


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				,       _SCREAMING_SNAKE_CASE = 512				,       _SCREAMING_SNAKE_CASE = 2				,       _SCREAMING_SNAKE_CASE = 1				,       _SCREAMING_SNAKE_CASE = 0				,       _SCREAMING_SNAKE_CASE = 2				,       _SCREAMING_SNAKE_CASE = 3_0522				,       _SCREAMING_SNAKE_CASE = 768				,       _SCREAMING_SNAKE_CASE = 12				,       _SCREAMING_SNAKE_CASE = 12				,       _SCREAMING_SNAKE_CASE = 3072				,       _SCREAMING_SNAKE_CASE = "gelu"				,       _SCREAMING_SNAKE_CASE = 0.1				,       _SCREAMING_SNAKE_CASE = 0.1				,       _SCREAMING_SNAKE_CASE = 512				,       _SCREAMING_SNAKE_CASE = 2				,       _SCREAMING_SNAKE_CASE = 0.0_2				,       _SCREAMING_SNAKE_CASE = 1e-12				,       _SCREAMING_SNAKE_CASE = False				,       **_SCREAMING_SNAKE_CASE				,       )->Optional[Any]:




         '''simple docstring'''





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

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






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = "default"				,       _SCREAMING_SNAKE_CASE = None		)->Union[str, Any]:




         '''simple docstring'''





         super().__init__(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					True



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




         '''simple docstring'''





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



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




         '''simple docstring'''





         A_      :				str		     =					super().outputs
         if self.task == "default":
           A_      :				List[Any]		     =					{0: '''batch'''}
         return outputs



       @property
       def        _snake_case       (      self		)->float:




         '''simple docstring'''





         return 1e-4



       @property
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         return max(super().default_onnx_opset				,       14		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = -1				,       _SCREAMING_SNAKE_CASE = -1				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = None				,       )->Mapping[str, Any]:




         '''simple docstring'''





         A_      :				Any		     =					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
         A_      :				List[str]		     =					torch.zeros_like(inputs['''input_ids''']		)
         # make every second token global
         A_      :				Dict		     =					1

         return inputs








from math import isclose, sqrt


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					point_y / 4 / point_x
  A_      :				Union[str, Any]		     =					2 * normal_gradient / (1 + normal_gradient * normal_gradient)
  A_      :				int		     =					(1 - normal_gradient * normal_gradient) / (
      1 + normal_gradient * normal_gradient
  )
  A_      :				List[str]		     =					(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

  # to find the next point, solve the simultaeneous equations:
  # y^2 + 4x^2 = 100
  # y - b = m * (x - a)
  # ==> A x^2 + B x + C = 0
  A_      :				List[str]		     =					outgoing_gradient**2 + 4
  A_      :				List[str]		     =					2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
  A_      :				Any		     =					(point_y - outgoing_gradient * point_x) ** 2 - 100

  A_      :				str		     =					(
      -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)
  A_      :				Any		     =					(
      -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)

  # two solutions, one of which is our input point
  A_      :				int		     =					x_minus if isclose(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) else x_plus
  A_      :				Any		     =					point_y + outgoing_gradient * (next_x - point_x)

  return next_x, next_y, outgoing_gradient


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE = 1.4 ,			SCREAMING_SNAKE_CASE = -9.6       ):
  A_      :				int		     =					0
  A_      :				float		     =					first_x_coord
  A_      :				float		     =					first_y_coord
  A_      :				float		     =					(1_0.1 - point_y) / (0.0 - point_x)

  while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
    A_     ,       A_     ,       A_      :				List[str]		     =					next_point(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    num_reflections += 1

  return num_reflections


if __name__ == "__main__":
     print(F'''{solution() = }''')




