"""simple docstring"""






import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				copy.deepcopy(lowerCamelCase			)
				UpperCAmelCase__			 =				sorted(lowerCamelCase			)
				UpperCAmelCase__			 =				[bisect.bisect_right(lowerCamelCase   ,     lowerCamelCase			) for y in x]
				return quantized



def     a_		(   lowerCamelCase   ,     lowerCamelCase=0			):
				UpperCAmelCase__			 =				np.arange(start=3   ,     stop=lowerCamelCase   ,     step=4			).tolist() if k > 0 else [1_0]
				UpperCAmelCase__			 =				_quantize(lowerCamelCase   ,     lowerCamelCase			)
				# count number of elements per group
				UpperCAmelCase__			 =				np.unique(lowerCamelCase   ,     return_counts=lowerCamelCase			)[1]
				UpperCAmelCase__			 =				[0] + bins + [np.inf]
				logger.info('Using {} as bins for aspect lengths quantization'.format(lowerCamelCase			)			)
				logger.info('Count of instances per bin: {}'.format(lowerCamelCase			)			)
				return groups







class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					def __init__(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Dict       ,lowerCamelCase__  :      Optional[Any]       ,lowerCamelCase__  :      List[str]     ):
									if not isinstance(lowerCamelCase__       ,lowerCamelCase__     ):
													raise ValueError(
													    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowerCamelCase__     )     )
									UpperCAmelCase__			 =				sampler
									UpperCAmelCase__			 =				group_ids
									UpperCAmelCase__			 =				batch_size







					def __iter__(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				defaultdict(lowerCamelCase__     )
									UpperCAmelCase__			 =				defaultdict(lowerCamelCase__     )

									UpperCAmelCase__			 =				0
									for idx in self.sampler:
													UpperCAmelCase__			 =				self.group_ids[idx]
													buffer_per_group[group_id].append(lowerCamelCase__     )
													samples_per_group[group_id].append(lowerCamelCase__     )
													if len(buffer_per_group[group_id]     ) == self.batch_size:
																	yield buffer_per_group[group_id]  # TODO
																	num_batches += 1
																	del buffer_per_group[group_id]
													assert len(buffer_per_group[group_id]     ) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									UpperCAmelCase__			 =				len(self     )
									UpperCAmelCase__			 =				expected_num_batches - num_batches
									if num_remaining > 0:
													# for the remaining batches, group the batches by similar lengths
													UpperCAmelCase__			 =				[]
													for group_id, idxs in sorted(buffer_per_group.items()       ,key=lambda lowerCamelCase__     : x[0]     ):
																	batch_idx.extend(lowerCamelCase__     )
																	if len(lowerCamelCase__     ) >= self.batch_size:
																					yield batch_idx[: self.batch_size]
																					UpperCAmelCase__			 =				batch_idx[self.batch_size :]
																					num_remaining -= 1
													if len(lowerCamelCase__     ) > 0:
																	yield batch_idx
																	num_remaining -= 1
									assert num_remaining == 0







					def __len__(							self  :      Union[str, Any]     ):
									return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size




"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase__    :    Optional[int]			=      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase__    :    Union[str, Any]			=      clf.predict(test)



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



