



'''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 			_lowerCAmelCase(      UpperCAmelCase_      :	Optional[Any]					, UpperCAmelCase_      :	str						) ->							int:
							lowerCAmelCase__  =   copy.deepcopy(UpperCAmelCase_						)
							lowerCAmelCase__  =   sorted(UpperCAmelCase_						)
							lowerCAmelCase__  =   [bisect.bisect_right(UpperCAmelCase_					, UpperCAmelCase_						) for y in x]
							return quantized
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Optional[Any]					, UpperCAmelCase_      :	Tuple=0						) ->							List[Any]:
							lowerCAmelCase__  =   np.arange(start=3					, stop=UpperCAmelCase_					, step=4						).tolist() if k > 0 else [10]
							lowerCAmelCase__  =   _quantize(UpperCAmelCase_					, UpperCAmelCase_						)
							# count number of elements per group
							lowerCAmelCase__  =   np.unique(UpperCAmelCase_					, return_counts=UpperCAmelCase_						)[1]
							lowerCAmelCase__  =   [0] + bins + [np.inf]
							logger.info("""Using {} as bins for aspect lengths quantization""".format(UpperCAmelCase_						)						)
							logger.info("""Count of instances per bin: {}""".format(UpperCAmelCase_						)						)
							return groups






class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	def __init__(  self		:  Union[str, Any]   ,		__A		:  Any   ,		__A		:  Dict   ,		__A		:  List[Any]					)     ->			Any:




								'''simple docstring'''





								if not isinstance(__A   ,		__A					):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(__A					)					)
								lowerCAmelCase__  =   sampler
								lowerCAmelCase__  =   group_ids
								lowerCAmelCase__  =   batch_size




	def __iter__(  self		:  Dict					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   defaultdict(__A					)
								lowerCAmelCase__  =   defaultdict(__A					)

								lowerCAmelCase__  =   0
								for idx in self.sampler:
															lowerCAmelCase__  =   self.group_ids[idx]
															buffer_per_group[group_id].append(__A					)
															samples_per_group[group_id].append(__A					)
															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
								lowerCAmelCase__  =   len(self					)
								lowerCAmelCase__  =   expected_num_batches - num_batches
								if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															lowerCAmelCase__  =   []
															for group_id, idxs in sorted(buffer_per_group.items()   ,		key=lambda __A					: x[0]					):
																						batch_idx.extend(__A					)
																						if len(__A					) >= self.batch_size:
																													yield batch_idx[: self.batch_size]
																													lowerCAmelCase__  =   batch_idx[self.batch_size :]
																													num_remaining -= 1
															if len(__A					) > 0:
																						yield batch_idx
																						num_remaining -= 1
								assert num_remaining == 0




	def __len__(  self		:  List[str]					)     ->			Optional[Any]:




								'''simple docstring'''





								return (len(self.sampler					) + self.batch_size - 1) // self.batch_size










'''simple docstring'''







from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	bool						) ->							torch.Tensor:
							# The first operation in a checkpoint can't be in-place, but it's
							# nice to have in-place addition during inference. Thus...
							if not inplace:
														lowerCAmelCase__  =   ma + ma
							else:
														ma += ma

							return ma
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	List[int]						) ->							torch.Tensor:
							lowerCAmelCase__  =   -1 * len(UpperCAmelCase_						)
							lowerCAmelCase__  =   list(range(len(tensor.shape[:zero_index]						)						)						)
							return tensor.permute(first_inds + [zero_index + i for i in inds]						)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	int						) ->							torch.Tensor:
							return t.reshape(t.shape[:-no_dims] + (-1,)						)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	float = 1E-4						) ->							torch.Tensor:
							lowerCAmelCase__  =   mask.expand(*value.shape						)
							return torch.sum(mask * value					, dim=UpperCAmelCase_						) / (eps + torch.sum(UpperCAmelCase_					, dim=UpperCAmelCase_						))
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.types.Number = 2.31_25					, UpperCAmelCase_      :	torch.types.Number = 21.68_75					, UpperCAmelCase_      :	int = 64						) ->							torch.Tensor:
							lowerCAmelCase__  =   torch.linspace(UpperCAmelCase_					, UpperCAmelCase_					, no_bins - 1					, device=pts.device						)
							lowerCAmelCase__  =   torch.sqrt(torch.sum((pts.unsqueeze(-2						) - pts.unsqueeze(-3						)) ** 2					, dim=-1						)						)
							return torch.bucketize(UpperCAmelCase_					, UpperCAmelCase_						)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[list], Any]					, UpperCAmelCase_      :	List[dict]						) ->							dict:
							lowerCAmelCase__  =   dicts[0]
							lowerCAmelCase__  =   {}
							for k, v in first.items():
														lowerCAmelCase__  =   [d[k] for d in dicts]
														if isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
																					lowerCAmelCase__  =   dict_multimap(UpperCAmelCase_					, UpperCAmelCase_						)
														else:
																					lowerCAmelCase__  =   fn(UpperCAmelCase_						)

							return new_dict
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.Tensor						) ->							torch.Tensor:
							lowerCAmelCase__  =   v_bins.view(((1,) * len(x.shape						)) + (len(UpperCAmelCase_						),)						)
							lowerCAmelCase__  =   x[..., None] - reshaped_bins
							lowerCAmelCase__  =   torch.argmin(torch.abs(UpperCAmelCase_						)					, dim=-1						)
							return nn.functional.one_hot(UpperCAmelCase_					, num_classes=len(UpperCAmelCase_						)						).float()
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	int = 0					, UpperCAmelCase_      :	int = 0						) ->							torch.Tensor:
							lowerCAmelCase__  =   []
							for i, s in enumerate(data.shape[:no_batch_dims]						):
														lowerCAmelCase__  =   torch.arange(UpperCAmelCase_						)
														lowerCAmelCase__  =   r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape						) - i - 1)))						)
														ranges.append(UpperCAmelCase_						)

							lowerCAmelCase__  =   [slice(UpperCAmelCase_						) for _ in range(len(data.shape						) - no_batch_dims						)]
							lowerCAmelCase__  =   inds
							ranges.extend(UpperCAmelCase_						)
							# Matt note: Editing this to get around the behaviour of using a list as an array index changing
							# in recent Numpy versions
							return data[tuple(UpperCAmelCase_						)]


_UpperCamelCase  						=			TypeVar("""T""")
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	Dict[Any, Union[dict, list, tuple, T]]					, UpperCAmelCase_      :	Type[T]						) ->							Dict[Any, Union[dict, list, tuple, Any]]:
							lowerCAmelCase__  =   {}
							for k, v in dic.items():
														if isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
																					lowerCAmelCase__  =   dict_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						)
														else:
																					lowerCAmelCase__  =   tree_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						)

							return new_dict
@overload
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	T					, UpperCAmelCase_      :	Type[T]						) ->							Any:
							...
@overload
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	dict					, UpperCAmelCase_      :	Type[T]						) ->							dict:
							...
@overload
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	list					, UpperCAmelCase_      :	Type[T]						) ->							list:
							...
@overload
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	tuple					, UpperCAmelCase_      :	Type[T]						) ->							tuple:
							...
def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	Dict					, UpperCAmelCase_      :	Union[str, Any]						) ->							List[str]:
							if isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
														return dict_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						)
							elif isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
														return [tree_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						) for x in tree]
							elif isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
														return tuple(tree_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						) for x in tree						)
							elif isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
														return fn(UpperCAmelCase_						)
							else:
														print(type(UpperCAmelCase_						)						)
														raise ValueError("""Not supported"""						)






_UpperCamelCase  						=			partial(tree_map, leaf_type=torch.Tensor)





