

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


__snake_case							:	Tuple											=       logging.get_logger(__name__)

__snake_case							:	str											=       {
    """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     A__(__SCREAMING_SNAKE_CASE				):
						"""simple docstring"""






						_A  :							Optional[Any]     =       """longformer"""





						def __init__(  self   ,	_lowercase = 512   ,	_lowercase = 2   ,	_lowercase = 1   ,	_lowercase = 0   ,	_lowercase = 2   ,	_lowercase = 30_522   ,	_lowercase = 768   ,	_lowercase = 12   ,	_lowercase = 12   ,	_lowercase = 3_072   ,	_lowercase = "gelu"   ,	_lowercase = 0.1   ,	_lowercase = 0.1   ,	_lowercase = 512   ,	_lowercase = 2   ,	_lowercase = 0.0_2   ,	_lowercase = 1e-12   ,	_lowercase = False   ,	**_lowercase   ,	)			->      Optional[int]:
									super().__init__(pad_token_id=_lowercase   ,	**_lowercase  )

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







class     A__(__SCREAMING_SNAKE_CASE				):
						"""simple docstring"""






						def __init__(  self   ,	_lowercase   ,	_lowercase = "default"   ,	_lowercase = None  )			->      Dict:
									super().__init__(_lowercase   ,	_lowercase   ,	_lowercase  )
									a_		:		Optional[int]      				=      True





						@property
						def        UpperCamelCase__ (  self  )			->      Tuple:
									if self.task == "multiple-choice":
												a_		:		Any      				=      {0: """batch""", 1: """choice""", 2: """sequence"""}
									else:
												a_		:		Optional[int]      				=      {0: """batch""", 1: """sequence"""}
									return OrderedDict(
									    [
									        ("""input_ids""", dynamic_axis),
									        ("""attention_mask""", dynamic_axis),
									        ("""global_attention_mask""", dynamic_axis),
									    ]  )





						@property
						def        UpperCamelCase__ (  self  )			->      Tuple:
									a_		:		str      				=      super().outputs
									if self.task == "default":
												a_		:		Any      				=      {0: """batch"""}
									return outputs





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





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





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase = -1   ,	_lowercase = -1   ,	_lowercase = False   ,	_lowercase = None   ,	)			->      Optional[Any]:
									a_		:		List[Any]      				=      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_		:		Any      				=      torch.zeros_like(inputs["""input_ids"""]  )
									# make every second token global
									a_		:		Union[str, Any]      				=      1

									return inputs




_lowerCamelCase					   ={
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4E-1_9,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355818,
}





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
  lowerCamelCase		:	Dict							=      (
      F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Valid values are: {", ".join(lowerCamelCase							)}'''
  )
  raise ValueError(lowerCamelCase							)
 return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




