





import random







class   __lowerCAmelCase			:





   @staticmethod
   def 	_snake_case ( snake_case      )				->  tuple[list[int], list[int]]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  [ord(snake_case      ) for i in text]
      a__	:						Optional[Any]      		=  []
      a__	:						Tuple      		=  []
      for i in plain:
         a__	:						str      		=  random.randint(1					,			300      )
         a__	:						List[Any]      		=  (i + k) * k
         cipher.append(snake_case      )
         key.append(snake_case      )
      return cipher, key





   @staticmethod
   def 	_snake_case ( snake_case					,			snake_case      )				->  str:
      """simple docstring"""





      a__	:						Tuple      		=  []
      for i in range(len(snake_case      )      ):
         a__	:						str      		=  int((cipher[i] - (key[i]) ** 2) / key[i]      )
         plain.append(chr(snake_case      )      )
      return "".join(snake_case      )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__      ,     SCREAMING_SNAKE_CASE__       :			List[str]			      =						Onepad().encrypt("""Hello""")
  print(c, k)
  print(Onepad().decrypt(c, k))








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       _A  (      lowerCamelCase   ):
   a__	:						List[str]      		=  []
   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       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						List[str]      		=  []
   for d in reversed(lowerCamelCase   ):
      idx.append(flat_idx % d   )
      a__	:						Union[str, Any]      		=  flat_idx // d

   return tuple(reversed(lowerCamelCase   )   )






@torch.jit.ignore
def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase = None							, lowerCamelCase = None							, ):

   # 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(lowerCamelCase   ) -> None:
      a__	:						int      		=  True
      for i in range(len(lowerCamelCase   )   ):
         a__	:						Optional[Any]      		=  -1 * (i + 1)
         l[reversed_idx] &= tally
         a__	:						Tuple      		=  l[reversed_idx]

   if start_edges is None:
      a__	:						Optional[int]      		=  [s == 0 for s in start]
      reduce_edge_list(lowerCamelCase   )
   if end_edges is None:
      a__	:						Union[str, Any]      		=  [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   ),)]

   a__	:						List[Tuple[slice, ...]]      		=  []
   a__	:						List[slice]      		=  []

   # 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

   a__	:						Tuple[slice, ...]      		=  tuple(lowerCamelCase   )
   a__	:						Optional[Any]      		=  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

      a__	:						Optional[Any]      		=  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

      a__	:						List[str]      		=  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()   )
      a__	:						Optional[int]      		=  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       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						Optional[int]      		=  t.shape[:no_batch_dims]
   a__	:						List[str]      		=  list(_flat_idx_to_idx(lowerCamelCase							, lowerCamelCase   )   )
   # _get_minimal_slice_set is inclusive
   a__	:						Dict      		=  list(_flat_idx_to_idx(flat_end - 1							, lowerCamelCase   )   )

   # Get an ordered list of slices to perform
   a__	:						str      		=  _get_minimal_slice_set(
       lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, )

   a__	:						Any      		=  [t[s] for s in slices]

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






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase = False							, lowerCamelCase = None							, lowerCamelCase = False							, ):
   if not (len(lowerCamelCase   ) > 0):
      raise ValueError("Must provide at least one input"   )

   a__	:						str      		=  [shape[:no_batch_dims] for shape in _fetch_dims(lowerCamelCase   )]
   a__	:						Dict      		=  tuple([max(lowerCamelCase   ) for s in zip(*lowerCamelCase   )]   )

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

   a__	:						Dict[str, Any]      		=  tensor_tree_map(_prep_inputs							, lowerCamelCase   )
   a__	:						str      		=  None
   if _out is not None:
      a__	:						Optional[int]      		=  tensor_tree_map(lambda lowerCamelCase   : t.view([-1] + list(t.shape[no_batch_dims:]   )   )							, _out   )

   a__	:						Optional[Any]      		=  1
   for d in orig_batch_dims:
      flat_batch_dim *= d

   a__	:						Tuple      		=  flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

      a__	:						Dict[str, Any]      		=  tensor_tree_map(lowerCamelCase							, lowerCamelCase   )

      # Run the layer on the chunk
      a__	:						Any      		=  layer(**lowerCamelCase   )

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

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

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

      i += chunk_size

   a__	:						Any      		=  tensor_tree_map(lambda lowerCamelCase   : t.view(orig_batch_dims + t.shape[1:]   )							, lowerCamelCase   )

   return out







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = 512					,			)				->  List[str]:
      """simple docstring"""





      a__	:						int      		=  max_chunk_size
      a__	:						Optional[int]      		=  None
      a__	:						Optional[tuple]      		=  None





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  int:
      """simple docstring"""





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

      if min_chunk_size >= self.max_chunk_size:
         return min_chunk_size

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

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

      a__	:						Union[str, Any]      		=  0
      a__	:						Dict      		=  len(snake_case      ) - 1
      while i > min_viable_chunk_size_index:
         a__	:						Any      		=  test_chunk_size(candidates[i]      )
         if not viable:
            a__	:						List[Any]      		=  (min_viable_chunk_size_index + i) // 2
         else:
            a__	:						Tuple      		=  i
            a__	:						Any      		=  (i + len(snake_case      ) - 1) // 2

      return candidates[min_viable_chunk_size_index]





   def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  bool:
      """simple docstring"""





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

      return consistent





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			)				->  int:
      """simple docstring"""





      a__	:						List[Any]      		=  True
      a__	:						tuple      		=  tree_map(lambda snake_case      : a.shape if isinstance(snake_case					,			torch.Tensor      ) else a					,			snake_case					,			snake_case      )
      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(snake_case      )
         a__	:						Union[str, Any]      		=  self._compare_arg_caches(self.cached_arg_data					,			snake_case      )
      else:
         # Otherwise, we can reuse the precomputed value
         a__	:						Optional[int]      		=  False

      if not consistent:
         a__	:						List[str]      		=  self._determine_favorable_chunk_size(
             snake_case					,			snake_case					,			snake_case					,			)
         a__	:						List[str]      		=  arg_data

      assert self.cached_chunk_size is not None

      return self.cached_chunk_size

