





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						(_SCREAMING_SNAKE_CASE	)       ->     List[Tuple[int, ...]]:
  lowercase__			    =						[]
  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						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple[int, ...]:
  lowercase__			    =						[]
  for d in reversed(__lowerCAmelCase	):
    idx.append(flat_idx % d	)
    lowercase__			    =						flat_idx // d

  return tuple(reversed(__lowerCAmelCase	)	)




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


  def reduce_edge_list(_SCREAMING_SNAKE_CASE	) -> None:
    lowercase__			    =						True
    for i in range(len(__lowerCAmelCase	)	):
      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(__lowerCAmelCase	)
  if end_edges is None:
    lowercase__			    =						[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	),)]

  lowercase__			    =						[]
  lowercase__			    =						[]

  # 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

  lowercase__			    =						tuple(__lowerCAmelCase	)
  lowercase__			    =						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

    lowercase__			    =						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

    lowercase__			    =						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()	)
    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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     torch.Tensor:
  lowercase__			    =						t.shape[:no_batch_dims]
  lowercase__			    =						list(_flat_idx_to_idx(__lowerCAmelCase	,		__lowerCAmelCase	)	)
  # _get_minimal_slice_set is inclusive
  lowercase__			    =						list(_flat_idx_to_idx(flat_end - 1	,		__lowerCAmelCase	)	)

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

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

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




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


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

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

  def _prep_inputs(_SCREAMING_SNAKE_CASE	) -> 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	,		__lowerCAmelCase	)
  lowercase__			    =						None
  if _out is not None:
    lowercase__			    =						tensor_tree_map(lambda _SCREAMING_SNAKE_CASE	: 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(_SCREAMING_SNAKE_CASE	) -> torch.Tensor:
    return t[i : i + chunk_size] if t.shape[0] != 1 else t

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

    lowercase__			    =						tensor_tree_map(__lowerCAmelCase	,		__lowerCAmelCase	)

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

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

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

      def assign(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	) -> 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:
              lowercase__			    =						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:
          lowercase__			    =						xa
    elif isinstance(__lowerCAmelCase	,		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 _SCREAMING_SNAKE_CASE	: t.view(orig_batch_dims + t.shape[1:]	)	,		__lowerCAmelCase	)

  return out


class        SCREAMING_SNAKE_CASE     :


   def __init__(					self    :       Tuple    ,  a    :       int = 512    ,  )->     Any:





     """simple docstring"""







     lowercase__			    =						max_chunk_size
     lowercase__			    =						None
     lowercase__			    =						None


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Callable    ,  a    :       tuple    ,  a    :       int )->     str:





     """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(a    :       int ) -> bool:
       try:
         with torch.no_grad():
           fn(*lowerCAmelCase_    ,  chunk_size=lowerCAmelCase_ )
         return True
       except RuntimeError:
         return False

     lowercase__			    =						0
     lowercase__			    =						len(lowerCAmelCase_ ) - 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(lowerCAmelCase_ ) - 1) // 2

     return candidates[min_viable_chunk_size_index]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       Iterable    ,  a    :       Iterable )->     str:





     """simple docstring"""







     lowercase__			    =						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_ ):
         lowercase__			    =						[v for _, v in sorted(aa.items()    ,  key=lambda a : x[0] )]
         lowercase__			    =						[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  SCREAMING_SNAKE_CASE_							(					self    :       int    ,  a    :       Callable    ,  a    :       tuple    ,  a    :       int    ,  )->     Union[str, Any]:





     """simple docstring"""







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

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

     assert self.cached_chunk_size is not None

     return self.cached_chunk_size









from PIL import Image




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Image:

  def brightness(_SCREAMING_SNAKE_CASE	) -> float:
    return 128 + level + (c - 128)

  if not -2_5_5.0 <= level <= 2_5_5.0:
    raise ValueError('level must be between -255.0 (black) and 255.0 (white)'	)
  return img.point(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            lowercase_					     =	change_brightness(img, 100)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


