
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a_											=						logging.get_logger(__name__)

a_											=						{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""beit"""





 def __init__(    self ,       lowercase_=81_92 ,       lowercase_=7_68 ,       lowercase_=12 ,       lowercase_=12 ,       lowercase_=30_72 ,       lowercase_="gelu" ,       lowercase_=0.0 ,       lowercase_=0.0 ,       lowercase_=0.02 ,       lowercase_=1E-12 ,       lowercase_=2_24 ,       lowercase_=16 ,       lowercase_=3 ,       lowercase_=False ,       lowercase_=False ,       lowercase_=False ,       lowercase_=False ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=True ,       lowercase_=[3, 5, 7, 11] ,       lowercase_=[1, 2, 3, 6] ,       lowercase_=True ,       lowercase_=0.4 ,       lowercase_=2_56 ,       lowercase_=1 ,       lowercase_=False ,       lowercase_=2_55 ,       **lowercase_ ,       ):
     super().__init__(**lowercase_)

     snake_case_ :    int     			=						vocab_size
     snake_case_ :    List[Any]     			=						hidden_size
     snake_case_ :    Tuple     			=						num_hidden_layers
     snake_case_ :    Optional[Any]     			=						num_attention_heads
     snake_case_ :    int     			=						intermediate_size
     snake_case_ :    Union[str, Any]     			=						hidden_act
     snake_case_ :    Optional[int]     			=						hidden_dropout_prob
     snake_case_ :    Optional[Any]     			=						attention_probs_dropout_prob
     snake_case_ :    Optional[int]     			=						initializer_range
     snake_case_ :    List[Any]     			=						layer_norm_eps

     snake_case_ :    List[str]     			=						image_size
     snake_case_ :    List[Any]     			=						patch_size
     snake_case_ :    Optional[Any]     			=						num_channels
     snake_case_ :    int     			=						use_mask_token
     snake_case_ :    int     			=						use_absolute_position_embeddings
     snake_case_ :    Any     			=						use_relative_position_bias
     snake_case_ :    Optional[int]     			=						use_shared_relative_position_bias
     snake_case_ :    Any     			=						layer_scale_init_value
     snake_case_ :    Dict     			=						drop_path_rate
     snake_case_ :    Tuple     			=						use_mean_pooling
     # decode head attributes (semantic segmentation)
     snake_case_ :    Any     			=						out_indices
     snake_case_ :    Dict     			=						pool_scales
     # auxiliary head attributes (semantic segmentation)
     snake_case_ :    Tuple     			=						use_auxiliary_head
     snake_case_ :    Optional[Any]     			=						auxiliary_loss_weight
     snake_case_ :    List[Any]     			=						auxiliary_channels
     snake_case_ :    str     			=						auxiliary_num_convs
     snake_case_ :    Union[str, Any]     			=						auxiliary_concat_input
     snake_case_ :    Any     			=						semantic_loss_ignore_index






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						version.parse("""1.11"""					)





 @property
 def        snake_case__		(    self):
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ])





 @property
 def        snake_case__		(    self):
     return 1E-4






'''simple docstring'''
import doctest
from collections import deque

import numpy as np






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    List[Any]     			=						[2, 1, 2, -1]
     snake_case_ :    int     			=						[1, 2, 3, 4]





 def        snake_case__		(    self):
     snake_case_ :    str     			=						len(self.first_signal)
     snake_case_ :    Any     			=						len(self.second_signal)

     snake_case_ :    List[Any]     			=						max(lowercase_ ,       lowercase_)

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

     # 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(lowercase_):
         snake_case_ :    List[str]     			=						deque(self.second_signal)
         rotated_signal.rotate(lowercase_)
         for j, item in enumerate(lowercase_):
             matrix[i][j] += item

        # multiply the matrix with the first signal
     snake_case_ :    Any     			=						np.matmul(np.transpose(lowercase_) ,       np.transpose(self.first_signal))

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


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




