


'''simple docstring'''







A				:					str    						=			[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       _A					:     Optional[int]     = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
       _A					:     int     = 0
       _A					:     Tuple     = 0
       while place < len(lowerCamelCase       ):
              if (place + 1 < len(lowerCamelCase       )) and (vals[roman[place]] < vals[roman[place + 1]]):
                     total += vals[roman[place + 1]] - vals[roman[place]]
                     place += 2
              else:
                     total += vals[roman[place]]
                     place += 1
       return total






def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       _A					:     Dict     = []
       for arabic, roman in ROMAN:
              ((_A)  ,			(_A))					:     str     = divmod(lowerCamelCase  ,lowerCamelCase       )
              result.append(roman * factor       )
              if number == 0:
                     break
       return "".join(lowerCamelCase       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()










'''simple docstring'''







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				:					Tuple    						=			logging.get_logger(__name__)

A				:					Tuple    						=			{
    '''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 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "longformer"


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[List[int], int] = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 30522				,							SCREAMING_SNAKE_CASE      :							int = 768				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 3072				,							SCREAMING_SNAKE_CASE      :							str = "gelu"				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							float = 0.02				,							SCREAMING_SNAKE_CASE      :							float = 1e-12				,							SCREAMING_SNAKE_CASE      :							bool = False				,							**SCREAMING_SNAKE_CASE      :							List[Any]				,							):
           super().__init__(pad_token_id=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = attention_window
           _A					:     int     = sep_token_id
           _A					:     Tuple     = bos_token_id
           _A					:     Any     = eos_token_id
           _A					:     List[str]     = vocab_size
           _A					:     Any     = hidden_size
           _A					:     Optional[int]     = num_hidden_layers
           _A					:     int     = num_attention_heads
           _A					:     Dict     = hidden_act
           _A					:     List[Any]     = intermediate_size
           _A					:     int     = hidden_dropout_prob
           _A					:     Optional[int]     = attention_probs_dropout_prob
           _A					:     int     = max_position_embeddings
           _A					:     Any     = type_vocab_size
           _A					:     Dict     = initializer_range
           _A					:     Any     = layer_norm_eps
           _A					:     List[Any]     = onnx_export




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							"PretrainedConfig"				,							SCREAMING_SNAKE_CASE      :							str = "default"				,							SCREAMING_SNAKE_CASE      :							"List[PatchingSpec]" = None):
           super().__init__(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = True


    @property
    def        A   (  self      :							List[str]):
           if self.task == "multiple-choice":
                  _A					:     Tuple     = {0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
                  _A					:     List[Any]     = {0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
                   ('global_attention_mask', dynamic_axis),
               ])


    @property
    def        A   (  self      :							str):
           _A					:     int     = super().outputs
           if self.task == "default":
                  _A					:     str     = {0: 'batch'}
           return outputs


    @property
    def        A   (  self      :							List[Any]):
           return 1e-4


    @property
    def        A   (  self      :							Dict):
           # needs to be >= 14 to support tril operator
           return max(super().default_onnx_opset				,							14)


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							"PreTrainedTokenizerBase"				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							Optional[TensorType] = None				,							):
           _A					:     Union[str, Any]     = super().generate_dummy_inputs(
               preprocessor=SCREAMING_SNAKE_CASE				,							batch_size=SCREAMING_SNAKE_CASE				,							seq_length=SCREAMING_SNAKE_CASE				,							is_pair=SCREAMING_SNAKE_CASE				,							framework=SCREAMING_SNAKE_CASE)
           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
           _A					:     Tuple     = torch.zeros_like(inputs['input_ids'])
           # make every second token global
           _A					:     Dict     = 1

           return inputs






