
'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase :  str    							=       {
    "configuration_xlm_roberta_xl": [
        "XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaXLConfig",
        "XLMRobertaXLOnnxConfig",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  List[str]    							=       [
							    "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "XLMRobertaXLForCausalLM",
							    "XLMRobertaXLForMaskedLM",
							    "XLMRobertaXLForMultipleChoice",
							    "XLMRobertaXLForQuestionAnswering",
							    "XLMRobertaXLForSequenceClassification",
							    "XLMRobertaXLForTokenClassification",
							    "XLMRobertaXLModel",
							    "XLMRobertaXLPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_xlm_roberta_xl import (
							    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XLMRobertaXLConfig,
							    XLMRobertaXLOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_roberta_xl import (
														    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMRobertaXLForCausalLM,
														    XLMRobertaXLForMaskedLM,
														    XLMRobertaXLForMultipleChoice,
														    XLMRobertaXLForQuestionAnswering,
														    XLMRobertaXLForSequenceClassification,
														    XLMRobertaXLForTokenClassification,
														    XLMRobertaXLModel,
														    XLMRobertaXLPreTrainedModel,
														)

else:
							import sys

							_lowercase :  Tuple    							=       _LazyModule(__name__, globals()["__file__"], _import_structure)








"""simple docstring"""
import math
class 				lowerCamelCase  :






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							list[list[float]]						,     snake_case:							list[int]						)   ->	int:
											snake_case_ :Any						=			0.0
											snake_case_ :Tuple						=			0.0
											for i in range(len(snake_case						)						):
																	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    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							list[list[int | float]]						,     snake_case:							list[int]						,     snake_case:							int						,     snake_case:							float						)   ->	list[list[int | float]]:
											for i in range(len(snake_case						)						):
																	weights[j][i] += alpha * (sample[i] - weights[j][i])
											return weights

def    A_  (			):







						'''simple docstring'''


						snake_case_ :Dict						=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

						for _ in range(_lowercase	):
												for j in range(len(_lowercase	)	):
																		# training sample
																		snake_case_ :List[Any]						=			training_samples[j]

																		# Compute the winning vector
																		snake_case_ :Optional[int]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

																		# Update the winning vector
																		snake_case_ :List[str]						=			self_organizing_map.update(_lowercase,					_lowercase,					_lowercase,					_lowercase	)

    # classify test sample
						snake_case_ :str						=			[0, 0, 0, 1]
						snake_case_ :List[Any]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

						# 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()
