
"""simple docstring"""





import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map



def 				lowerCamelCase__		(      __snake_case     )							->		List[Tuple[int, ...]]:


					"""simple docstring"""





					_UpperCamelCase										=  []
					if isinstance(UpperCamelCase__,					UpperCamelCase__     ):
										for v in tree.values():
															shapes.extend(_fetch_dims(UpperCamelCase__     )     )
					elif isinstance(UpperCamelCase__,					(list, tuple)     ):
										for t in tree:
															shapes.extend(_fetch_dims(UpperCamelCase__     )     )
					elif isinstance(UpperCamelCase__,					torch.Tensor     ):
										shapes.append(tree.shape     )
					else:
										raise ValueError('''Not supported'''     )

					return shapes



@torch.jit.ignore
def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Tuple[int, ...]:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for d in reversed(UpperCamelCase__     ):
										idx.append(flat_idx % d     )
										_UpperCamelCase										=  flat_idx // d

					return tuple(reversed(UpperCamelCase__     )     )



@torch.jit.ignore
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case = None,					__snake_case = None,					)							->		List[Tuple[slice, ...]]:


					"""simple docstring"""





					def reduce_edge_list(__snake_case     ) -> None:
										_UpperCamelCase										=  True
										for i in range(len(UpperCamelCase__     )     ):
															_UpperCamelCase										=  -1 * (i + 1)
															l[reversed_idx] &= tally
															_UpperCamelCase										=  l[reversed_idx]

					if start_edges is None:
										_UpperCamelCase										=  [s == 0 for s in start]
										reduce_edge_list(UpperCamelCase__     )
					if end_edges is None:
										_UpperCamelCase										=  [e == (d - 1) for e, d in zip(UpperCamelCase__,					UpperCamelCase__     )]
										reduce_edge_list(UpperCamelCase__     )

					# Base cases. Either start/end are empty and we're done, or the final,
					# one-dimensional tensor can be simply sliced
					if len(UpperCamelCase__     ) == 0:
										return [()]
					elif len(UpperCamelCase__     ) == 1:
										return [(slice(start[0],					end[0] + 1     ),)]

					_UpperCamelCase										=  []
					_UpperCamelCase										=  []

					# Dimensions common to start and end can be selected directly
					for s, e in zip(UpperCamelCase__,					UpperCamelCase__     ):
										if s == e:
															path_list.append(slice(UpperCamelCase__,					s + 1     )     )
										else:
															break

					_UpperCamelCase										=  tuple(UpperCamelCase__     )
					_UpperCamelCase										=  len(UpperCamelCase__     )

					# start == end, and we're done
					if divergence_idx == len(UpperCamelCase__     ):
										return [path]

					def upper() -> Tuple[Tuple[slice, ...], ...]:
										assert start_edges is not None
										assert end_edges is not None

										_UpperCamelCase										=  start[divergence_idx]
										return tuple(
										    path + (slice(UpperCamelCase__,					sdi + 1     ),) + s
										    for s in _get_minimal_slice_set(
										        start[divergence_idx + 1 :],					[d - 1 for d in dims[divergence_idx + 1 :]],					dims[divergence_idx + 1 :],					start_edges=start_edges[divergence_idx + 1 :],					end_edges=[True for _ in end_edges[divergence_idx + 1 :]],					)     )

					def lower() -> Tuple[Tuple[slice, ...], ...]:
										assert start_edges is not None
										assert end_edges is not None

										_UpperCamelCase										=  end[divergence_idx]
										return tuple(
										    path + (slice(UpperCamelCase__,					edi + 1     ),) + s
										    for s in _get_minimal_slice_set(
										        [0 for _ in start[divergence_idx + 1 :]],					end[divergence_idx + 1 :],					dims[divergence_idx + 1 :],					start_edges=[True for _ in start_edges[divergence_idx + 1 :]],					end_edges=end_edges[divergence_idx + 1 :],					)     )

					# If both start and end are at the edges of the subtree rooted at
					# divergence_idx, we can just select the whole subtree at once
					if start_edges[divergence_idx] and end_edges[divergence_idx]:
										slices.append(path + (slice(start[divergence_idx],					end[divergence_idx] + 1     ),)     )
					# If just start is at the edge, we can grab almost all of the subtree,
					# treating only the ragged bottom edge as an edge case
					elif start_edges[divergence_idx]:
										slices.append(path + (slice(start[divergence_idx],					end[divergence_idx]     ),)     )
										slices.extend(lower()     )
					# Analogous to the previous case, but the top is ragged this time
					elif end_edges[divergence_idx]:
										slices.extend(upper()     )
										slices.append(path + (slice(start[divergence_idx] + 1,					end[divergence_idx] + 1     ),)     )
					# If both sides of the range are ragged, we need to handle both sides
					# separately. If there's contiguous meat in between them, we can index it
					# in one big chunk
					else:
										slices.extend(upper()     )
										_UpperCamelCase										=  end[divergence_idx] - start[divergence_idx]
										if middle_ground > 1:
															slices.append(path + (slice(start[divergence_idx] + 1,					end[divergence_idx]     ),)     )
										slices.extend(lower()     )

					return slices



@torch.jit.ignore
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case     )							->		torch.Tensor:


					"""simple docstring"""





					_UpperCamelCase										=  t.shape[:no_batch_dims]
					_UpperCamelCase										=  list(_flat_idx_to_idx(UpperCamelCase__,					UpperCamelCase__     )     )
					# _get_minimal_slice_set is inclusive
					_UpperCamelCase										=  list(_flat_idx_to_idx(flat_end - 1,					UpperCamelCase__     )     )

					# Get an ordered list of slices to perform
					_UpperCamelCase										=  _get_minimal_slice_set(
					    UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__,					)

					_UpperCamelCase										=  [t[s] for s in slices]

					return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]     ) for s in sliced_tensors]     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case = False,					__snake_case = None,					__snake_case = False,					)							->		Any:


					"""simple docstring"""





					if not (len(UpperCamelCase__     ) > 0):
										raise ValueError('''Must provide at least one input'''     )

					_UpperCamelCase										=  [shape[:no_batch_dims] for shape in _fetch_dims(UpperCamelCase__     )]
					_UpperCamelCase										=  tuple([max(UpperCamelCase__     ) for s in zip(*UpperCamelCase__     )]     )

					def _prep_inputs(__snake_case     ) -> torch.Tensor:
										if not low_mem:
															if not sum(t.shape[:no_batch_dims]     ) == no_batch_dims:
																				_UpperCamelCase										=  t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
															_UpperCamelCase										=  t.reshape(-1,					*t.shape[no_batch_dims:]     )
										else:
															_UpperCamelCase										=  t.expand(orig_batch_dims + t.shape[no_batch_dims:]     )
										return t

					_UpperCamelCase										=  tensor_tree_map(_prep_inputs,					UpperCamelCase__     )
					_UpperCamelCase										=  None
					if _out is not None:
										_UpperCamelCase										=  tensor_tree_map(lambda __snake_case     : t.view([-1] + list(t.shape[no_batch_dims:]     )     ),					_out     )

					_UpperCamelCase										=  1
					for d in orig_batch_dims:
										flat_batch_dim *= d

					_UpperCamelCase										=  flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

					def _select_chunk(__snake_case     ) -> torch.Tensor:
										return t[i : i + chunk_size] if t.shape[0] != 1 else t

					_UpperCamelCase										=  0
					_UpperCamelCase										=  prepped_outputs
					for _ in range(UpperCamelCase__     ):
										# Chunk the input
										if not low_mem:
															_UpperCamelCase										=  _select_chunk
										else:
															_UpperCamelCase										=  partial(
															    _chunk_slice,					flat_start=UpperCamelCase__,					flat_end=min(UpperCamelCase__,					i + chunk_size     ),					no_batch_dims=len(UpperCamelCase__     ),					)

										_UpperCamelCase										=  tensor_tree_map(UpperCamelCase__,					UpperCamelCase__     )

										# Run the layer on the chunk
										_UpperCamelCase										=  layer(**UpperCamelCase__     )

										# Allocate space for the output
										if out is None:
															_UpperCamelCase										=  tensor_tree_map(lambda __snake_case     : t.new_zeros((flat_batch_dim,) + t.shape[1:]     ),					UpperCamelCase__     )

										# Put the chunk in its pre-allocated space
										if isinstance(UpperCamelCase__,					UpperCamelCase__     ):

															def assign(__snake_case,					__snake_case     ) -> None:
																				for k, v in da.items():
																									if isinstance(UpperCamelCase__,					UpperCamelCase__     ):
																														assign(UpperCamelCase__,					da[k]     )
																									else:
																														if _add_into_out:
																																			v[i : i + chunk_size] += da[k]
																														else:
																																			_UpperCamelCase										=  da[k]

															assign(UpperCamelCase__,					UpperCamelCase__     )
										elif isinstance(UpperCamelCase__,					UpperCamelCase__     ):
															for xa, xa in zip(UpperCamelCase__,					UpperCamelCase__     ):
																				if _add_into_out:
																									xa[i : i + chunk_size] += xa
																				else:
																									_UpperCamelCase										=  xa
										elif isinstance(UpperCamelCase__,					torch.Tensor     ):
															if _add_into_out:
																				out[i : i + chunk_size] += output_chunk
															else:
																				_UpperCamelCase										=  output_chunk
										else:
															raise ValueError('''Not supported'''     )

										i += chunk_size

					_UpperCamelCase										=  tensor_tree_map(lambda __snake_case     : t.view(orig_batch_dims + t.shape[1:]     ),					UpperCamelCase__     )

					return out






class    _UpperCAmelCase:



		def __init__( self    ,		__a = 5_12    ,		)	->	List[str]:
							_UpperCamelCase										=  max_chunk_size
							_UpperCamelCase										=  None
							_UpperCamelCase										=  None



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Optional[int]:
							logging.info('''Tuning chunk size...''')

							if min_chunk_size >= self.max_chunk_size:
												return min_chunk_size

							_UpperCamelCase										=  [2**l for l in range(int(math.log(self.max_chunk_size    ,		2)) + 1)]
							_UpperCamelCase										=  [c for c in candidates if c > min_chunk_size]
							_UpperCamelCase										=  [min_chunk_size] + candidates
							candidates[-1] += 4

							def test_chunk_size(__a) -> bool:
												try:
																	with torch.no_grad():
																						fn(*__lowerCamelCase    ,		chunk_size=__lowerCamelCase)
																	return True
												except RuntimeError:
																	return False

							_UpperCamelCase										=  0
							_UpperCamelCase										=  len(__lowerCamelCase) - 1
							while i > min_viable_chunk_size_index:
												_UpperCamelCase										=  test_chunk_size(candidates[i])
												if not viable:
																	_UpperCamelCase										=  (min_viable_chunk_size_index + i) // 2
												else:
																	_UpperCamelCase										=  i
																	_UpperCamelCase										=  (i + len(__lowerCamelCase) - 1) // 2

							return candidates[min_viable_chunk_size_index]



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Optional[int]:
							_UpperCamelCase										=  True
							for aa, aa in zip(__lowerCamelCase    ,		__lowerCamelCase):
												assert type(__lowerCamelCase) == type(__lowerCamelCase)
												if isinstance(__lowerCamelCase    ,		(list, tuple)):
																	consistent &= self._compare_arg_caches(__lowerCamelCase    ,		__lowerCamelCase)
												elif isinstance(__lowerCamelCase    ,		__lowerCamelCase):
																	_UpperCamelCase										=  [v for _, v in sorted(aa.items()    ,		key=lambda __a: x[0])]
																	_UpperCamelCase										=  [v for _, v in sorted(aa.items()    ,		key=lambda __a: x[0])]
																	consistent &= self._compare_arg_caches(__lowerCamelCase    ,		__lowerCamelCase)
												else:
																	consistent &= aa == aa

							return consistent




		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		)	->	str:
							_UpperCamelCase										=  True
							_UpperCamelCase										=  tree_map(lambda __a: a.shape if isinstance(__lowerCamelCase    ,		torch.Tensor) else a    ,		__lowerCamelCase    ,		__lowerCamelCase)
							if self.cached_arg_data is not None:
												# If args have changed shape/value, we need to re-tune
												assert len(self.cached_arg_data) == len(__lowerCamelCase)
												_UpperCamelCase										=  self._compare_arg_caches(self.cached_arg_data    ,		__lowerCamelCase)
							else:
												# Otherwise, we can reuse the precomputed value
												_UpperCamelCase										=  False

							if not consistent:
												_UpperCamelCase										=  self._determine_favorable_chunk_size(
												    __lowerCamelCase    ,		__lowerCamelCase    ,		__lowerCamelCase    ,		)
												_UpperCamelCase										=  arg_data

							assert self.cached_chunk_size is not None

							return self.cached_chunk_size








"""simple docstring"""





class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  name
							_UpperCamelCase										=  value
							_UpperCamelCase										=  weight



		def __repr__( self)	->	List[str]:






							'''simple docstring'''
							return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							return self.value



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							return self.name



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.weight




		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							return self.value / self.weight





def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for i in range(len(__snake_case     )     ):
										menu.append(Things(name[i],					value[i],					weight[i]     )     )
					return menu



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  sorted(__snake_case,					key=__snake_case,					reverse=__snake_case     )
					_UpperCamelCase										=  []
					_UpperCamelCase			,  _UpperCamelCase										=  0.0, 0.0
					for i in range(len(__snake_case     )     ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]     )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def 				lowerCamelCase__		(      )							->		List[str]:


					"""simple docstring"""







if __name__ == "__main__":
				import doctest

				doctest.testmod()






