
"""simple docstring"""

import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
					from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
					_A            = None

if is_ray_available():
					import ray  # noqa: E402 # isort:skip
					from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
					_A            = None
					_A            = None
					_A            = None
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
				lowerCamelCase     :			Optional[int]		=    unittest.skip('test requires Datasets, Faiss, psutil'			)(a_			)
		return test_case






@require_distributed_retrieval
class        _lowercase   (				__UpperCAmelCase		):




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Tuple		=    tempfile.mkdtemp()
			lowerCamelCase     :			Any		=    8

			# DPR tok
			lowerCamelCase     :			str		=    [
			    '[UNK]',
			    '[CLS]',
			    '[SEP]',
			    '[PAD]',
			    '[MASK]',
			    'want',
			    '##want',
			    '##ed',
			    'wa',
			    'un',
			    'runn',
			    '##ing',
			    ',',
			    'low',
			    'lowest',
			]
			lowerCamelCase     :			Optional[Any]		=    os.path.join(self.tmpdirname		, 'dpr_tokenizer'					)
			os.makedirs(UpperCAmelCase_		, exist_ok=UpperCAmelCase_					)
			lowerCamelCase     :			int		=    os.path.join(UpperCAmelCase_		, DPR_VOCAB_FILES_NAMES['vocab_file']					)
			with open(self.vocab_file		, 'w'		, encoding='utf-8'					) as vocab_writer:
					vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)

			# BART tok
			lowerCamelCase     :			List[Any]		=    [
			    'l',
			    'o',
			    'w',
			    'e',
			    'r',
			    's',
			    't',
			    'i',
			    'd',
			    'n',
			    '\u0120',
			    '\u0120l',
			    '\u0120n',
			    '\u0120lo',
			    '\u0120low',
			    'er',
			    '\u0120lowest',
			    '\u0120newer',
			    '\u0120wider',
			    '<unk>',
			]
			lowerCamelCase     :			Tuple		=    dict(zip(UpperCAmelCase_		, range(len(UpperCAmelCase_					)					)					)					)
			lowerCamelCase     :			Tuple		=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
			lowerCamelCase     :			Union[str, Any]		=    {'unk_token': '<unk>'}

			lowerCamelCase     :			str		=    os.path.join(self.tmpdirname		, 'bart_tokenizer'					)
			os.makedirs(UpperCAmelCase_		, exist_ok=UpperCAmelCase_					)
			lowerCamelCase     :			str		=    os.path.join(UpperCAmelCase_		, BART_VOCAB_FILES_NAMES['vocab_file']					)
			lowerCamelCase     :			int		=    os.path.join(UpperCAmelCase_		, BART_VOCAB_FILES_NAMES['merges_file']					)
			with open(self.vocab_file		, 'w'		, encoding='utf-8'					) as fp:
					fp.write(json.dumps(UpperCAmelCase_					) + '\n'					)
			with open(self.merges_file		, 'w'		, encoding='utf-8'					) as fp:
					fp.write('\n'.join(UpperCAmelCase_					)					)




	def 			_UpperCamelCase    ( self					)		->							DPRQuestionEncoderTokenizer:
			return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		, 'dpr_tokenizer'					)					)




	def 			_UpperCamelCase    ( self					)		->							BartTokenizer:
			return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		, 'bart_tokenizer'					)					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			shutil.rmtree(self.tmpdirname					)




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[int]		=    Dataset.from_dict(
			    {
			        'id': ['0', '1'],
			        'text': ['foo', 'bar'],
			        'title': ['Foo', 'Bar'],
			        'embeddings': [np.ones(self.retrieval_vector_size					), 2 * np.ones(self.retrieval_vector_size					)],
			    }					)
			dataset.add_faiss_index('embeddings'		, string_factory='Flat'		, metric_type=faiss.METRIC_INNER_PRODUCT					)
			return dataset




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_=12345					)		->							RagPyTorchDistributedRetriever:
			lowerCamelCase     :			List[Any]		=    self.get_dummy_dataset()
			lowerCamelCase     :			Tuple		=    RagConfig(
			    retrieval_vector_size=self.retrieval_vector_size		, question_encoder=DPRConfig().to_dict()		, generator=BartConfig().to_dict()		, )
			with patch('transformers.models.rag.retrieval_rag.load_dataset'					) as mock_load_dataset:
					lowerCamelCase     :			Optional[int]		=    dataset
					lowerCamelCase     :			int		=    RagPyTorchDistributedRetriever(
					    UpperCAmelCase_		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, )
					if init_retrieval:
							retriever.init_retrieval(UpperCAmelCase_					)
			return retriever




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							RagRayDistributedRetriever:
			# Have to run in local mode because sys.path modifications at top of
			# file are not propogated to remote workers.
			# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
			ray.init(local_mode=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    RagConfig(
			    retrieval_vector_size=self.retrieval_vector_size		, question_encoder=DPRConfig().to_dict()		, generator=BartConfig().to_dict()		, )
			lowerCamelCase     :			Optional[Any]		=    ray.remote(UpperCAmelCase_					)
			lowerCamelCase     :			Optional[Any]		=    [remote_cls.remote() for _ in range(1					)]
			with patch('transformers.models.rag.retrieval_rag.load_dataset'					) as mock_load_dataset:
					lowerCamelCase     :			Dict		=    self.get_dummy_dataset()
					lowerCamelCase     :			Union[str, Any]		=    RagRayDistributedRetriever(
					    UpperCAmelCase_		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, retrieval_workers=UpperCAmelCase_		, )
					if init_retrieval:
							retriever.init_retrieval()
			return retriever




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_=12345					)		->							Tuple:
			lowerCamelCase     :			Optional[Any]		=    self.get_dummy_dataset()
			lowerCamelCase     :			Union[str, Any]		=    RagConfig(
			    retrieval_vector_size=self.retrieval_vector_size		, question_encoder=DPRConfig().to_dict()		, generator=BartConfig().to_dict()		, index_name='custom'		, )
			if from_disk:
					lowerCamelCase     :			Tuple		=    os.path.join(self.tmpdirname		, 'dataset'					)
					lowerCamelCase     :			str		=    os.path.join(self.tmpdirname		, 'index.faiss'					)
					dataset.get_index('embeddings'					).save(os.path.join(self.tmpdirname		, 'index.faiss'					)					)
					dataset.drop_index('embeddings'					)
					dataset.save_to_disk(os.path.join(self.tmpdirname		, 'dataset'					)					)
					del dataset
					lowerCamelCase     :			List[str]		=    RagPyTorchDistributedRetriever(
					    UpperCAmelCase_		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, )
			else:
					lowerCamelCase     :			List[Any]		=    RagPyTorchDistributedRetriever(
					    UpperCAmelCase_		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, index=CustomHFIndex(config.retrieval_vector_size		, UpperCAmelCase_					)		, )
			if init_retrieval:
					retriever.init_retrieval(UpperCAmelCase_					)
			return retriever




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Any:
			# Have to run in local mode because sys.path modifications at top of
			# file are not propogated to remote workers.
			# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
			ray.init(local_mode=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    self.get_dummy_dataset()
			lowerCamelCase     :			List[Any]		=    RagConfig(
			    retrieval_vector_size=self.retrieval_vector_size		, question_encoder=DPRConfig().to_dict()		, generator=BartConfig().to_dict()		, index_name='custom'		, )
			lowerCamelCase     :			Optional[Any]		=    ray.remote(UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    [remote_cls.remote() for _ in range(1					)]
			if from_disk:
					lowerCamelCase     :			List[Any]		=    os.path.join(self.tmpdirname		, 'dataset'					)
					lowerCamelCase     :			Optional[Any]		=    os.path.join(self.tmpdirname		, 'index.faiss'					)
					dataset.get_index('embeddings'					).save(os.path.join(self.tmpdirname		, 'index.faiss'					)					)
					dataset.drop_index('embeddings'					)
					dataset.save_to_disk(os.path.join(self.tmpdirname		, 'dataset'					)					)
					del dataset
					lowerCamelCase     :			List[str]		=    RagRayDistributedRetriever(
					    UpperCAmelCase_		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, retrieval_workers=UpperCAmelCase_		, index=CustomHFIndex.load_from_disk(
					        vector_size=config.retrieval_vector_size		, dataset_path=config.passages_path		, index_path=config.index_path		, )		, )
			else:
					lowerCamelCase     :			Any		=    RagRayDistributedRetriever(
					    UpperCAmelCase_		, question_encoder_tokenizer=self.get_dpr_tokenizer()		, generator_tokenizer=self.get_bart_tokenizer()		, retrieval_workers=UpperCAmelCase_		, index=CustomHFIndex(config.retrieval_vector_size		, UpperCAmelCase_					)		, )
			if init_retrieval:
					retriever.init_retrieval()
			return retriever




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							None:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Any		=    retriever.retrieve(UpperCAmelCase_		, n_docs=UpperCAmelCase_					)
			self.assertEqual(retrieved_doc_embeds.shape		, (2, n_docs, self.retrieval_vector_size)					)
			self.assertEqual(len(UpperCAmelCase_					)		, 2					)
			self.assertEqual(sorted(doc_dicts[0]					)		, ['embeddings', 'id', 'text', 'title']					)
			self.assertEqual(len(doc_dicts[0]['id']					)		, UpperCAmelCase_					)
			self.assertEqual(doc_dicts[0]['id'][0]		, '1'					)  # max inner product is reached with second doc
			self.assertEqual(doc_dicts[1]['id'][0]		, '0'					)  # max inner product is reached with first doc
			self.assertListEqual(doc_ids.tolist()		, [[1], [0]]					)




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			lowerCamelCase     :			List[str]		=    1
			lowerCamelCase     :			Dict		=    np.array(
			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]		, dtype=np.floataa					)

			self.distributed_retriever_check(
			    self.get_dummy_pytorch_distributed_retriever(init_retrieval=UpperCAmelCase_					)		, UpperCAmelCase_		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			Optional[int]		=    1
			lowerCamelCase     :			Union[str, Any]		=    np.array(
			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]		, dtype=np.floataa					)

			self.distributed_retriever_check(
			    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=UpperCAmelCase_		, from_disk=UpperCAmelCase_					)		, UpperCAmelCase_		, UpperCAmelCase_		, )




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			List[Any]		=    1
			lowerCamelCase     :			Optional[int]		=    np.array(
			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]		, dtype=np.floataa					)

			self.distributed_retriever_check(
			    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=UpperCAmelCase_		, from_disk=UpperCAmelCase_					)		, UpperCAmelCase_		, UpperCAmelCase_		, )




	@require_ray
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			int		=    1
			lowerCamelCase     :			Union[str, Any]		=    np.array(
			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]		, dtype=np.floataa					)

			self.distributed_retriever_check(
			    self.get_dummy_ray_distributed_retriever(init_retrieval=UpperCAmelCase_					)		, UpperCAmelCase_		, UpperCAmelCase_					)
			ray.shutdown()




	@require_ray
	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Optional[int]		=    1
			lowerCamelCase     :			Optional[int]		=    np.array(
			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]		, dtype=np.floataa					)
			with self.assertRaises(UpperCAmelCase_					):
					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=UpperCAmelCase_		, from_disk=UpperCAmelCase_					)		, UpperCAmelCase_		, UpperCAmelCase_		, )
			ray.shutdown()




	@require_ray
	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase     :			str		=    1
			lowerCamelCase     :			List[Any]		=    np.array(
			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]		, dtype=np.floataa					)

			self.distributed_retriever_check(
			    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=UpperCAmelCase_		, from_disk=UpperCAmelCase_					)		, UpperCAmelCase_		, UpperCAmelCase_					)
			ray.shutdown()












import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()
class 				__snake_case     (unittest.TestCase						):
				lowerCAmelCase__			=   "hf-internal-testing/tiny-stable-diffusion-pipe"
				lowerCAmelCase__			=   1
				lowerCAmelCase__			=   7_7
				lowerCAmelCase__			=   3_2
				lowerCAmelCase__			=   4
				lowerCAmelCase__			=   lowerCAmelCase__			=   6_4
				lowerCAmelCase__			=   torch.manual_seed(0						)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						Optional[Any]=0.9999					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					UNetaDConditionModel.from_pretrained(self.model_id						,		subfolder="""unet"""					)
								_lowerCAmelCase			:			Optional[Any]        =					unet.to(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					EMAModel(unet.parameters()						,		decay=_UpperCAmelCase						,		model_cls=_UpperCAmelCase						,		model_config=unet.config					)
								return unet, ema_unet



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





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					torch.randn(
								    self.batch_size						,		self.num_in_channels						,		self.latent_height						,		self.latent_width						,		generator=self.generator					).to(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					torch.randint(0						,		1000						,		size=(self.batch_size,)						,		generator=self.generator					).to(_UpperCAmelCase					)
								_lowerCAmelCase			:			Any        =					torch.randn(
								    self.batch_size						,		self.prompt_length						,		self.text_encoder_hidden_dim						,		generator=self.generator					).to(_UpperCAmelCase					)
								return noisy_latents, timesteps, encoder_hidden_states



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						Dict					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					{}
								for k, param in unet.state_dict().items():
												_lowerCAmelCase			:			List[Any]        =					torch.randn_like(_UpperCAmelCase					) + (param * torch.randn_like(_UpperCAmelCase					))
												updated_state_dict.update({k: updated_param}					)
								unet.load_state_dict(_UpperCAmelCase					)
								return unet



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[int]        =					self.get_models()
								# Take the first (hypothetical) EMA step.
								ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 1

								# Take two more.
								for _ in range(2					):
												ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 3



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					self.get_models()
								# Since the `unet` is not being updated (i.e., backprop'd)
								# there won't be any difference between the `params` of `unet`
								# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
								ema_unet.step(unet.parameters()					)
								_lowerCAmelCase			:			Optional[int]        =					list(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)

								# The above holds true even if we call `ema.step()` multiple times since
								# `unet` params are still not being updated.
								for _ in range(4					):
												ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



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





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					self.get_models()
								# Here we simulate the parameter updates for `unet`. Since there might
								# be some parameters which are initialized to zero we take extra care to
								# initialize their values to something non-zero before the multiplication.
								_lowerCAmelCase			:			str        =					self.simulate_backprop(_UpperCAmelCase					)

								# Take the EMA step.
								ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

								# Now the EMA'd parameters won't be equal to the original model parameters.
								_lowerCAmelCase			:			Tuple        =					list(unet_pseudo_updated_step_one.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)

								# Ensure this is the case when we take multiple EMA steps.
								for _ in range(4					):
												ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			str        =					self.get_models()

								# First backprop + EMA
								_lowerCAmelCase			:			Optional[int]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_one.parameters()					)
								_lowerCAmelCase			:			List[str]        =					ema_unet.shadow_params

								# Second backprop + EMA
								_lowerCAmelCase			:			Optional[int]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_two.parameters()					)
								_lowerCAmelCase			:			Tuple        =					ema_unet.shadow_params

								for step_one, step_two in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



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





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			int        =					self.get_models(decay=0.0					)
								_lowerCAmelCase			:			Dict        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_one.parameters()					)
								_lowerCAmelCase			:			Any        =					ema_unet.shadow_params

								_lowerCAmelCase			:			List[Any]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_two.parameters()					)
								_lowerCAmelCase			:			int        =					ema_unet.shadow_params

								for step_one, step_two in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				@skip_mps
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.get_models()
								_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdir:
												ema_unet.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			Union[str, Any]        =					UNetaDConditionModel.from_pretrained(_UpperCAmelCase						,		model_cls=_UpperCAmelCase					)
												_lowerCAmelCase			:			Any        =					loaded_unet.to(unet.device					)

								# Since no EMA step has been performed the outputs should match.
								_lowerCAmelCase			:			str        =					unet(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					).sample
								_lowerCAmelCase			:			Union[str, Any]        =					loaded_unet(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					).sample

								assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase						,		atol=1E-4					)

