





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase      							=   {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase      							=   [
           'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'BioGptForCausalLM',
           'BioGptForTokenClassification',
           'BioGptForSequenceClassification',
           'BioGptModel',
           'BioGptPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
       from .tokenization_biogpt import BioGptTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_biogpt import (
                  BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BioGptForCausalLM,
                  BioGptForSequenceClassification,
                  BioGptForTokenClassification,
                  BioGptModel,
                  BioGptPreTrainedModel,
              )


else:
       import sys

       _lowerCamelCase      							=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








import doctest
from collections import deque

import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		[2, 1, 2, -1]
             _lowerCamelCase				:  Optional[int]						=		[1, 2, 3, 4]



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		len(self.first_signal)
             _lowerCamelCase				:  str						=		len(self.second_signal)

             _lowerCamelCase				:  str						=		max(a__   ,				a__)

             # create a zero matrix of max_length x max_length
             _lowerCamelCase				:  Optional[Any]						=		[[0] * max_length for i in range(a__)]

             # 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(a__):
                    _lowerCamelCase				:  Optional[Any]						=		deque(self.second_signal)
                    rotated_signal.rotate(a__)
                    for j, item in enumerate(a__):
                           matrix[i][j] += item

        # multiply the matrix with the first signal
             _lowerCamelCase				:  List[Any]						=		np.matmul(np.transpose(a__)   ,				np.transpose(self.first_signal))

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


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

