
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowercase__						       =      get_logger(__name__)






class 	snake_case__       :







		"""simple docstring"""

		lowerCamelCase     							=						"""dummy_data"""
		lowerCamelCase     							=						"""datasets"""
		lowerCamelCase     							=						False



		def __init__(      self				:			str  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Union[Version, str]  ,	UpperCamelCase__				:			Optional[str] = None  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			Optional[List[Callable]] = None  ,	)						->			int:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						0
				snake_case  :       Optional[int]     			=						dataset_name
				snake_case  :       List[Any]     			=						cache_dir
				snake_case  :       Tuple     			=						use_local_dummy_data
				snake_case  :       str     			=						config
				# download_callbacks take a single url as input
				snake_case  :       List[Callable]     			=						download_callbacks or []
				# if False, it doesn't load existing files and it returns the paths of the dummy files relative
				# to the dummy_data zip file root
				snake_case  :       Tuple     			=						load_existing_dummy_data

				# TODO(PVP, QL) might need to make this more general
				snake_case  :       str     			=						str(UpperCamelCase__      )
				# to be downloaded
				snake_case  :       Any     			=						None
				snake_case  :       int     			=						None



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






				"""simple docstring"""







				if self._dummy_file is None:
						snake_case  :       Tuple     			=						self.download_dummy_data()
				return self._dummy_file



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






				"""simple docstring"""







				if self.config is not None:
						# structure is dummy / config_name / version_name
						return os.path.join('''dummy'''  ,	self.config.name  ,	self.version_name      )
				# structure is dummy / version_name
				return os.path.join('''dummy'''  ,	self.version_name      )



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






				"""simple docstring"""







				return os.path.join(self.dummy_data_folder  ,	'''dummy_data.zip'''      )



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






				"""simple docstring"""







				snake_case  :       Any     			=						(
				    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
				)

				snake_case  :       int     			=						cached_path(
				    UpperCamelCase__  ,	cache_dir=self.cache_dir  ,	extract_compressed_file=UpperCamelCase__  ,	force_extract=UpperCamelCase__      )

				return os.path.join(UpperCamelCase__  ,	self.dummy_file_name      )



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






				"""simple docstring"""







				return os.path.join(self.datasets_scripts_dir  ,	self.dataset_name  ,	self.dummy_zip_file      )



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






				"""simple docstring"""







				if self._bucket_url is None:
						snake_case  :       Tuple     			=						hf_github_url(self.dataset_name  ,	self.dummy_zip_file.replace(os.sep  ,	'''/'''      )      )
				return self._bucket_url



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






				"""simple docstring"""







				if os.path.isdir(self.dummy_file      ):
						return self.dummy_file
				# else cut off path to file -> example `xsum`.
				return "/".join(self.dummy_file.replace(os.sep  ,	'''/'''      ).split('''/'''      )[:-1]      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Optional[int]  ,	*UpperCamelCase__				:			Dict      )						->			Optional[int]:






				"""simple docstring"""







				if self.load_existing_dummy_data:
						# dummy data is downloaded and tested
						snake_case  :       Optional[int]     			=						self.dummy_file
				else:
						# dummy data cannot be downloaded and only the path to dummy file is returned
						snake_case  :       int     			=						self.dummy_file_name

				# special case when data_url is a dict
				if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ):
						return self.create_dummy_data_dict(UpperCamelCase__  ,	UpperCamelCase__      )
				elif isinstance(UpperCamelCase__  ,	(list, tuple)      ):
						return self.create_dummy_data_list(UpperCamelCase__  ,	UpperCamelCase__      )
				else:
						return self.create_dummy_data_single(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Union[str, Any]  ,	*UpperCamelCase__				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				return self.download_and_extract(UpperCamelCase__      )



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






				"""simple docstring"""







				return self.download_and_extract(UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			List[str]  ,	*UpperCamelCase__				:			int  ,	**UpperCamelCase__				:			Optional[int]      )						->			str:






				"""simple docstring"""







				return path



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






				"""simple docstring"""







				return {}



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






				"""simple docstring"""







				snake_case  :       str     			=						{}
				for key, single_urls in data_url.items():
						for download_callback in self.download_callbacks:
								if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ):
										for single_url in single_urls:
												download_callback(UpperCamelCase__      )
								else:
										snake_case  :       Dict     			=						single_urls
										download_callback(UpperCamelCase__      )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
						if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ):
								snake_case  :       Tuple     			=						[os.path.join(UpperCamelCase__  ,	urllib.parse.quote_plus(Path(UpperCamelCase__      ).name      )      ) for x in single_urls]
						else:
								snake_case  :       Tuple     			=						single_urls
								snake_case  :       Optional[int]     			=						os.path.join(UpperCamelCase__  ,	urllib.parse.quote_plus(Path(UpperCamelCase__      ).name      )      )
						snake_case  :       List[str]     			=						value

				# make sure that values are unique
				if all(isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) for i in dummy_data_dict.values()      ) and len(set(dummy_data_dict.values()      )      ) < len(
				    dummy_data_dict.values()      ):
						# append key to value to make its name unique
						snake_case  :       Optional[Any]     			=						{key: value + key for key, value in dummy_data_dict.items()}

				return dummy_data_dict



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






				"""simple docstring"""







				snake_case  :       Any     			=						[]
				# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
				snake_case  :       str     			=						all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''  ,	UpperCamelCase__      )      ) for url in data_url      )
				snake_case  :       Dict     			=						all(
				    url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''      ) for url in data_url      )
				if data_url and (is_tf_records or is_pubmed_records):
						snake_case  :       Union[str, Any]     			=						[data_url[0]] * len(UpperCamelCase__      )
				for single_url in data_url:
						for download_callback in self.download_callbacks:
								download_callback(UpperCamelCase__      )
						# we force the name of each key to be the last file / folder name of the url path
						# if the url has arguments, we need to encode them with urllib.parse.quote_plus
						snake_case  :       str     			=						os.path.join(UpperCamelCase__  ,	urllib.parse.quote_plus(single_url.split('''/'''      )[-1]      )      )
						dummy_data_list.append(UpperCamelCase__      )
				return dummy_data_list



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






				"""simple docstring"""







				for download_callback in self.download_callbacks:
						download_callback(UpperCamelCase__      )
				# we force the name of each key to be the last file / folder name of the url path
				# if the url has arguments, we need to encode them with urllib.parse.quote_plus
				snake_case  :       List[Any]     			=						os.path.join(UpperCamelCase__  ,	urllib.parse.quote_plus(data_url.split('''/'''      )[-1]      )      )
				if os.path.exists(UpperCamelCase__      ) or not self.load_existing_dummy_data:
						return value
				else:
						# Backward compatibility, maybe deprecate at one point.
						# For many datasets with single url calls to dl_manager.download_and_extract,
						# the dummy_data.zip file is actually the zipped downloaded file
						# while now we expected the dummy_data.zip file to be a directory containing
						# the downloaded file.
						return path_to_dummy_data



		def       lowerCAmelCase				(      self				:			Tuple      )						->			Dict:






				"""simple docstring"""







				pass



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






				"""simple docstring"""







				pass



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






				"""simple docstring"""







				def _iter_archive_members(UpperCamelCase__				:			int      ):
						# this preserves the order of the members inside the ZIP archive
						snake_case  :       Optional[int]     			=						Path(self.dummy_file      ).parent
						snake_case  :       int     			=						path.relative_to(UpperCamelCase__      )
						with ZipFile(self.local_path_to_dummy_data      ) as zip_file:
								snake_case  :       Any     			=						zip_file.namelist()
						for member in members:
								if member.startswith(relative_path.as_posix()      ):
										yield dummy_parent_path.joinpath(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						Path(UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						_iter_archive_members(UpperCamelCase__      ) if self.use_local_dummy_data else path.rglob('''*'''      )
				for file_path in file_paths:
						if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')      ):
								yield file_path.relative_to(UpperCamelCase__      ).as_posix(), file_path.open('''rb'''      )



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






				"""simple docstring"""







				if not isinstance(UpperCamelCase__  ,	UpperCamelCase__      ):
						snake_case  :       Optional[int]     			=						[paths]
				for path in paths:
						if os.path.isfile(UpperCamelCase__      ):
								if os.path.basename(UpperCamelCase__      ).startswith(('''.''', '''__''')      ):
										return
								yield path
						else:
								for dirpath, dirnames, filenames in os.walk(UpperCamelCase__      ):
										if os.path.basename(UpperCamelCase__      ).startswith(('''.''', '''__''')      ):
												continue
										dirnames.sort()
										for filename in sorted(UpperCamelCase__      ):
												if filename.startswith(('''.''', '''__''')      ):
														continue
												yield os.path.join(UpperCamelCase__  ,	UpperCamelCase__      )




'''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


