






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


UpperCamelCase        =				logging.get_logger(__name__)

UpperCamelCase        =				{}
UpperCamelCase        =				{}
UpperCamelCase        =				{}




def 				A		(					lowercase__  :  type ,		lowercase__  :  Optional[str] ,		lowercase__  :  Optional[List[str]] = None ,		)		-> int:
	UpperCamelCase__ :Union[str, Any]						=      aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""  )
	UpperCamelCase__ :Union[str, Any]						=      formatter_cls
	for alias in set(aliases + [format_type]  ):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""  )
		UpperCamelCase__ :List[Any]						=      format_type




def 				A		(					lowercase__  :  Exception ,		lowercase__  :  Optional[str] ,		lowercase__  :  Optional[List[str]] = None  )		-> List[Any]:
	UpperCamelCase__ :Any						=      aliases if aliases is not None else []
	for alias in set(aliases + [format_type]  ):
		UpperCamelCase__ :Optional[int]						=      unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
							UpperCamelCase        =				ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
							_register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
							UpperCamelCase        =				ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
							_register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, "jax", aliases=[])
else:
							UpperCamelCase        =				ValueError("JAX needs to be installed to be able to return JAX arrays.")
							_register_unavailable_formatter(_jax_error, "jax", aliases=[])




def 				A		(					lowercase__  :  Optional[str]  )		-> Optional[str]:
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def 				A		(					lowercase__  :  Optional[str] ,		**lowercase__  :  Optional[Any]  )		-> Formatter:
	UpperCamelCase__ :Union[str, Any]						=      get_format_type_from_alias(lowercase__  )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowercase__  )
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None  )}, but got '{format_type}'"""  )

import math




class   __lowercase						:






	def       _a(self				: Dict      ,	snake_case				: list[list[float]]      ,	snake_case				: list[int]    ) ->			int:
							_lowercase       :	List[Any]           =   0.0
							_lowercase       :	Any           =   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       _a(self				: Dict      ,	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 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

																		# Compute the winning vector
																		_lowercase       :	Tuple           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

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

    # classify test sample
						_lowercase       :	Any           =   [0, 0, 0, 1]
						_lowercase       :	Dict           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

						# 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()




