




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


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    '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	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"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 = 30_522       ,     SCREAMING_SNAKE_CASE__					:					int = 768       ,     SCREAMING_SNAKE_CASE__					:					int = 12       ,     SCREAMING_SNAKE_CASE__					:					int = 12       ,     SCREAMING_SNAKE_CASE__					:					int = 3_072       ,     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-1_2       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     )				->	List[str]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

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






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					"PretrainedConfig"       ,     SCREAMING_SNAKE_CASE__					:					str = "default"       ,     SCREAMING_SNAKE_CASE__					:					"List[PatchingSpec]" = None       )				->	Tuple:
         super().__init__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			True


    @property
    def        a				(	self					:					str       )				->	Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
              lowerCAmelCase__          =			{0: "batch", 1: "choice", 2: "sequence"}
         else:
              lowerCAmelCase__          =			{0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
                 ("global_attention_mask", dynamic_axis),
             ]       )


    @property
    def        a				(	self					:					Union[str, Any]       )				->	Mapping[str, Mapping[int, str]]:
         lowerCAmelCase__          =			super().outputs
         if self.task == "default":
              lowerCAmelCase__          =			{0: "batch"}
         return outputs


    @property
    def        a				(	self					:					List[str]       )				->	float:
         return 1e-4


    @property
    def        a				(	self					:					Union[str, Any]       )				->	int:
         # needs to be >= 14 to support tril operator
         return max(super().default_onnx_opset       ,     14       )


    def        a				(	self					:					Any       ,     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       ,     )				->	Mapping[str, Any]:
         lowerCAmelCase__          =			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
         lowerCAmelCase__          =			torch.zeros_like(inputs["input_ids"]       )
         # make every second token global
         lowerCAmelCase__          =			1

         return inputs






from maths.prime_factors import prime_factors



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     if not isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
          lowerCAmelCase__          =			F'Input value of [number={number}] must be an integer'
          raise TypeError(lowerCAmelCase_      )
     if number < 1:
          raise ValueError("Input must be a positive integer"      )
     return -1 if len(prime_factors(lowerCAmelCase_      )      ) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()
