import math





class 					lowercase__						:


						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				list[list[float]]    ,		UpperCamelCase__	:				list[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							0.0
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							0.0
												for i in range(len(UpperCamelCase__						)						):
																		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        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				list[list[int | float]]    ,		UpperCamelCase__	:				list[int]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				float						):





												'''simple docstring'''





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






def       A	(    ):
						# Training Examples ( m, n )
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

						# training
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							SelfOrganizingMap()
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							3
						SCREAMING_SNAKE_CASE							:    List[Any]							=							0.5

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

																		# Compute the winning vector
																		SCREAMING_SNAKE_CASE							:    int							=							self_organizing_map.get_winner(_lowercase		,  _lowercase						)

																		# Update the winning vector
																		SCREAMING_SNAKE_CASE							:    str							=							self_organizing_map.update(_lowercase		,  _lowercase		,  _lowercase		,  _lowercase						)

    # classify test sample
						SCREAMING_SNAKE_CASE							:    str							=							[0, 0, 0, 1]
						SCREAMING_SNAKE_CASE							:    List[str]							=							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()






from typing import TYPE_CHECKING

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


__UpperCamelCase		:       Any									=     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     ['MBartTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Dict									=     ['MBartTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MBartForCausalLM',
					    'MBartForConditionalGeneration',
					    'MBartForQuestionAnswering',
					    'MBartForSequenceClassification',
					    'MBartModel',
					    'MBartPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'TFMBartForConditionalGeneration',
					    'TFMBartModel',
					    'TFMBartPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'FlaxMBartForConditionalGeneration',
					    'FlaxMBartForQuestionAnswering',
					    'FlaxMBartForSequenceClassification',
					    'FlaxMBartModel',
					    'FlaxMBartPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart import MBartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart_fast import MBartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mbart import (
										    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MBartForCausalLM,
										    MBartForConditionalGeneration,
										    MBartForQuestionAnswering,
										    MBartForSequenceClassification,
										    MBartModel,
										    MBartPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mbart import (
										    FlaxMBartForConditionalGeneration,
										    FlaxMBartForQuestionAnswering,
										    FlaxMBartForSequenceClassification,
										    FlaxMBartModel,
										    FlaxMBartPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





