


from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class        a					:
     UpperCAmelCase__ :  torch.Tensor  # [batch_size x 3]
     UpperCAmelCase__ :  torch.Tensor  # [batch_size x 3]
     UpperCAmelCase__ :  torch.Tensor  # [batch_size x 3]
     UpperCAmelCase__ :  torch.Tensor  # [batch_size x 3]
     UpperCAmelCase__ :  int
     UpperCAmelCase__ :  int
     UpperCAmelCase__ :  float
     UpperCAmelCase__ :  float
     UpperCAmelCase__ :  Tuple[int]





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
        assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
        assert len(self.x.shape							) == len(self.y.shape							) == len(self.z.shape							) == len(self.origin.shape							) == 2





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        return torch.from_numpy(np.array([self.width, self.height]    ,	dtype=np.floataa							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return torch.from_numpy(np.array([self.x_fov, self.y_fov]    ,	dtype=np.floataa							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				str     =	torch.arange(self.height * self.width							)
        __lowerCamelCase:				int     =	torch.stack(
            [
                pixel_indices % self.width,
                torch.div(SCREAMING_SNAKE_CASE_    ,	self.width    ,	rounding_mode="""trunc"""							),
            ]    ,	axis=1    ,	)
        return coords





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase   ,     *__lowerCamelCase:				Tuple     =	self.shape
        __lowerCamelCase:				Any     =	int(np.prod(SCREAMING_SNAKE_CASE_							)							)

        __lowerCamelCase:				str     =	self.get_image_coords()
        __lowerCamelCase:				int     =	torch.broadcast_to(coords.unsqueeze(0							)    ,	[batch_size * inner_batch_size, *coords.shape]							)
        __lowerCamelCase:				Optional[int]     =	self.get_camera_rays(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	rays.view(SCREAMING_SNAKE_CASE_    ,	inner_batch_size * self.height * self.width    ,	2    ,	3							)

        return rays





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				torch.Tensor							):
        __lowerCamelCase   ,     *__lowerCamelCase   ,     __lowerCamelCase:				Any     =	coords.shape
        assert n_coords == 2
        assert batch_size == self.origin.shape[0]

        __lowerCamelCase:				int     =	coords.view(SCREAMING_SNAKE_CASE_    ,	-1    ,	2							)

        __lowerCamelCase:				Optional[Any]     =	self.resolution()
        __lowerCamelCase:				List[Any]     =	self.fov()

        __lowerCamelCase:				Union[str, Any]     =	(flat.float() / (res - 1)) * 2 - 1
        __lowerCamelCase:				str     =	fracs * torch.tan(fov / 2							)

        __lowerCamelCase:				str     =	fracs.view(SCREAMING_SNAKE_CASE_    ,	-1    ,	2							)
        __lowerCamelCase:				Tuple     =	(
            self.z.view(SCREAMING_SNAKE_CASE_    ,	1    ,	3							)
            + self.x.view(SCREAMING_SNAKE_CASE_    ,	1    ,	3							) * fracs[:, :, :1]
            + self.y.view(SCREAMING_SNAKE_CASE_    ,	1    ,	3							) * fracs[:, :, 1:]
        )
        __lowerCamelCase:				str     =	directions / directions.norm(dim=-1    ,	keepdim=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	torch.stack(
            [
                torch.broadcast_to(self.origin.view(SCREAMING_SNAKE_CASE_    ,	1    ,	3							)    ,	[batch_size, directions.shape[1], 3]							),
                directions,
            ]    ,	dim=2    ,	)
        return rays.view(SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_    ,	2    ,	3							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int							):
        assert width * self.height == height * self.width, "The aspect ratio should not change."
        return DifferentiableProjectiveCamera(
            origin=self.origin    ,	x=self.x    ,	y=self.y    ,	z=self.z    ,	width=SCREAMING_SNAKE_CASE_    ,	height=SCREAMING_SNAKE_CASE_    ,	x_fov=self.x_fov    ,	y_fov=self.y_fov    ,	)



def     __lowerCAmelCase						(							snake_case	:    int )       ->							DifferentiableProjectiveCamera:
   __lowerCamelCase:				Any     =	[]
   __lowerCamelCase:				Union[str, Any]     =	[]
   __lowerCamelCase:				List[str]     =	[]
   __lowerCamelCase:				int     =	[]
   for theta in np.linspace(0						,      2 * np.pi						,      num=20 ):
      __lowerCamelCase:				Optional[Any]     =	np.array([np.sin(snake_case ), np.cos(snake_case ), -0.5] )
      z /= np.sqrt(np.sum(z**2 ) )
      __lowerCamelCase:				List[str]     =	-z * 4
      __lowerCamelCase:				Optional[Any]     =	np.array([np.cos(snake_case ), -np.sin(snake_case ), 0.0] )
      __lowerCamelCase:				Dict     =	np.cross(snake_case						,      snake_case )
      origins.append(snake_case )
      xs.append(snake_case )
      ys.append(snake_case )
      zs.append(snake_case )
   return DifferentiableProjectiveCamera(
       origin=torch.from_numpy(np.stack(snake_case						,      axis=0 ) ).float()						,      x=torch.from_numpy(np.stack(snake_case						,      axis=0 ) ).float()						,      y=torch.from_numpy(np.stack(snake_case						,      axis=0 ) ).float()						,      z=torch.from_numpy(np.stack(snake_case						,      axis=0 ) ).float()						,      width=snake_case						,      height=snake_case						,      x_fov=0.7						,      y_fov=0.7						,      shape=(1, len(snake_case ))						,      )






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

   return shapes



@torch.jit.ignore
def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    Tuple[int, ...] )       ->							Tuple[int, ...]:
   __lowerCamelCase:				Union[str, Any]     =	[]
   for d in reversed(snake_case ):
      idx.append(flat_idx % d )
      __lowerCamelCase:				Optional[int]     =	flat_idx // d

   return tuple(reversed(snake_case ) )



@torch.jit.ignore
def     __lowerCAmelCase						(							snake_case	:    Sequence[int]						,      snake_case	:    Sequence[int]						,      snake_case	:    Sequence[int]						,      snake_case	:    Optional[Sequence[bool]] = None						,      snake_case	:    Optional[Sequence[bool]] = 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(snake_case	:    List[bool] ) -> None:
      __lowerCamelCase:				List[Any]     =	True
      for i in range(len(snake_case ) ):
         __lowerCamelCase:				str     =	-1 * (i + 1)
         l[reversed_idx] &= tally
         __lowerCamelCase:				List[Any]     =	l[reversed_idx]

   if start_edges is None:
      __lowerCamelCase:				str     =	[s == 0 for s in start]
      reduce_edge_list(snake_case )
   if end_edges is None:
      __lowerCamelCase:				Union[str, Any]     =	[e == (d - 1) for e, d in zip(snake_case						,      snake_case )]
      reduce_edge_list(snake_case )

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

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

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

   __lowerCamelCase:				Tuple[slice, ...]     =	tuple(snake_case )
   __lowerCamelCase:				Dict     =	len(snake_case )

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

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

      __lowerCamelCase:				Dict     =	start[divergence_idx]
      return tuple(
          path + (slice(snake_case						,      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

      __lowerCamelCase:				List[Any]     =	end[divergence_idx]
      return tuple(
          path + (slice(snake_case						,      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() )
      __lowerCamelCase:				List[str]     =	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     __lowerCAmelCase						(							snake_case	:    torch.Tensor						,      snake_case	:    int						,      snake_case	:    int						,      snake_case	:    int )       ->							torch.Tensor:
   __lowerCamelCase:				Any     =	t.shape[:no_batch_dims]
   __lowerCamelCase:				Dict     =	list(_flat_idx_to_idx(snake_case						,      snake_case ) )
   # _get_minimal_slice_set is inclusive
   __lowerCamelCase:				Optional[int]     =	list(_flat_idx_to_idx(flat_end - 1						,      snake_case ) )

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

   __lowerCamelCase:				Tuple     =	[t[s] for s in slices]

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



def     __lowerCAmelCase						(							snake_case	:    Callable						,      snake_case	:    Dict[str, Any]						,      snake_case	:    int						,      snake_case	:    int						,      snake_case	:    bool = False						,      snake_case	:    Any = None						,      snake_case	:    bool = False						,      )       ->							Any:
   if not (len(snake_case ) > 0):
      raise ValueError("""Must provide at least one input""" )

   __lowerCamelCase:				Optional[int]     =	[shape[:no_batch_dims] for shape in _fetch_dims(snake_case )]
   __lowerCamelCase:				int     =	tuple([max(snake_case ) for s in zip(*snake_case )] )

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

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

   __lowerCamelCase:				List[Any]     =	1
   for d in orig_batch_dims:
      flat_batch_dim *= d

   __lowerCamelCase:				int     =	flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

   __lowerCamelCase:				List[Any]     =	0
   __lowerCamelCase:				Optional[Any]     =	prepped_outputs
   for _ in range(snake_case ):
      # Chunk the input
      if not low_mem:
         __lowerCamelCase:				int     =	_select_chunk
      else:
         __lowerCamelCase:				Optional[int]     =	partial(
             _chunk_slice						,      flat_start=snake_case						,      flat_end=min(snake_case						,      i + chunk_size )						,      no_batch_dims=len(snake_case )						,      )

      __lowerCamelCase:				Dict[str, Any]     =	tensor_tree_map(snake_case						,      snake_case )

      # Run the layer on the chunk
      __lowerCamelCase:				Union[str, Any]     =	layer(**snake_case )

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

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

         def assign(snake_case	:    dict						,      snake_case	:    dict ) -> None:
            for k, v in da.items():
               if isinstance(snake_case						,      snake_case ):
                  assign(snake_case						,      da[k] )
               else:
                  if _add_into_out:
                     v[i : i + chunk_size] += da[k]
                  else:
                     __lowerCamelCase:				List[str]     =	da[k]

         assign(snake_case						,      snake_case )
      elif isinstance(snake_case						,      snake_case ):
         for xa, xa in zip(snake_case						,      snake_case ):
            if _add_into_out:
               xa[i : i + chunk_size] += xa
            else:
               __lowerCamelCase:				Optional[int]     =	xa
      elif isinstance(snake_case						,      torch.Tensor ):
         if _add_into_out:
            out[i : i + chunk_size] += output_chunk
         else:
            __lowerCamelCase:				Optional[Any]     =	output_chunk
      else:
         raise ValueError("""Not supported""" )

      i += chunk_size

   __lowerCamelCase:				Union[str, Any]     =	tensor_tree_map(lambda snake_case : t.view(orig_batch_dims + t.shape[1:] )						,      snake_case )

   return out







class        a					:





     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int = 512    ,	):
        __lowerCamelCase:				Any     =	max_chunk_size
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Optional[tuple]     =	None





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Callable    ,	SCREAMING_SNAKE_CASE_					:				tuple    ,	SCREAMING_SNAKE_CASE_					:				int							):
        logging.info("""Tuning chunk size..."""							)

        if min_chunk_size >= self.max_chunk_size:
           return min_chunk_size

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

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

        __lowerCamelCase:				List[str]     =	0
        __lowerCamelCase:				Any     =	len(SCREAMING_SNAKE_CASE_							) - 1
        while i > min_viable_chunk_size_index:
           __lowerCamelCase:				List[str]     =	test_chunk_size(candidates[i]							)
           if not viable:
              __lowerCamelCase:				Optional[int]     =	(min_viable_chunk_size_index + i) // 2
           else:
              __lowerCamelCase:				Optional[Any]     =	i
              __lowerCamelCase:				List[str]     =	(i + len(SCREAMING_SNAKE_CASE_							) - 1) // 2

        return candidates[min_viable_chunk_size_index]





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

        return consistent







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Callable    ,	SCREAMING_SNAKE_CASE_					:				tuple    ,	SCREAMING_SNAKE_CASE_					:				int    ,	):
        __lowerCamelCase:				int     =	True
        __lowerCamelCase:				tuple     =	tree_map(lambda SCREAMING_SNAKE_CASE_							: a.shape if isinstance(SCREAMING_SNAKE_CASE_    ,	torch.Tensor							) else a    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_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(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				List[str]     =	self._compare_arg_caches(self.cached_arg_data    ,	SCREAMING_SNAKE_CASE_							)
        else:
           # Otherwise, we can reuse the precomputed value
           __lowerCamelCase:				Optional[int]     =	False

        if not consistent:
           __lowerCamelCase:				str     =	self._determine_favorable_chunk_size(
               SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	)
           __lowerCamelCase:				Optional[int]     =	arg_data

        assert self.cached_chunk_size is not None

        return self.cached_chunk_size


