


'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__    					=      {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''LayoutLMv3TokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''LayoutLMv3ForQuestionAnswering''',
      '''LayoutLMv3ForSequenceClassification''',
      '''LayoutLMv3ForTokenClassification''',
      '''LayoutLMv3Model''',
      '''LayoutLMv3PreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFLayoutLMv3ForQuestionAnswering''',
      '''TFLayoutLMv3ForSequenceClassification''',
      '''TFLayoutLMv3ForTokenClassification''',
      '''TFLayoutLMv3Model''',
      '''TFLayoutLMv3PreTrainedModel''',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''LayoutLMv3FeatureExtractor''']
  UpperCamelCase__    					=      ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
  from .configuration_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
      LayoutLMvaConfig,
      LayoutLMvaOnnxConfig,
  )
  from .processing_layoutlmva import LayoutLMvaProcessor
  from .tokenization_layoutlmva import LayoutLMvaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlmva import (
        LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMvaForQuestionAnswering,
        LayoutLMvaForSequenceClassification,
        LayoutLMvaForTokenClassification,
        LayoutLMvaModel,
        LayoutLMvaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlmva import (
        TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMvaForQuestionAnswering,
        TFLayoutLMvaForSequenceClassification,
        TFLayoutLMvaForTokenClassification,
        TFLayoutLMvaModel,
        TFLayoutLMvaPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
    from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
  import sys

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




'''simple docstring'''
import math




class 			lowerCamelCase_   :






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    list[list[float]]						,						_A				:    list[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				0.0
          UpperCAmelCase__			:		Union[str, Any]													=				0.0
          for i in range(len(_A ) ):
              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				:    Dict						,						_A				:    list[list[int | float]]						,						_A				:    list[int]						,						_A				:    int						,						_A				:    float ):


          '''simple docstring'''


          for i in range(len(_A ) ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
          return weights





def 					a__       (					)    ->	None:
    # Training Examples ( m, n )
    UpperCAmelCase__			:		Optional[Any]													=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

    # training
    UpperCAmelCase__			:		Optional[Any]													=				SelfOrganizingMap()
    UpperCAmelCase__			:		Tuple													=				3
    UpperCAmelCase__			:		Dict													=				0.5

    for _ in range(lowerCAmelCase__						):
        for j in range(len(lowerCAmelCase__						)						):
            # training sample
            UpperCAmelCase__			:		Optional[int]													=				training_samples[j]

            # Compute the winning vector
            UpperCAmelCase__			:		str													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

            # Update the winning vector
            UpperCAmelCase__			:		int													=				self_organizing_map.update(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    # classify test sample
    UpperCAmelCase__			:		str													=				[0, 0, 0, 1]
    UpperCAmelCase__			:		int													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

    # 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()
