
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 							A_     (						__SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  Dict=0.999			,     __SCREAMING_SNAKE_CASE						:  int="cosine"			,     )				->       Any:





  """simple docstring"""

  if alpha_transform_type == "cosine":

    def alpha_bar_fn(__SCREAMING_SNAKE_CASE						:  Optional[Any] ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(__SCREAMING_SNAKE_CASE						:  List[Any] ):
      return math.exp(t * -12.0 )

  else:
    raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )

  __A		:     Tuple												=		[]
  for i in range(snake_case_ ):
    __A		:     str												=		i / num_diffusion_timesteps
    __A		:     List[Any]												=		(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(snake_case_ ) / alpha_bar_fn(snake_case_ )			,     snake_case_ ) )
  return torch.tensor(snake_case_			,     dtype=torch.floataa )







class  __A				( UpperCamelCase__    ,						UpperCamelCase__			):
 lowerCamelCase				       =[e.name for e in KarrasDiffusionSchedulers]
 lowerCamelCase				       =2
 @register_to_config
 def __init__(   self			:    str				,   lowerCamelCase			:    int = 10_00				,   lowerCamelCase			:    Optional[Any] = 0.00_085				,   lowerCamelCase			:    Dict = 0.012				,   lowerCamelCase			:    Dict = "linear"				,   lowerCamelCase			:    Any = None				,   lowerCamelCase			:    Union[str, Any] = "epsilon"				,   lowerCamelCase			:    Any = False				,   lowerCamelCase			:    List[Any] = False				,   lowerCamelCase			:    Union[str, Any] = 1.0				,   lowerCamelCase			:    str = "linspace"				,   lowerCamelCase			:    Any = 0				,   ):



   """simple docstring"""





   if trained_betas is not None:
     __A		:     Optional[int]												=		torch.tensor(_a				,   dtype=torch.floataa       )
   elif beta_schedule == "linear":
     __A		:     List[str]												=		torch.linspace(_a				,   _a				,   _a				,   dtype=torch.floataa       )
   elif beta_schedule == "scaled_linear":
     # this schedule is very specific to the latent diffusion model.
     __A		:     List[str]												=		(
         torch.linspace(beta_start**0.5				,   beta_end**0.5				,   _a				,   dtype=torch.floataa       ) ** 2
     )
   elif beta_schedule == "squaredcos_cap_v2":
     # Glide cosine schedule
     __A		:     int												=		betas_for_alpha_bar(_a				,   alpha_transform_type="""cosine"""       )
   elif beta_schedule == "exp":
     __A		:     List[str]												=		betas_for_alpha_bar(_a				,   alpha_transform_type="""exp"""       )
   else:
     raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"       )

   __A		:     Tuple												=		1.0 - self.betas
   __A		:     Dict												=		torch.cumprod(self.alphas				,   dim=0       )

   #  set all values
   self.set_timesteps(_a				,   _a				,   _a       )
   __A		:     Any												=		use_karras_sigmas
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    Any=None       ):



   """simple docstring"""





   if schedule_timesteps is None:
     __A		:     Optional[Any]												=		self.timesteps

   __A		:     str												=		(schedule_timesteps == timestep).nonzero()

   # The sigma index that is taken for the **very** first `step`
   # is always the second index (or the last index if there is only 1)
   # This way we can ensure we don't accidentally skip a sigma in
   # case we start in the middle of the denoising schedule (e.g. for image-to-image)
   if len(self._index_counter       ) == 0:
     __A		:     List[Any]												=		1 if len(_a       ) > 1 else 0
   else:
     __A		:     Any												=		timestep.cpu().item() if torch.is_tensor(_a       ) else timestep
     __A		:     Any												=		self._index_counter[timestep_int]

   return indices[pos].item()
 @property
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   if self.config.timestep_spacing in ["linspace", "trailing"]:
     return self.sigmas.max()

   return (self.sigmas.max() ** 2 + 1) ** 0.5
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    Tuple				,   ):



   """simple docstring"""





   __A		:     Dict												=		self.index_for_timestep(_a       )

   __A		:     Optional[int]												=		self.sigmas[step_index]
   __A		:     Any												=		sample / ((sigma**2 + 1) ** 0.5)
   return sample
 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    int = None				,   lowerCamelCase			:    Optional[Any] = None				,   ):



   """simple docstring"""





   __A		:     str												=		num_inference_steps

   __A		:     Optional[Any]												=		num_train_timesteps or self.config.num_train_timesteps

   # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
   if self.config.timestep_spacing == "linspace":
     __A		:     Dict												=		np.linspace(0				,   num_train_timesteps - 1				,   _a				,   dtype=_a       )[::-1].copy()
   elif self.config.timestep_spacing == "leading":
     __A		:     Any												=		num_train_timesteps // self.num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # casting to int to avoid issues when num_inference_step is power of 3
     __A		:     Any												=		(np.arange(0				,   _a       ) * step_ratio).round()[::-1].copy().astype(_a       )
     timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
     __A		:     Dict												=		num_train_timesteps / self.num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # casting to int to avoid issues when num_inference_step is power of 3
     __A		:     Tuple												=		(np.arange(_a				,   0				,   -step_ratio       )).round().copy().astype(_a       )
     timesteps -= 1
   else:
     raise ValueError(
         f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."       )

   __A		:     str												=		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
   __A		:     Optional[int]												=		np.log(_a       )
   __A		:     List[str]												=		np.interp(_a				,   np.arange(0				,   len(_a       )       )				,   _a       )

   if self.config.use_karras_sigmas:
     __A		:     str												=		self._convert_to_karras(in_sigmas=_a				,   num_inference_steps=self.num_inference_steps       )
     __A		:     str												=		np.array([self._sigma_to_t(_a				,   _a       ) for sigma in sigmas]       )

   __A		:     Union[str, Any]												=		np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
   __A		:     str												=		torch.from_numpy(_a       ).to(device=_a       )
   __A		:     int												=		torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2       ), sigmas[-1:]]       )

   __A		:     Dict												=		torch.from_numpy(_a       )
   __A		:     str												=		torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2       )]       )

   if str(_a       ).startswith("""mps"""       ):
     # mps does not support float64
     __A		:     Tuple												=		timesteps.to(_a				,   dtype=torch.floataa       )
   else:
     __A		:     Optional[int]												=		timesteps.to(device=_a       )

   # empty dt and derivative
   __A		:     Union[str, Any]												=		None
   __A		:     Optional[Any]												=		None

   # for exp beta schedules, such as the one for `pipeline_shap_e.py`
   # we need an index counter
   __A		:     Dict												=		defaultdict(_a       )
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    int				,   lowerCamelCase			:    int       ):



   """simple docstring"""





   __A		:     str												=		np.log(_a       )

   # get distribution
   __A		:     List[str]												=		log_sigma - log_sigmas[:, np.newaxis]

   # get sigmas range
   __A		:     int												=		np.cumsum((dists >= 0)				,   axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
   __A		:     Any												=		low_idx + 1

   __A		:     Dict												=		log_sigmas[low_idx]
   __A		:     List[str]												=		log_sigmas[high_idx]

   # interpolate sigmas
   __A		:     Tuple												=		(low - log_sigma) / (low - high)
   __A		:     Optional[int]												=		np.clip(_a				,   0				,   1       )

   # transform interpolation to time range
   __A		:     List[Any]												=		(1 - w) * low_idx + w * high_idx
   __A		:     str												=		t.reshape(sigma.shape       )
   return t
 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     float												=		in_sigmas[-1].item()
   __A		:     float												=		in_sigmas[0].item()

   __A		:     List[str]												=		7.0  # 7.0 is the value used in the paper
   __A		:     Optional[int]												=		np.linspace(0				,   1				,   _a       )
   __A		:     Optional[Any]												=		sigma_min ** (1 / rho)
   __A		:     Optional[Any]												=		sigma_max ** (1 / rho)
   __A		:     Union[str, Any]												=		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
   return sigmas
 @property
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   return self.dt is None
 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    Tuple				,   lowerCamelCase			:    Tuple				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Any = True				,   ):



   """simple docstring"""





   __A		:     str												=		self.index_for_timestep(_a       )

   # advance index counter by 1
   __A		:     Optional[int]												=		timestep.cpu().item() if torch.is_tensor(_a       ) else timestep
   self._index_counter[timestep_int] += 1

   if self.state_in_first_order:
     __A		:     Optional[Any]												=		self.sigmas[step_index]
     __A		:     Any												=		self.sigmas[step_index + 1]
   else:
     # 2nd order / Heun's method
     __A		:     Optional[int]												=		self.sigmas[step_index - 1]
     __A		:     List[str]												=		self.sigmas[step_index]

   # currently only gamma=0 is supported. This usually works best anyways.
   # We can support gamma in the future but then need to scale the timestep before
   # passing it to the model which requires a change in API
   __A		:     Union[str, Any]												=		0
   __A		:     Tuple												=		sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   if self.config.prediction_type == "epsilon":
     __A		:     List[str]												=		sigma_hat if self.state_in_first_order else sigma_next
     __A		:     Optional[Any]												=		sample - sigma_input * model_output
   elif self.config.prediction_type == "v_prediction":
     __A		:     Union[str, Any]												=		sigma_hat if self.state_in_first_order else sigma_next
     __A		:     Tuple												=		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
         sample / (sigma_input**2 + 1)
     )
   elif self.config.prediction_type == "sample":
     __A		:     Optional[Any]												=		model_output
   else:
     raise ValueError(
         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"       )

   if self.config.clip_sample:
     __A		:     Tuple												=		pred_original_sample.clamp(
         -self.config.clip_sample_range				,   self.config.clip_sample_range       )

   if self.state_in_first_order:
     # 2. Convert to an ODE derivative for 1st order
     __A		:     Any												=		(sample - pred_original_sample) / sigma_hat
     # 3. delta timestep
     __A		:     Tuple												=		sigma_next - sigma_hat

     # store for 2nd order step
     __A		:     List[Any]												=		derivative
     __A		:     Dict												=		dt
     __A		:     Dict												=		sample
   else:
     # 2. 2nd order / Heun's method
     __A		:     Dict												=		(sample - pred_original_sample) / sigma_next
     __A		:     Dict												=		(self.prev_derivative + derivative) / 2

     # 3. take prev timestep & sample
     __A		:     Any												=		self.dt
     __A		:     List[str]												=		self.sample

     # free dt and derivative
     # Note, this puts the scheduler in "first order mode"
     __A		:     List[str]												=		None
     __A		:     List[Any]												=		None
     __A		:     List[Any]												=		None

   __A		:     List[Any]												=		sample + derivative * dt

   if not return_dict:
     return (prev_sample,)

   return SchedulerOutput(prev_sample=_a       )
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[str]				,   ):



   """simple docstring"""





   __A		:     List[Any]												=		self.sigmas.to(device=original_samples.device				,   dtype=original_samples.dtype       )
   if original_samples.device.type == "mps" and torch.is_floating_point(_a       ):
     # mps does not support float64
     __A		:     List[Any]												=		self.timesteps.to(original_samples.device				,   dtype=torch.floataa       )
     __A		:     Tuple												=		timesteps.to(original_samples.device				,   dtype=torch.floataa       )
   else:
     __A		:     str												=		self.timesteps.to(original_samples.device       )
     __A		:     List[str]												=		timesteps.to(original_samples.device       )

   __A		:     Union[str, Any]												=		[self.index_for_timestep(_a				,   _a       ) for t in timesteps]

   __A		:     Union[str, Any]												=		sigmas[step_indices].flatten()
   while len(sigma.shape       ) < len(original_samples.shape       ):
     __A		:     Optional[int]												=		sigma.unsqueeze(-1       )

   __A		:     Dict												=		original_samples + noise * sigma
   return noisy_samples






 def __len__(   self			:    List[str]       ):



   """simple docstring"""





   return self.config.num_train_timesteps





'''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        A_     (						__SCREAMING_SNAKE_CASE						:  Union[dict, list, tuple, torch.Tensor] )				->       List[Tuple[int, ...]]:





  """simple docstring"""

  __A		:     Optional[Any]												=		[]
  if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    for v in tree.values():
      shapes.extend(_fetch_dims(__SCREAMING_SNAKE_CASE ) )
  elif isinstance(__SCREAMING_SNAKE_CASE			,     (list, tuple) ):
    for t in tree:
      shapes.extend(_fetch_dims(__SCREAMING_SNAKE_CASE ) )
  elif isinstance(__SCREAMING_SNAKE_CASE			,     torch.Tensor ):
    shapes.append(tree.shape )
  else:
    raise ValueError("""Not supported""" )

  return shapes







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





  """simple docstring"""

  __A		:     List[str]												=		[]
  for d in reversed(__SCREAMING_SNAKE_CASE ):
    idx.append(flat_idx % d )
    __A		:     Any												=		flat_idx // d

  return tuple(reversed(__SCREAMING_SNAKE_CASE ) )







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





  """simple docstring"""

  def reduce_edge_list(__SCREAMING_SNAKE_CASE						:  List[bool] ) -> None:
    __A		:     Dict												=		True
    for i in range(len(__SCREAMING_SNAKE_CASE ) ):
      __A		:     List[str]												=		-1 * (i + 1)
      l[reversed_idx] &= tally
      __A		:     Optional[Any]												=		l[reversed_idx]

  if start_edges is None:
    __A		:     Any												=		[s == 0 for s in start]
    reduce_edge_list(__SCREAMING_SNAKE_CASE )
  if end_edges is None:
    __A		:     List[str]												=		[e == (d - 1) for e, d in zip(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )]
    reduce_edge_list(__SCREAMING_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(__SCREAMING_SNAKE_CASE ) == 0:
    return [()]
  elif len(__SCREAMING_SNAKE_CASE ) == 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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    if s == e:
      path_list.append(slice(__SCREAMING_SNAKE_CASE			,     s + 1 ) )
    else:
      break

  __A		:     Tuple[slice, ...]												=		tuple(__SCREAMING_SNAKE_CASE )
  __A		:     Dict												=		len(__SCREAMING_SNAKE_CASE )

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

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

    __A		:     List[Any]												=		start[divergence_idx]
    return tuple(
        path + (slice(__SCREAMING_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

    __A		:     Optional[int]												=		end[divergence_idx]
    return tuple(
        path + (slice(__SCREAMING_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() )
    __A		:     List[Any]												=		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_     (						__SCREAMING_SNAKE_CASE						:  torch.Tensor			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int )				->       torch.Tensor:





  """simple docstring"""

  __A		:     List[Any]												=		t.shape[:no_batch_dims]
  __A		:     Dict												=		list(_flat_idx_to_idx(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ) )
  # _get_minimal_slice_set is inclusive
  __A		:     List[str]												=		list(_flat_idx_to_idx(flat_end - 1			,     __SCREAMING_SNAKE_CASE ) )

  # Get an ordered list of slices to perform
  __A		:     Optional[Any]												=		_get_minimal_slice_set(
      __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )

  __A		:     int												=		[t[s] for s in slices]

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  Callable			,     __SCREAMING_SNAKE_CASE						:  Dict[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  bool = False			,     __SCREAMING_SNAKE_CASE						:  Any = None			,     __SCREAMING_SNAKE_CASE						:  bool = False			,     )				->       Any:





  """simple docstring"""

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

  __A		:     str												=		[shape[:no_batch_dims] for shape in _fetch_dims(__SCREAMING_SNAKE_CASE )]
  __A		:     Any												=		tuple([max(__SCREAMING_SNAKE_CASE ) for s in zip(*__SCREAMING_SNAKE_CASE )] )

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

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

  __A		:     Any												=		1
  for d in orig_batch_dims:
    flat_batch_dim *= d

  __A		:     Union[str, Any]												=		flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

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

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

    __A		:     Dict[str, Any]												=		tensor_tree_map(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

    # Run the layer on the chunk
    __A		:     List[Any]												=		layer(**__SCREAMING_SNAKE_CASE )

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

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

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

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

    i += chunk_size

  __A		:     Optional[Any]												=		tensor_tree_map(lambda __SCREAMING_SNAKE_CASE : t.view(orig_batch_dims + t.shape[1:] )			,     __SCREAMING_SNAKE_CASE )

  return out







class  __A				:
 def __init__(   self			:    Any				,   lowerCamelCase			:    int = 5_12				,   ):



   """simple docstring"""





   __A		:     Optional[Any]												=		max_chunk_size
   __A		:     Optional[int]												=		None
   __A		:     Optional[tuple]												=		None
 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    Callable				,   lowerCamelCase			:    tuple				,   lowerCamelCase			:    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		:     List[str]												=		[min_chunk_size] + candidates
   candidates[-1] += 4

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

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

   return candidates[min_viable_chunk_size_index]
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    Iterable				,   lowerCamelCase			:    Iterable       ):



   """simple docstring"""





   __A		:     Optional[int]												=		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       ):
       __A		:     Dict												=		[v for _, v in sorted(aa.items()				,   key=lambda lowerCamelCase       : x[0]       )]
       __A		:     Any												=		[v for _, v in sorted(aa.items()				,   key=lambda lowerCamelCase       : x[0]       )]
       consistent &= self._compare_arg_caches(lowerCamelCase				,   lowerCamelCase       )
     else:
       consistent &= aa == aa

   return consistent






 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    Callable				,   lowerCamelCase			:    tuple				,   lowerCamelCase			:    int				,   ):



   """simple docstring"""





   __A		:     str												=		True
   __A		:     tuple												=		tree_map(lambda lowerCamelCase       : 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       )
     __A		:     Optional[Any]												=		self._compare_arg_caches(self.cached_arg_data				,   lowerCamelCase       )
   else:
     # Otherwise, we can reuse the precomputed value
     __A		:     List[str]												=		False

   if not consistent:
     __A		:     List[Any]												=		self._determine_favorable_chunk_size(
         lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase				,   )
     __A		:     Optional[int]												=		arg_data

   assert self.cached_chunk_size is not None

   return self.cached_chunk_size



