

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


_SCREAMING_SNAKE_CASE  :						List[str]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						Tuple         =    {
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}






class  UpperCAmelCase__	( A__ ):






		"""simple docstring"""


		a     =      "data2vec-vision"






		def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	Optional[Any]=768			,     __lowerCamelCase		:	Optional[Any]=12			,     __lowerCamelCase		:	List[Any]=12			,     __lowerCamelCase		:	str=3072			,     __lowerCamelCase		:	Tuple="gelu"			,     __lowerCamelCase		:	Dict=0.0			,     __lowerCamelCase		:	Union[str, Any]=0.0			,     __lowerCamelCase		:	List[Any]=0.02			,     __lowerCamelCase		:	int=1e-12			,     __lowerCamelCase		:	List[str]=224			,     __lowerCamelCase		:	Optional[int]=16			,     __lowerCamelCase		:	Dict=3			,     __lowerCamelCase		:	Dict=False			,     __lowerCamelCase		:	str=False			,     __lowerCamelCase		:	Optional[int]=False			,     __lowerCamelCase		:	Union[str, Any]=False			,     __lowerCamelCase		:	Dict=0.1			,     __lowerCamelCase		:	Any=0.1			,     __lowerCamelCase		:	Union[str, Any]=True			,     __lowerCamelCase		:	List[str]=[3, 5, 7, 11]			,     __lowerCamelCase		:	Optional[Any]=[1, 2, 3, 6]			,     __lowerCamelCase		:	Optional[int]=True			,     __lowerCamelCase		:	int=0.4			,     __lowerCamelCase		:	Tuple=256			,     __lowerCamelCase		:	Optional[Any]=1			,     __lowerCamelCase		:	List[Any]=False			,     __lowerCamelCase		:	Union[str, Any]=255			,     **__lowerCamelCase		:	Any			,     )	->							Any:
							super().__init__(**__lowerCamelCase      )

							SCREAMING_SNAKE_CASE__					=					hidden_size
							SCREAMING_SNAKE_CASE__					=					num_hidden_layers
							SCREAMING_SNAKE_CASE__					=					num_attention_heads
							SCREAMING_SNAKE_CASE__					=					intermediate_size
							SCREAMING_SNAKE_CASE__					=					hidden_act
							SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
							SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE__					=					initializer_range
							SCREAMING_SNAKE_CASE__					=					layer_norm_eps

							SCREAMING_SNAKE_CASE__					=					image_size
							SCREAMING_SNAKE_CASE__					=					patch_size
							SCREAMING_SNAKE_CASE__					=					num_channels
							SCREAMING_SNAKE_CASE__					=					use_mask_token
							SCREAMING_SNAKE_CASE__					=					use_absolute_position_embeddings
							SCREAMING_SNAKE_CASE__					=					use_relative_position_bias
							SCREAMING_SNAKE_CASE__					=					use_shared_relative_position_bias
							SCREAMING_SNAKE_CASE__					=					layer_scale_init_value
							SCREAMING_SNAKE_CASE__					=					drop_path_rate
							SCREAMING_SNAKE_CASE__					=					use_mean_pooling
							# decode head attributes (semantic segmentation)
							SCREAMING_SNAKE_CASE__					=					out_indices
							SCREAMING_SNAKE_CASE__					=					pool_scales
							# auxiliary head attributes (semantic segmentation)
							SCREAMING_SNAKE_CASE__					=					use_auxiliary_head
							SCREAMING_SNAKE_CASE__					=					auxiliary_loss_weight
							SCREAMING_SNAKE_CASE__					=					auxiliary_channels
							SCREAMING_SNAKE_CASE__					=					auxiliary_num_convs
							SCREAMING_SNAKE_CASE__					=					auxiliary_concat_input
							SCREAMING_SNAKE_CASE__					=					semantic_loss_ignore_index






class  UpperCAmelCase__	( A__ ):






		"""simple docstring"""


		a     =      version.parse("1.11" )






		@property
		def    lowercase_	(							self		:	Dict      )	->							Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]      )







		@property
		def    lowercase_	(							self		:	List[str]      )	->							float:
							return 1e-4





import doctest
from collections import deque

import numpy as np






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]      )	->							None:
       SCREAMING_SNAKE_CASE__					=					[2, 1, 2, -1]
       SCREAMING_SNAKE_CASE__					=					[1, 2, 3, 4]







  def    lowercase_	(							self		:	List[str]      )	->							list[float]:
       SCREAMING_SNAKE_CASE__					=					len(self.first_signal      )
       SCREAMING_SNAKE_CASE__					=					len(self.second_signal      )

       SCREAMING_SNAKE_CASE__					=					max(__lowerCamelCase			,     __lowerCamelCase      )

       # create a zero matrix of max_length x max_length
       SCREAMING_SNAKE_CASE__					=					[[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      ):
            SCREAMING_SNAKE_CASE__					=					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
       SCREAMING_SNAKE_CASE__					=					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()


