





from typing import TYPE_CHECKING

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


__magic_name__   :						List[str]    =			{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			["""PoolFormerFeatureExtractor"""]
 __magic_name__   :						Any    =			["""PoolFormerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			[
     """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """PoolFormerForImageClassification""",
     """PoolFormerModel""",
     """PoolFormerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_poolformer import (
     POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     PoolFormerConfig,
     PoolFormerOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_poolformer import PoolFormerFeatureExtractor
  from .image_processing_poolformer import PoolFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_poolformer import (
      POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PoolFormerForImageClassification,
      PoolFormerModel,
      PoolFormerPreTrainedModel,
  )


else:
 import sys

 __magic_name__   :						List[str]    =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)









'''simple docstring'''



import math


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				0.0
       _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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       for i in range(len(lowerCamelCase	)	):
         weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

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

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

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

  # 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()


