


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


_lowercase       :      Optional[int]	=  logging.get_logger(__name__)

_lowercase       :      int	=  {
    "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    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'longformer'

				def __init__(						self				,   a__ = 512				,   a__ = 2				,   a__ = 1				,   a__ = 0				,   a__ = 2				,   a__ = 3_0522				,   a__ = 768				,   a__ = 12				,   a__ = 12				,   a__ = 3072				,   a__ = "gelu"				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 512				,   a__ = 2				,   a__ = 0.02				,   a__ = 1e-12				,   a__ = False				,   **a__				,   )	->				List[str]:
					super().__init__(pad_token_id=lowercase_				,   **lowercase_   )

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






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__ = "default"				,   a__ = None   )	->				Any:
					super().__init__(lowercase_				,   lowercase_				,   lowercase_   )
					A								=				True

				@property
				def      _UpperCAmelCase					(						self   )	->				Mapping[str, Mapping[int, str]]:







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

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

				@property
				def      _UpperCAmelCase					(						self   )	->				float:
					return 1e-4

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return max(super().default_onnx_opset				,   14   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = -1				,   a__ = -1				,   a__ = False				,   a__ = None				,   )	->				Mapping[str, Any]:
					A								=				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
					A								=				torch.zeros_like(inputs["""input_ids"""]   )
					# make every second token global
					A								=				1

					return inputs







def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



