


'''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 _SCREAMING_SNAKE_CASE      (A       )       ->       List[Tuple[int, ...]]:






       """simple docstring"""







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

       return shapes

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






       """simple docstring"""







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

       return tuple(reversed(A       )       )

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






       """simple docstring"""







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

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

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

       lowercase__      			= []
       lowercase__      			= []

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

       lowercase__      			= tuple(A       )
       lowercase__      			= len(A       )

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

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

              lowercase__      			= start[divergence_idx]
              return tuple(
                  path + (slice(A  , 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

              lowercase__      			= end[divergence_idx]
              return tuple(
                  path + (slice(A  , 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()       )
              lowercase__      			= 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 _SCREAMING_SNAKE_CASE      (A  , A  , A  , A       )       ->       torch.Tensor:






       """simple docstring"""







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

       # Get an ordered list of slices to perform
       lowercase__      			= _get_minimal_slice_set(
           A  , A  , A  , )

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

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

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A  , A = False  , A = None  , A = False  , )       ->       Any:






       """simple docstring"""







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

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

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

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

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

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

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

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

              lowercase__      			= tensor_tree_map(A  , A       )

              # Run the layer on the chunk
              lowercase__      			= layer(**A       )

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

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

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

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

              i += chunk_size

       lowercase__      			= tensor_tree_map(lambda A       : t.view(orig_batch_dims + t.shape[1:]       )  , A       )

       return out

class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    Tuple   ,						UpperCamelCase    :    int = 512   ,						):

             '''simple docstring'''







             lowercase__      			= max_chunk_size
             lowercase__      			= None
             lowercase__      			= None



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    Callable   ,						UpperCamelCase    :    tuple   ,						UpperCamelCase    :    int ):

             '''simple docstring'''







             logging.info('''Tuning chunk size...''' )

             if min_chunk_size >= self.max_chunk_size:
                    return min_chunk_size

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

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

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

             return candidates[min_viable_chunk_size_index]



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    Iterable   ,						UpperCamelCase    :    Iterable ):

             '''simple docstring'''







             lowercase__      			= 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 ):
                           lowercase__      			= [v for _, v in sorted(aa.items()   ,						key=lambda UpperCamelCase : x[0] )]
                           lowercase__      			= [v for _, v in sorted(aa.items()   ,						key=lambda UpperCamelCase : x[0] )]
                           consistent &= self._compare_arg_caches(UpperCamelCase   ,						UpperCamelCase )
                    else:
                           consistent &= aa == aa

             return consistent






      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    Callable   ,						UpperCamelCase    :    tuple   ,						UpperCamelCase    :    int   ,						):

             '''simple docstring'''







             lowercase__      			= True
             lowercase__      			= tree_map(lambda UpperCamelCase : 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 )
                    lowercase__      			= self._compare_arg_caches(self.cached_arg_data   ,						UpperCamelCase )
             else:
                    # Otherwise, we can reuse the precomputed value
                    lowercase__      			= False

             if not consistent:
                    lowercase__      			= self._determine_favorable_chunk_size(
                        UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						)
                    lowercase__      			= arg_data

             assert self.cached_chunk_size is not None

             return self.cached_chunk_size








'''simple docstring'''

from __future__ import annotations

def _SCREAMING_SNAKE_CASE      (A       )       ->       bool:






       """simple docstring"""







       return len(set(A       )       ) == len(A       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




