

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    [
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

   _SCREAMING_SNAKE_CASE  :						Dict         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import math






class  UpperCAmelCase__	:






  """simple docstring"""


  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	list[list[float]]			,     __lowerCamelCase		:	list[int]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					0.0
       SCREAMING_SNAKE_CASE__					=					0.0
       for i in range(len(__lowerCamelCase      )      ):
            da += math.pow((sample[i] - weights[0][i])			,     2      )
            da += math.pow((sample[i] - weights[1][i])			,     2      )
            return 0 if da > da else 1
       return 0







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	list[list[int | float]]			,     __lowerCamelCase		:	list[int]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	float      )	->							list[list[int | float]]:
       for i in range(len(__lowerCamelCase      )      ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

     # weight initialization ( n, C )
     SCREAMING_SNAKE_CASE__					=					[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

     # training
     SCREAMING_SNAKE_CASE__					=					SelfOrganizingMap()
     SCREAMING_SNAKE_CASE__					=					3
     SCREAMING_SNAKE_CASE__					=					0.5

     for _ in range(_A       ):
          for j in range(len(_A       )       ):
               # training sample
               SCREAMING_SNAKE_CASE__					=					training_samples[j]

               # Compute the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

               # Update the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.update(_A   ,	_A   ,	_A   ,	_A       )

    # classify test sample
     SCREAMING_SNAKE_CASE__					=					[0, 0, 0, 1]
     SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

     # results
     print(F'''Clusters that the test sample belongs to : {winner}'''       )
     print(F'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
       main()


