
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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """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		(						A_ ):
   A__					:		Optional[Any]						     =     "longformer"



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Union[List[int], int] = 5_12    ,						snake_case__		:					int = 2    ,						snake_case__		:					int = 1    ,						snake_case__		:					int = 0    ,						snake_case__		:					int = 2    ,						snake_case__		:					int = 3_05_22    ,						snake_case__		:					int = 7_68    ,						snake_case__		:					int = 12    ,						snake_case__		:					int = 12    ,						snake_case__		:					int = 30_72    ,						snake_case__		:					str = "gelu"    ,						snake_case__		:					float = 0.1    ,						snake_case__		:					float = 0.1    ,						snake_case__		:					int = 5_12    ,						snake_case__		:					int = 2    ,						snake_case__		:					float = 0.02    ,						snake_case__		:					float = 1e-12    ,						snake_case__		:					bool = False    ,						**snake_case__		:					List[Any]    ,						)		->	Union[str, Any]:


          '''simple docstring'''


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

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






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					Dict    ,						snake_case__		:					"PretrainedConfig"    ,						snake_case__		:					str = "default"    ,						snake_case__		:					"List[PatchingSpec]" = None )		->	Dict:


          '''simple docstring'''


          super().__init__(snake_case__    ,						snake_case__    ,						snake_case__ )
          snake_case       :   int							=      True



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


          '''simple docstring'''


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



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


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



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	float:


          '''simple docstring'''


          return 1e-4



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


          return max(super().default_onnx_opset    ,						14 )



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


          '''simple docstring'''


          snake_case       :   int							=      super().generate_dummy_inputs(
              preprocessor=snake_case__    ,						batch_size=snake_case__    ,						seq_length=snake_case__    ,						is_pair=snake_case__    ,						framework=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
          snake_case       :   Optional[int]							=      torch.zeros_like(inputs["input_ids"] )
          # make every second token global
          snake_case       :   Union[str, Any]							=      1

          return inputs












"""simple docstring"""
from collections.abc import Callable

import numpy as np



def      __lowerCamelCase   (							a_     :		Callable ,   a_     :		float ,   a_     :		float ,   a_     :		float ,   a_     :		float							)			->      np.ndarray:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  int(np.ceil((x_end - xa) / step_size							)							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.zeros((n + 1,)							)
      __SCREAMING_SNAKE_CASE   :int						=  ya
      __SCREAMING_SNAKE_CASE   :str						=  xa

      for k in range(a_							):
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  y[k] + step_size * ode_func(a_ ,   y[k]							)
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()