


'''simple docstring'''






import doctest
from collections import deque

import numpy as np






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    int						)  ->    None:
      lowerCAmelCase_     : List[str]     =  [2, 1, 2, -1]
      lowerCAmelCase_     : Optional[Any]     =  [1, 2, 3, 4]





    def __lowercase  (  self       :    List[str]						)  ->    list[float]:
      lowerCAmelCase_     : List[Any]     =  len(self.first_signal						)
      lowerCAmelCase_     : List[str]     =  len(self.second_signal						)

      lowerCAmelCase_     : Any     =  max(lowerCamelCase       ,		lowerCamelCase						)

      # create a zero matrix of max_length x max_length
      lowerCAmelCase_     : Dict     =  [[0] * max_length for i in range(lowerCamelCase						)]

      # fills the smaller signal with zeros to make both signals of same length
      if length_first_signal < length_second_signal:
        self.first_signal += [0] * (max_length - length_first_signal)
      elif length_first_signal > length_second_signal:
        self.second_signal += [0] * (max_length - length_second_signal)
      for i in range(lowerCamelCase						):
        lowerCAmelCase_     : Optional[Any]     =  deque(self.second_signal						)
        rotated_signal.rotate(lowerCamelCase						)
        for j, item in enumerate(lowerCamelCase						):
          matrix[i][j] += item

        # multiply the matrix with the first signal
      lowerCAmelCase_     : Optional[int]     =  np.matmul(np.transpose(lowerCamelCase						)       ,		np.transpose(self.first_signal						)						)

      # rounding-off to two decimal places
      return [round(lowerCamelCase       ,		2						) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()







'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A   :    List[Any]											= logging.get_logger(__name__)

__A   :    List[Any]											= {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roberta-prelayernorm'

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Tuple=5_02_65       ,		lowerCamelCase       :    Optional[int]=7_68       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    int=30_72       ,		lowerCamelCase       :    Optional[int]="gelu"       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Optional[int]=5_12       ,		lowerCamelCase       :    Union[str, Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    Any=1E-12       ,		lowerCamelCase       :    int=1       ,		lowerCamelCase       :    List[Any]=0       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    Optional[Any]="absolute"       ,		lowerCamelCase       :    List[Any]=True       ,		lowerCamelCase       :    Tuple=None       ,		**lowerCamelCase       :    int       ,		)  ->    Optional[Any]:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  vocab_size
      lowerCAmelCase_     : str     =  hidden_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : List[Any]     =  num_attention_heads
      lowerCAmelCase_     : List[str]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  intermediate_size
      lowerCAmelCase_     : List[str]     =  hidden_dropout_prob
      lowerCAmelCase_     : Tuple     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Optional[int]     =  max_position_embeddings
      lowerCAmelCase_     : List[Any]     =  type_vocab_size
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  position_embedding_type
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : Union[str, Any]     =  classifier_dropout







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    List[Any]						)  ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCAmelCase_     : Dict     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        lowerCAmelCase_     : Optional[int]     =  {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]						)



