


"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_   :   Union[str, Any]									=  logging.get_logger(__name__)

lowerCamelCase_   :   Dict									=  {
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}






class       __A				(       _lowerCAmelCase			):






 """simple docstring"""




 __lowerCAmelCase 					=  """visual_bert"""


 def __init__(		self ,    __A=3_0522 ,    __A=768 ,    __A=512 ,    __A=12 ,    __A=12 ,    __A=3072 ,    __A="gelu" ,    __A=0.1 ,    __A=0.1 ,    __A=512 ,    __A=2 ,    __A=0.02 ,    __A=1E-1_2 ,    __A=False ,    __A=True ,    __A=1 ,    __A=0 ,    __A=2 ,    **__A ,    )					->     Union[str, Any]:
     super().__init__(pad_token_id=__A ,    bos_token_id=__A ,    eos_token_id=__A ,    **__A						)

     a 		=vocab_size
     a 		=max_position_embeddings
     a 		=hidden_size
     a 		=visual_embedding_dim
     a 		=num_hidden_layers
     a 		=num_attention_heads
     a 		=intermediate_size
     a 		=hidden_act
     a 		=hidden_dropout_prob
     a 		=attention_probs_dropout_prob
     a 		=initializer_range
     a 		=type_vocab_size
     a 		=layer_norm_eps
     a 		=bypass_transformer
     a 		=special_visual_initialize




"""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()
