



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"""





			lowercase__      :  List[Any]      =			"""longformer"""





			def __init__(				self	:       int			,     lowercase	:       Union[List[int], int] = 512			,     lowercase	:       int = 2			,     lowercase	:       int = 1			,     lowercase	:       int = 0			,     lowercase	:       int = 2			,     lowercase	:       int = 30_522			,     lowercase	:       int = 768			,     lowercase	:       int = 12			,     lowercase	:       int = 12			,     lowercase	:       int = 3_072			,     lowercase	:       str = "gelu"			,     lowercase	:       float = 0.1			,     lowercase	:       float = 0.1			,     lowercase	:       int = 512			,     lowercase	:       int = 2			,     lowercase	:       float = 0.02			,     lowercase	:       float = 1E-1_2			,     lowercase	:       bool = False			,     **lowercase	:       Dict			,     )						->					List[Any]:
									"""simple docstring"""







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

									__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 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			def __init__(				self	:       int			,     lowercase	:       "PretrainedConfig"			,     lowercase	:       str = "default"			,     lowercase	:       "List[PatchingSpec]" = None   )						->					Union[str, Any]:
									"""simple docstring"""







									super().__init__(lowercase			,     lowercase			,     lowercase   )
									__lowercase					=				True





			@property
			def 		snake_case__			(				self	:       List[Any]   )						->					Mapping[str, Mapping[int, str]]:
									"""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 		snake_case__			(				self	:       Union[str, Any]   )						->					Mapping[str, Mapping[int, str]]:
									"""simple docstring"""







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





			@property
			def 		snake_case__			(				self	:       Optional[int]   )						->					float:
									"""simple docstring"""







									return 1E-4





			@property
			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									return max(super().default_onnx_opset			,     14   )







			def 		snake_case__			(				self	:       Any			,     lowercase	:       "PreTrainedTokenizerBase"			,     lowercase	:       int = -1			,     lowercase	:       int = -1			,     lowercase	:       bool = False			,     lowercase	:       Optional[TensorType] = None			,     )						->					Mapping[str, Any]:
									"""simple docstring"""







									__lowercase					=				super().generate_dummy_inputs(
									    preprocessor=lowercase			,     batch_size=lowercase			,     seq_length=lowercase			,     is_pair=lowercase			,     framework=lowercase   )
									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










def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						__lowercase					=				(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
						# formula for sum of series
						return total






def  UpperCAmelCase__    (    ) ->      List[str]:
						print(sum_of_series(1						,       1						,       10   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





