


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        UpperCamelCase_(				_A   :Dict  )->		List[Tuple[int, ...]]:
			UpperCamelCase__					=       []
			if isinstance(lowerCamelCase_				,     lowerCamelCase_  ):
						for v in tree.values():
									shapes.extend(_fetch_dims(lowerCamelCase_  )  )
			elif isinstance(lowerCamelCase_				,     (list, tuple)  ):
						for t in tree:
									shapes.extend(_fetch_dims(lowerCamelCase_  )  )
			elif isinstance(lowerCamelCase_				,     torch.Tensor  ):
						shapes.append(tree.shape  )
			else:
						raise ValueError("Not supported"  )

			return shapes
@torch.jit.ignore
def        UpperCamelCase_(				_A   :Optional[int]				,     _A   :Union[str, Any]  )->		Tuple[int, ...]:
			UpperCamelCase__					=       []
			for d in reversed(lowerCamelCase_  ):
						idx.append(flat_idx % d  )
						UpperCamelCase__					=       flat_idx // d

			return tuple(reversed(lowerCamelCase_  )  )
@torch.jit.ignore
def        UpperCamelCase_(				_A   :Dict				,     _A   :Dict				,     _A   :Union[str, Any]				,     _A   :Union[str, Any] = None				,     _A   :Tuple = None				,     )->		List[Tuple[slice, ...]]:

			# start_edges and end_edges both indicate whether, starting from any given
			# dimension, the start/end index is at the top/bottom edge of the
			# corresponding tensor, modeled as a tree
			def reduce_edge_list(_A   :Union[str, Any]  ) -> None:
						UpperCamelCase__					=       True
						for i in range(len(lowerCamelCase_  )  ):
									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(lowerCamelCase_  )
			if end_edges is None:
						UpperCamelCase__					=       [e == (d - 1) for e, d in zip(lowerCamelCase_				,     lowerCamelCase_  )]
						reduce_edge_list(lowerCamelCase_  )

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

			UpperCamelCase__					=       tuple(lowerCamelCase_  )
			UpperCamelCase__					=       len(lowerCamelCase_  )

			# start == end, and we're done
			if divergence_idx == len(lowerCamelCase_  ):
						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(lowerCamelCase_				,     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(lowerCamelCase_				,     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        UpperCamelCase_(				_A   :Optional[int]				,     _A   :Optional[Any]				,     _A   :Union[str, Any]				,     _A   :Any  )->		torch.Tensor:
			UpperCamelCase__					=       t.shape[:no_batch_dims]
			UpperCamelCase__					=       list(_flat_idx_to_idx(lowerCamelCase_				,     lowerCamelCase_  )  )
			# _get_minimal_slice_set is inclusive
			UpperCamelCase__					=       list(_flat_idx_to_idx(flat_end - 1				,     lowerCamelCase_  )  )

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

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

			return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]  ) for s in sliced_tensors]  )
def        UpperCamelCase_(				_A   :Any				,     _A   :List[str]				,     _A   :str				,     _A   :Union[str, Any]				,     _A   :Optional[Any] = False				,     _A   :Dict = None				,     _A   :List[str] = False				,     )->		Any:
			if not (len(lowerCamelCase_  ) > 0):
						raise ValueError("Must provide at least one input"  )

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

			def _prep_inputs(_A   :Any  ) -> 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				,     lowerCamelCase_  )
			UpperCamelCase__					=       None
			if _out is not None:
						UpperCamelCase__					=       tensor_tree_map(lambda _A  : 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(_A   :str  ) -> torch.Tensor:
						return t[i : i + chunk_size] if t.shape[0] != 1 else t

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

						UpperCamelCase__					=       tensor_tree_map(lowerCamelCase_				,     lowerCamelCase_  )

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

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

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

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

									assign(lowerCamelCase_				,     lowerCamelCase_  )
						elif isinstance(lowerCamelCase_				,     lowerCamelCase_  ):
									for xa, xa in zip(lowerCamelCase_				,     lowerCamelCase_  ):
												if _add_into_out:
															xa[i : i + chunk_size] += xa
												else:
															UpperCamelCase__					=       xa
						elif isinstance(lowerCamelCase_				,     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 _A  : t.view(orig_batch_dims + t.shape[1:]  )				,     lowerCamelCase_  )

			return out







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case = 512      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       max_chunk_size
						UpperCamelCase__					=       None
						UpperCamelCase__					=       None




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case				):

						'''simple docstring'''


						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(snake_case				) -> bool:
									try:
												with torch.no_grad():
															fn(*UpperCAmelCase_      ,						chunk_size=UpperCAmelCase_				)
												return True
									except RuntimeError:
												return False

						UpperCamelCase__					=       0
						UpperCamelCase__					=       len(UpperCAmelCase_				) - 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(UpperCAmelCase_				) - 1) // 2

						return candidates[min_viable_chunk_size_index]




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case				):

						'''simple docstring'''


						UpperCamelCase__					=       True
						for aa, aa in zip(UpperCAmelCase_      ,						UpperCAmelCase_				):
									assert type(UpperCAmelCase_				) == type(UpperCAmelCase_				)
									if isinstance(UpperCAmelCase_      ,						(list, tuple)				):
												consistent &= self._compare_arg_caches(UpperCAmelCase_      ,						UpperCAmelCase_				)
									elif isinstance(UpperCAmelCase_      ,						UpperCAmelCase_				):
												UpperCamelCase__					=       [v for _, v in sorted(aa.items()      ,						key=lambda snake_case				: x[0]				)]
												UpperCamelCase__					=       [v for _, v in sorted(aa.items()      ,						key=lambda snake_case				: x[0]				)]
												consistent &= self._compare_arg_caches(UpperCAmelCase_      ,						UpperCAmelCase_				)
									else:
												consistent &= aa == aa

						return consistent







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       True
						UpperCamelCase__					=       tree_map(lambda snake_case				: a.shape if isinstance(UpperCAmelCase_      ,						torch.Tensor				) else a      ,						UpperCAmelCase_      ,						UpperCAmelCase_				)
						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(UpperCAmelCase_				)
									UpperCamelCase__					=       self._compare_arg_caches(self.cached_arg_data      ,						UpperCAmelCase_				)
						else:
									# Otherwise, we can reuse the precomputed value
									UpperCamelCase__					=       False

						if not consistent:
									UpperCamelCase__					=       self._determine_favorable_chunk_size(
									    UpperCAmelCase_      ,						UpperCAmelCase_      ,						UpperCAmelCase_      ,						)
									UpperCamelCase__					=       arg_data

						assert self.cached_chunk_size is not None

						return self.cached_chunk_size






def        UpperCamelCase_(				_A   :str  )->		int:
			UpperCamelCase__					=       hex_num.strip()
			if not hex_num:
						raise ValueError("No value was passed to the function"  )

			UpperCamelCase__					=       hex_num[0] == "-"
			if is_negative:
						UpperCamelCase__					=       hex_num[1:]

			try:
						UpperCamelCase__					=       int(_A				,     16  )
			except ValueError:
						raise ValueError("Invalid value was passed to the function"  )

			UpperCamelCase__					=       ""
			while int_num > 0:
						UpperCamelCase__					=       str(int_num % 2  ) + bin_str
						int_num >>= 1

			return int(("-" + bin_str) if is_negative else bin_str  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()


