
'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[Tuple[int, ...]]:


		'''simple docstring'''


		snake_case  :       int     			=						[]
		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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Tuple[int, ...]:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						[]
		for d in reversed(SCREAMING_SNAKE_CASE__			):
				idx.append(flat_idx % d			)
				snake_case  :       Optional[Any]     			=						flat_idx // d

		return tuple(reversed(SCREAMING_SNAKE_CASE__			)			)




@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, ...]]:


		'''simple docstring'''


		def reduce_edge_list(SCREAMING_SNAKE_CASE__			) -> None:
				snake_case  :       str     			=						True
				for i in range(len(SCREAMING_SNAKE_CASE__			)			):
						snake_case  :       Union[str, Any]     			=						-1 * (i + 1)
						l[reversed_idx] &= tally
						snake_case  :       str     			=						l[reversed_idx]

		if start_edges is None:
				snake_case  :       int     			=						[s == 0 for s in start]
				reduce_edge_list(SCREAMING_SNAKE_CASE__			)
		if end_edges is None:
				snake_case  :       List[Any]     			=						[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			),)]

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

		snake_case  :       Tuple[slice, ...]     			=						tuple(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Tuple     			=						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

				snake_case  :       Tuple     			=						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

				snake_case  :       Tuple     			=						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()			)
				snake_case  :       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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							torch.Tensor:


		'''simple docstring'''


		snake_case  :       int     			=						t.shape[:no_batch_dims]
		snake_case  :       Union[str, Any]     			=						list(_flat_idx_to_idx(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)			)
		# _get_minimal_slice_set is inclusive
		snake_case  :       Optional[Any]     			=						list(_flat_idx_to_idx(flat_end - 1 ,       SCREAMING_SNAKE_CASE__			)			)

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

		snake_case  :       Any     			=						[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:


		'''simple docstring'''


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

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

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

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

		snake_case  :       str     			=						1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		snake_case  :       List[str]     			=						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

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

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

				# Run the layer on the chunk
				snake_case  :       Any     			=						layer(**SCREAMING_SNAKE_CASE__			)

				# Allocate space for the output
				if out is None:
						snake_case  :       Any     			=						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__ ,       SCREAMING_SNAKE_CASE__			) -> 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:
														snake_case  :       Tuple     			=						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:
										snake_case  :       Dict     			=						xa
				elif isinstance(SCREAMING_SNAKE_CASE__ ,       torch.Tensor			):
						if _add_into_out:
								out[i : i + chunk_size] += output_chunk
						else:
								snake_case  :       Optional[int]     			=						output_chunk
				else:
						raise ValueError('''Not supported'''			)

				i += chunk_size

		snake_case  :       List[str]     			=						tensor_tree_map(lambda SCREAMING_SNAKE_CASE__			: t.view(orig_batch_dims + t.shape[1:]			) ,       SCREAMING_SNAKE_CASE__			)

		return out






class 	snake_case__       :







		"""simple docstring"""

		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int = 512  ,	)						->			List[str]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						max_chunk_size
				snake_case  :       Optional[int]     			=						None
				snake_case  :       Optional[tuple]     			=						None



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Callable  ,	UpperCamelCase__				:			tuple  ,	UpperCamelCase__				:			int      )						->			int:






				"""simple docstring"""







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

				if min_chunk_size >= self.max_chunk_size:
						return min_chunk_size

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

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

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

				return candidates[min_viable_chunk_size_index]



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Iterable  ,	UpperCamelCase__				:			Iterable      )						->			bool:






				"""simple docstring"""







				snake_case  :       Any     			=						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__      ):
								snake_case  :       Dict     			=						[v for _, v in sorted(aa.items()  ,	key=lambda UpperCamelCase__      : x[0]      )]
								snake_case  :       Any     			=						[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       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Callable  ,	UpperCamelCase__				:			tuple  ,	UpperCamelCase__				:			int  ,	)						->			int:






				"""simple docstring"""







				snake_case  :       int     			=						True
				snake_case  :       tuple     			=						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__      )
						snake_case  :       Tuple     			=						self._compare_arg_caches(self.cached_arg_data  ,	UpperCamelCase__      )
				else:
						# Otherwise, we can reuse the precomputed value
						snake_case  :       List[Any]     			=						False

				if not consistent:
						snake_case  :       Optional[Any]     			=						self._determine_favorable_chunk_size(
						    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	)
						snake_case  :       Optional[int]     			=						arg_data

				assert self.cached_chunk_size is not None

				return self.cached_chunk_size




'''simple docstring'''
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowercase__						       =      logging.get_logger(__name__)

enable_full_determinism()






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Any     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       str     			=						(32, 32)

				snake_case  :       Optional[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Tuple     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			str      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       Any     			=						{
				    '''block_out_channels''': (32, 64),
				    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
				    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
				    '''attention_head_dim''': 3,
				    '''out_channels''': 3,
				    '''in_channels''': 3,
				    '''layers_per_block''': 2,
				    '''sample_size''': 32,
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       str     			=						4
				snake_case  :       Tuple     			=						4
				snake_case  :       str     			=						(32, 32)

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				return (4, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			int:






				"""simple docstring"""







				return (4, 32, 32)



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						{
				    '''sample_size''': 32,
				    '''in_channels''': 4,
				    '''out_channels''': 4,
				    '''layers_per_block''': 2,
				    '''block_out_channels''': (32, 64),
				    '''attention_head_dim''': 32,
				    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
				    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
				}
				snake_case  :       str     			=						self.dummy_input
				return init_dict, inputs_dict



		def       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )

				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						model(**self.dummy_input      ).sample

				assert image is not None, "Make sure output is not None"



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				snake_case  :       Any     			=						model(**self.dummy_input      ).sample

				assert image is not None, "Make sure output is not None"



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			Any      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       Any     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model_accelerate.to(UpperCamelCase__      )
				model_accelerate.eval()

				snake_case  :       Optional[Any]     			=						torch.randn(
				    1  ,	model_accelerate.config.in_channels  ,	model_accelerate.config.sample_size  ,	model_accelerate.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       int     			=						noise.to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						model_accelerate(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				# two models don't need to stay in the device at the same time
				del model_accelerate
				torch.cuda.empty_cache()
				gc.collect()

				snake_case			,snake_case  :       List[Any]     			=						UNetaDModel.from_pretrained(
				    '''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__  ,	low_cpu_mem_usage=UpperCamelCase__      )
				model_normal_load.to(UpperCamelCase__      )
				model_normal_load.eval()
				snake_case  :       Union[str, Any]     			=						model_normal_load(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''      )
				model.eval()
				model.to(UpperCamelCase__      )

				snake_case  :       Any     			=						torch.randn(
				    1  ,	model.config.in_channels  ,	model.config.sample_size  ,	model.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       Dict     			=						noise.to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Union[str, Any]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       int     			=						output[0, -1, -3:, -3:].flatten().cpu()
				# fmt: off
				snake_case  :       Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			List[Any]=(32, 32)      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						4
				snake_case  :       Tuple     			=						3

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor(batch_size * [10]      ).to(dtype=torch.intaa  ,	device=UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



		def       lowerCAmelCase				(      self				:			int      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Dict     			=						{
				    '''block_out_channels''': [32, 64, 64, 64],
				    '''in_channels''': 3,
				    '''layers_per_block''': 1,
				    '''out_channels''': 3,
				    '''time_embedding_type''': '''fourier''',
				    '''norm_eps''': 1e-6,
				    '''mid_block_scale_factor''': math.sqrt(2.0      ),
				    '''norm_num_groups''': None,
				    '''down_block_types''': [
				        '''SkipDownBlock2D''',
				        '''AttnSkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				    ],
				    '''up_block_types''': [
				        '''SkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				        '''AttnSkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				    ],
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict



		@slow
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''  ,	output_loading_info=UpperCamelCase__      )
				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       str     			=						self.dummy_input
				snake_case  :       Dict     			=						floats_tensor((4, 3) + (256, 256)      ).to(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						noise
				snake_case  :       List[Any]     			=						model(**UpperCamelCase__      )

				assert image is not None, "Make sure output is not None"



		@slow
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Dict     			=						4
				snake_case  :       Optional[int]     			=						3
				snake_case  :       Tuple     			=						(256, 256)

				snake_case  :       Optional[int]     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Dict     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       Optional[int]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Any:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       Union[str, Any]     			=						(32, 32)

				snake_case  :       Any     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Dict     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Optional[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       int     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				pass


