





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				:       Optional[Any]	   =				logging.get_logger(__name__)

_UpperCAmelCase				:       Any	   =				{
    """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			):
 UpperCAmelCase__													=		"""longformer"""
 def __init__(      self  :      Any			,     UpperCAmelCase  :      Union[List[int], int] = 512			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      int = 0			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 30522			,     UpperCAmelCase  :      int = 768			,     UpperCAmelCase  :      int = 12			,     UpperCAmelCase  :      int = 12			,     UpperCAmelCase  :      int = 3072			,     UpperCAmelCase  :      str = "gelu"			,     UpperCAmelCase  :      float = 0.1			,     UpperCAmelCase  :      float = 0.1			,     UpperCAmelCase  :      int = 512			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      float = 0.0_2			,     UpperCAmelCase  :      float = 1e-12			,     UpperCAmelCase  :      bool = False			,     **UpperCAmelCase  :      int			,     )			->	Union[str, Any]:
     super().__init__(pad_token_id=UpperCAmelCase			,     **UpperCAmelCase	)

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


class    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      "PretrainedConfig"			,     UpperCAmelCase  :      str = "default"			,     UpperCAmelCase  :      "List[PatchingSpec]" = None	)			->	Any:
     super().__init__(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		True
 @property
 def      A_	(      self  :      List[Any]	)			->	Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         lowerCamelCase__    :      int					 =		{0: 'batch', 1: 'choice', 2: 'sequence'}
     else:
         lowerCamelCase__    :      Optional[int]					 =		{0: 'batch', 1: 'sequence'}
     return OrderedDict(
         [
             ('input_ids', dynamic_axis),
             ('attention_mask', dynamic_axis),
             ('global_attention_mask', dynamic_axis),
         ]	)
 @property
 def      A_	(      self  :      Dict	)			->	Mapping[str, Mapping[int, str]]:
     lowerCamelCase__    :      Any					 =		super().outputs
     if self.task == "default":
         lowerCamelCase__    :      List[Any]					 =		{0: 'batch'}
     return outputs
 @property
 def      A_	(      self  :      Optional[int]	)			->	float:
     return 1e-4
 @property
 def      A_	(      self  :      str	)			->	int:
     # needs to be >= 14 to support tril operator
     return max(super().default_onnx_opset			,     14	)







 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      "PreTrainedTokenizerBase"			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      int = -1			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      Optional[TensorType] = None			,     )			->	Mapping[str, Any]:
     lowerCamelCase__    :      List[str]					 =		super().generate_dummy_inputs(
         preprocessor=UpperCAmelCase			,     batch_size=UpperCAmelCase			,     seq_length=UpperCAmelCase			,     is_pair=UpperCAmelCase			,     framework=UpperCAmelCase	)
     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__    :      Dict					 =		torch.zeros_like(inputs['input_ids']	)
     # make every second token global
     lowerCamelCase__    :      Dict					 =		1

     return inputs






'''simple docstring'''





from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE_:     Optional[int]  =3_00  # TEMPERATURE (unit = K)





def 	lowerCAmelCase_				(					snake_case_       : float					,    snake_case_       : float					,    snake_case_       : float					,    )    ->      float:


     '''simple docstring'''




     if donor_conc <= 0:
          raise ValueError("Donor concentration should be positive"			)
     elif acceptor_conc <= 0:
          raise ValueError("Acceptor concentration should be positive"			)
     elif intrinsic_conc <= 0:
          raise ValueError("Intrinsic concentration should be positive"			)
     elif donor_conc <= intrinsic_conc:
          raise ValueError(
              "Donor concentration should be greater than intrinsic concentration"			)
     elif acceptor_conc <= intrinsic_conc:
          raise ValueError(
              "Acceptor concentration should be greater than intrinsic concentration"			)
     else:
          return (
              Boltzmann
              * T
              * log((donor_conc * acceptor_conc) / intrinsic_conc**2			)
              / physical_constants["electron volt"][0]
          )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




