


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


a					  =     logging.get_logger(__name__)

a					  =     {
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}







class        UpperCamelCase__   (	__magic_name__   ):
     __SCREAMING_SNAKE_CASE       :  Any						=   'longformer'

     def __init__(     self       :   Union[str, Any]							,					UpperCamelCase__       :   Union[List[int], int] = 512							,					UpperCamelCase__       :   int = 2							,					UpperCamelCase__       :   int = 1							,					UpperCamelCase__       :   int = 0							,					UpperCamelCase__       :   int = 2							,					UpperCamelCase__       :   int = 30_522							,					UpperCamelCase__       :   int = 768							,					UpperCamelCase__       :   int = 12							,					UpperCamelCase__       :   int = 12							,					UpperCamelCase__       :   int = 3_072							,					UpperCamelCase__       :   str = "gelu"							,					UpperCamelCase__       :   float = 0.1							,					UpperCamelCase__       :   float = 0.1							,					UpperCamelCase__       :   int = 512							,					UpperCamelCase__       :   int = 2							,					UpperCamelCase__       :   float = 0.02							,					UpperCamelCase__       :   float = 1e-12							,					UpperCamelCase__       :   bool = False							,					**UpperCamelCase__       :   Optional[int]							,					):






          '''simple docstring'''






          super().__init__(pad_token_id=UpperCamelCase__							,					**UpperCamelCase__       )

          lowercase_  							=			attention_window
          lowercase_  							=			sep_token_id
          lowercase_  							=			bos_token_id
          lowercase_  							=			eos_token_id
          lowercase_  							=			vocab_size
          lowercase_  							=			hidden_size
          lowercase_  							=			num_hidden_layers
          lowercase_  							=			num_attention_heads
          lowercase_  							=			hidden_act
          lowercase_  							=			intermediate_size
          lowercase_  							=			hidden_dropout_prob
          lowercase_  							=			attention_probs_dropout_prob
          lowercase_  							=			max_position_embeddings
          lowercase_  							=			type_vocab_size
          lowercase_  							=			initializer_range
          lowercase_  							=			layer_norm_eps
          lowercase_  							=			onnx_export







class        UpperCamelCase__   (	__magic_name__   ):

     def __init__(     self       :   Optional[Any]							,					UpperCamelCase__       :   "PretrainedConfig"							,					UpperCamelCase__       :   str = "default"							,					UpperCamelCase__       :   "List[PatchingSpec]" = None       ):






          '''simple docstring'''






          super().__init__(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )
          lowercase_  							=			True

     @property
     def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






          '''simple docstring'''






          if self.task == "multiple-choice":
               lowercase_  							=			{0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
               lowercase_  							=			{0: """batch""", 1: """sequence"""}
          return OrderedDict(
              [
                  ("""input_ids""", dynamic_axis),
                  ("""attention_mask""", dynamic_axis),
                  ("""global_attention_mask""", dynamic_axis),
              ]       )

     @property
     def   UpperCAmelCase__       (     self       :   Dict       ):






          '''simple docstring'''






          lowercase_  							=			super().outputs
          if self.task == "default":
               lowercase_  							=			{0: """batch"""}
          return outputs

     @property
     def   UpperCAmelCase__       (     self       :   List[Any]       ):






          '''simple docstring'''






          return 1e-4

     @property
     def   UpperCAmelCase__       (     self       :   Dict       ):






          '''simple docstring'''






          return max(super().default_onnx_opset							,					14       )







     def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   "PreTrainedTokenizerBase"							,					UpperCamelCase__       :   int = -1							,					UpperCamelCase__       :   int = -1							,					UpperCamelCase__       :   bool = False							,					UpperCamelCase__       :   Optional[TensorType] = None							,					):






          '''simple docstring'''






          lowercase_  							=			super().generate_dummy_inputs(
              preprocessor=UpperCamelCase__							,					batch_size=UpperCamelCase__							,					seq_length=UpperCamelCase__							,					is_pair=UpperCamelCase__							,					framework=UpperCamelCase__       )
          import torch

          # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
          # makes the export fail randomly
          lowercase_  							=			torch.zeros_like(inputs["""input_ids"""]       )
          # make every second token global
          lowercase_  							=			1

          return inputs






from __future__ import annotations

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

a					  =     TypeVar('T')







class        UpperCamelCase__   (	Generic[T]   ):
					__SCREAMING_SNAKE_CASE       :  deque[T]  # Cache store of keys
					__SCREAMING_SNAKE_CASE       :  set[T]  # References of the keys in cache
					__SCREAMING_SNAKE_CASE       :  int						=   10  # Maximum capacity of cache

					def __init__(     self       :   str							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			deque()
										lowercase_  							=			set()
										if not n:
															lowercase_  							=			sys.maxsize
										elif n < 0:
															raise ValueError("""n should be an integer greater than 0."""       )
										else:
															lowercase_  							=			n

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   T       ):






										'''simple docstring'''






										if x not in self.key_reference:
															if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																				lowercase_  							=			self.dq_store.pop()
																				self.key_reference.remove(UpperCamelCase__       )
										else:
															self.dq_store.remove(UpperCamelCase__       )

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

					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										for k in self.dq_store:
															print(UpperCamelCase__       )







					def __repr__(     self       :   Optional[Any]       ):






										'''simple docstring'''






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


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							a					  =     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]"


