensor(snake_case__					,-1					,snake_case__					,snake_case__							)
         UpperCAmelCase__							:				Dict   				=  [feature['''original_entity_spans'''] for feature in features]
         UpperCAmelCase__							:				str   				=  padding_tensor(snake_case__					,(-1, -1)					,snake_case__					,snake_case__							)
         UpperCAmelCase__							:				int   				=  {k: torch.tensor(snake_case__					,dtype=torch.intaa							) for k, v in batch.items()}

         return batch





'''simple docstring'''

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase__     :	Tuple				=			TypeVar('T')


class 				_lowercase	(      Generic[T]   ):
      '''simple docstring'''


      UpperCAmelCase_      :      deque[T]  # Cache store of keys
      UpperCAmelCase_      :      set[T]  # References of the keys in cache
      UpperCAmelCase_      :      int      							=   10  # Maximum capacity of cache




      def __init__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  deque()
         UpperCAmelCase__							:				Optional[int]   				=  set()
         if not n:
            UpperCAmelCase__							:				List[Any]   				=  sys.maxsize
         elif n < 0:
            raise ValueError('''n should be an integer greater than 0.'''							)
         else:
            UpperCAmelCase__							:				Any   				=  n




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         if x not in self.key_reference:
            if len(self.dq_store							) == LRUCache._MAX_CAPACITY:
               UpperCAmelCase__							:				int   				=  self.dq_store.pop()
               self.key_reference.remove(lowerCamelCase_							)
         else:
            self.dq_store.remove(lowerCamelCase_							)

         self.dq_store.appendleft(lowerCamelCase_							)
         self.key_reference.add(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         for k in self.dq_store:
            print(lowerCamelCase_							)




      def __repr__(				self							)			->			str:



         '''simple docstring'''



         return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store							)}'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCamelCase__     :	LRUCache[str | int]				=			LRUCache(4)
  lru_cache.refer('A')
  lru_cache.refer(2)
  lru_cache.refer(3)
  lru_cache.refer('A')
  lru_cache.refer(4)
  lru_cache.refer(5)
  lru_cache.display()

  print(lru_cache)
  assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




