



'''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   __lowerCAmelCase					(							lowerCamelCase   :						Union[dict, list, tuple, torch.Tensor]					):






	'''simple docstring'''




	__lowerCAmelCase												=  []
	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   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						Tuple[int, ...]					):






	'''simple docstring'''




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

	return tuple(reversed(lowerCamelCase					)					)



@torch.jit.ignore
def   __lowerCAmelCase					(							lowerCamelCase   :						Sequence[int]				,		lowerCamelCase   :						Sequence[int]				,		lowerCamelCase   :						Sequence[int]				,		lowerCamelCase   :						Optional[Sequence[bool]] = None				,		lowerCamelCase   :						Optional[Sequence[bool]] = None				,		):






	'''simple docstring'''




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

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

	__lowerCAmelCase												=  []
	__lowerCAmelCase												=  []

	# 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

	__lowerCAmelCase												=  tuple(lowerCamelCase					)
	__lowerCAmelCase												=  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

		__lowerCAmelCase												=  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

		__lowerCAmelCase												=  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()					)
		__lowerCAmelCase												=  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					(							lowerCamelCase   :						torch.Tensor				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int					):






	'''simple docstring'''




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

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

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

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



def   __lowerCAmelCase					(							lowerCamelCase   :						Callable				,		lowerCamelCase   :						Dict[str, Any]				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int				,		lowerCamelCase   :						bool = False				,		lowerCamelCase   :						Any = None				,		lowerCamelCase   :						bool = False				,		):






	'''simple docstring'''




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

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

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

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

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

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

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

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

		__lowerCAmelCase												=  tensor_tree_map(lowerCamelCase				,		lowerCamelCase					)

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

		# Allocate space for the output
		if out is None:
			__lowerCAmelCase												=  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   :						dict				,		lowerCamelCase   :						dict					) -> 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:
							__lowerCAmelCase												=  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:
					__lowerCAmelCase												=  xa
		elif isinstance(lowerCamelCase				,		torch.Tensor					):
			if _add_into_out:
				out[i : i + chunk_size] += output_chunk
			else:
				__lowerCAmelCase												=  output_chunk
		else:
			raise ValueError("Not supported"					)

		i += chunk_size

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

	return out






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase = 512			,				)     ->			Any:
						__lowerCAmelCase												=  max_chunk_size
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  None



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			int:
						logging.info("Tuning chunk size..."     )

						if min_chunk_size >= self.max_chunk_size:
							return min_chunk_size

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

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

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

						return candidates[min_viable_chunk_size_index]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			bool:
						__lowerCAmelCase												=  True
						for aa, aa in zip(UpperCamelCase			,				UpperCamelCase     ):
							assert type(UpperCamelCase     ) == type(UpperCamelCase     )
							if isinstance(UpperCamelCase			,				(list, tuple)     ):
								consistent &= self._compare_arg_caches(UpperCamelCase			,				UpperCamelCase     )
							elif isinstance(UpperCamelCase			,				UpperCamelCase     ):
								__lowerCAmelCase												=  [v for _, v in sorted(aa.items()			,				key=lambda UpperCamelCase     : x[0]     )]
								__lowerCAmelCase												=  [v for _, v in sorted(aa.items()			,				key=lambda UpperCamelCase     : x[0]     )]
								consistent &= self._compare_arg_caches(UpperCamelCase			,				UpperCamelCase     )
							else:
								consistent &= aa == aa

						return consistent



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				)     ->			int:
						__lowerCAmelCase												=  True
						__lowerCAmelCase												=  tree_map(lambda UpperCamelCase     : a.shape if isinstance(UpperCamelCase			,				torch.Tensor     ) else a			,				UpperCamelCase			,				UpperCamelCase     )
						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(UpperCamelCase     )
							__lowerCAmelCase												=  self._compare_arg_caches(self.cached_arg_data			,				UpperCamelCase     )
						else:
							# Otherwise, we can reuse the precomputed value
							__lowerCAmelCase												=  False

						if not consistent:
							__lowerCAmelCase												=  self._determine_favorable_chunk_size(
							    UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				)
							__lowerCAmelCase												=  arg_data

						assert self.cached_chunk_size is not None

						return self.cached_chunk_size




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[str]        =							(CMStochasticIterativeScheduler,)
					a      :     str        =							1_0



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 201,
						    "sigma_min": 0.0_02,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  10

						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  self.scheduler_classes[0](**UpperCamelCase     )

						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  scheduler.timesteps[0]
						__lowerCAmelCase												=  scheduler.timesteps[1]

						__lowerCAmelCase												=  self.dummy_sample
						__lowerCAmelCase												=  0.1 * sample

						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample
						__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

						self.assertEqual(output_a.shape			,				sample.shape     )
						self.assertEqual(output_a.shape			,				output_a.shape     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  1
						scheduler.set_timesteps(UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for i, t in enumerate(UpperCamelCase     ):
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 1_92.76_14     ) < 1E-2
						assert abs(result_mean.item() - 0.25_10     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [106, 0]
						scheduler.set_timesteps(timesteps=UpperCamelCase     )
						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  torch.manual_seed(0     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter * scheduler.init_noise_sigma

						for t in timesteps:
							# 1. scale model input
							__lowerCAmelCase												=  scheduler.scale_model_input(UpperCamelCase			,				UpperCamelCase     )

							# 2. predict noise residual
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )

							# 3. predict previous sample x_t-1
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				generator=UpperCamelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 3_47.63_57     ) < 1E-2
						assert abs(result_mean.item() - 0.45_27     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 15, 0]

						with self.assertRaises(UpperCamelCase			,				msg="`timesteps` must be in descending order."     ):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [39, 30, 12, 1, 0]
						__lowerCAmelCase												=  len(UpperCamelCase     )

						with self.assertRaises(UpperCamelCase			,				msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
							scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,				timesteps=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase												=  [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    UpperCamelCase			,				msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,				):
							scheduler.set_timesteps(timesteps=UpperCamelCase     )