"""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  ( __UpperCamelCase   :	Union[dict, list, tuple, torch.Tensor]     )							-> List[Tuple[int, ...]]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Tuple[int, ...]     )							-> Tuple[int, ...]:







  """simple docstring"""





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

  return tuple(reversed(__UpperCamelCase     )     )



@torch.jit.ignore
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Sequence[int]			,					__UpperCamelCase   :	Sequence[int]			,					__UpperCamelCase   :	Sequence[int]			,					__UpperCamelCase   :	Optional[Sequence[bool]] = None			,					__UpperCamelCase   :	Optional[Sequence[bool]] = None			,					)							-> List[Tuple[slice, ...]]:







  """simple docstring"""





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

  if start_edges is None:
    SCREAMING_SNAKE_CASE__      							=				[s == 0 for s in start]
    reduce_edge_list(__UpperCamelCase     )
  if end_edges is None:
    SCREAMING_SNAKE_CASE__      							=				[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     ),)]

  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[]

  # 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

  SCREAMING_SNAKE_CASE__      							=				tuple(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				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

    SCREAMING_SNAKE_CASE__      							=				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

    SCREAMING_SNAKE_CASE__      							=				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()     )
    SCREAMING_SNAKE_CASE__      							=				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  ( __UpperCamelCase   :	torch.Tensor			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int     )							-> torch.Tensor:







  """simple docstring"""





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

  # Get an ordered list of slices to perform
  SCREAMING_SNAKE_CASE__      							=				_get_minimal_slice_set(
      __UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					)

  SCREAMING_SNAKE_CASE__      							=				[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  ( __UpperCamelCase   :	Callable			,					__UpperCamelCase   :	Dict[str, Any]			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	bool = False			,					__UpperCamelCase   :	Any = None			,					__UpperCamelCase   :	bool = False			,					)							-> Any:







  """simple docstring"""





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

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

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

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

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

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

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

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

    SCREAMING_SNAKE_CASE__      							=				tensor_tree_map(__UpperCamelCase			,					__UpperCamelCase     )

    # Run the layer on the chunk
    SCREAMING_SNAKE_CASE__      							=				layer(**__UpperCamelCase     )

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

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

      def assign(__UpperCamelCase   :	dict			,					__UpperCamelCase   :	dict     ) -> 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:
              SCREAMING_SNAKE_CASE__      							=				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:
          SCREAMING_SNAKE_CASE__      							=				xa
    elif isinstance(__UpperCamelCase			,					torch.Tensor     ):
      if _add_into_out:
        out[i : i + chunk_size] += output_chunk
      else:
        SCREAMING_SNAKE_CASE__      							=				output_chunk
    else:
      raise ValueError("""Not supported"""     )

    i += chunk_size

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

  return out







class __snake_case    :




   def __init__(   self      :    List[str]	,     _lowercase      :    int = 5_12	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				max_chunk_size
     SCREAMING_SNAKE_CASE__      							=				None
     SCREAMING_SNAKE_CASE__      							=				None




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    Callable	,     _lowercase      :    tuple	,     _lowercase      :    int				):



     """simple docstring"""






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

     if min_chunk_size >= self.max_chunk_size:
       return min_chunk_size

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

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

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

     return candidates[min_viable_chunk_size_index]




   def      __a						(   self      :    List[Any]	,     _lowercase      :    Iterable	,     _lowercase      :    Iterable				):



     """simple docstring"""






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

     return consistent




   def      __a						(   self      :    Tuple	,     _lowercase      :    Callable	,     _lowercase      :    tuple	,     _lowercase      :    int	,     ):



     """simple docstring"""






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

     if not consistent:
       SCREAMING_SNAKE_CASE__      							=				self._determine_favorable_chunk_size(
           _lowercase	,     _lowercase	,     _lowercase	,     )
       SCREAMING_SNAKE_CASE__      							=				arg_data

     assert self.cached_chunk_size is not None

     return self.cached_chunk_size

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
   __lowerCamelCase      :			Optional[Any]							       =		{
       '''linear''': PIL.Image.Resampling.BILINEAR,
       '''bilinear''': PIL.Image.Resampling.BILINEAR,
       '''bicubic''': PIL.Image.Resampling.BICUBIC,
       '''lanczos''': PIL.Image.Resampling.LANCZOS,
       '''nearest''': PIL.Image.Resampling.NEAREST,
   }
else:
   __lowerCamelCase      :			int							       =		{
       '''linear''': PIL.Image.LINEAR,
       '''bilinear''': PIL.Image.BILINEAR,
       '''bicubic''': PIL.Image.BICUBIC,
       '''lanczos''': PIL.Image.LANCZOS,
       '''nearest''': PIL.Image.NEAREST,
   }



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				(images / 2 + 0.5).clamp(0			,					1     )
  SCREAMING_SNAKE_CASE__      							=				images.cpu().permute(0			,					2			,					3			,					1     ).float().numpy()
  SCREAMING_SNAKE_CASE__      							=				numpy_to_pil(__UpperCamelCase     )
  return images






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> int:







  """simple docstring"""





  if images.ndim == 3:
    SCREAMING_SNAKE_CASE__      							=				images[None, ...]
  SCREAMING_SNAKE_CASE__      							=				(images * 2_55).round().astype("""uint8"""     )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(image.squeeze()			,					mode="""L"""     ) for image in images]
  else:
    SCREAMING_SNAKE_CASE__      							=				[Image.fromarray(__UpperCamelCase     ) for image in images]

  return pil_images
