



import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('3.8'):
							import importlib_metadata
else:
							import importlib.metadata as importlib_metadata






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	List[str]=False):





				'''simple docstring'''

				try:
								lowerCAmelCase__  :       Optional[int]				=  os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								lowerCAmelCase__  :       Optional[int]				=  default
				else:
								# KEY is set, convert it to True or False.
								try:
												lowerCAmelCase__  :       int				=  strtobool(lowerCamelCase_)
								except ValueError:
												# More values are supported, but let's keep the message simple.
												raise ValueError(f"""If set, {key} must be yes or no.""")
				return _value


__snake_case            =parse_flag_from_env('RUN_SLOW', default=False)
__snake_case            =parse_flag_from_env('RUN_REMOTE', default=False)
__snake_case            =parse_flag_from_env('RUN_LOCAL', default=True)
__snake_case            =parse_flag_from_env('RUN_PACKAGED', default=True)

# Compression
__snake_case            =pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4')
__snake_case            =pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr')
__snake_case            =pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard')

# Audio
__snake_case            =pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'),
    reason='test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ',
)

# Beam
__snake_case            =pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'),
    reason='test requires apache-beam and a compatible dill version',
)

# Dill-cloudpickle compatibility
__snake_case            =pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('0.3.2'),
    reason='test requires dill>0.3.2 for cloudpickle compatibility',
)

# Windows
__snake_case            =pytest.mark.skipif(
    sys.platform == 'win32',
    reason='test should not be run on Windows',
)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				try:
								import faiss  # noqa
				except ImportError:
								lowerCAmelCase__  :       Optional[Any]				=  unittest.skip('''test requires faiss''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				try:
								import regex  # noqa
				except ImportError:
								lowerCAmelCase__  :       Optional[int]				=  unittest.skip('''test requires regex''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str]):





				'''simple docstring'''

				try:
								import elasticsearch  # noqa
				except ImportError:
								lowerCAmelCase__  :       str				=  unittest.skip('''test requires elasticsearch''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				try:
								import sqlalchemy  # noqa
				except ImportError:
								lowerCAmelCase__  :       Optional[int]				=  unittest.skip('''test requires sqlalchemy''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str):





				'''simple docstring'''

				if not config.TORCH_AVAILABLE:
								lowerCAmelCase__  :       Optional[int]				=  unittest.skip('''test requires PyTorch''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple):





				'''simple docstring'''

				if not config.TF_AVAILABLE:
								lowerCAmelCase__  :       Dict				=  unittest.skip('''test requires TensorFlow''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				if not config.JAX_AVAILABLE:
								lowerCAmelCase__  :       Optional[Any]				=  unittest.skip('''test requires JAX''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str):





				'''simple docstring'''

				if not config.PIL_AVAILABLE:
								lowerCAmelCase__  :       Any				=  unittest.skip('''test requires Pillow''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				try:
								import transformers  # noqa F401
				except ImportError:
								return unittest.skip('''test requires transformers''')(lowerCamelCase_)
				else:
								return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				try:
								import tiktoken  # noqa F401
				except ImportError:
								return unittest.skip('''test requires tiktoken''')(lowerCamelCase_)
				else:
								return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any):





				'''simple docstring'''

				try:
								import spacy  # noqa F401
				except ImportError:
								return unittest.skip('''test requires spacy''')(lowerCamelCase_)
				else:
								return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any):





				'''simple docstring'''

				def _require_spacy_model(lowerCamelCase_	:	Dict):
								try:
												import spacy  # noqa F401

												spacy.load(lowerCamelCase_)
								except ImportError:
												return unittest.skip('''test requires spacy''')(lowerCamelCase_)
								except OSError:
												return unittest.skip('''test requires spacy model \'{}\''''.format(lowerCamelCase_))(lowerCamelCase_)
								else:
												return test_case

				return _require_spacy_model






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str):





				'''simple docstring'''

				try:
								import pyspark  # noqa F401
				except ImportError:
								return unittest.skip('''test requires pyspark''')(lowerCamelCase_)
				else:
								return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				try:
								import joblibspark  # noqa F401
				except ImportError:
								return unittest.skip('''test requires joblibspark''')(lowerCamelCase_)
				else:
								return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str]):





				'''simple docstring'''

				if not _run_slow_tests or _run_slow_tests == 0:
								lowerCAmelCase__  :       str				=  unittest.skip('''test is slow''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				if not _run_local_tests or _run_local_tests == 0:
								lowerCAmelCase__  :       Tuple				=  unittest.skip('''test is local''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict):





				'''simple docstring'''

				if not _run_packaged_tests or _run_packaged_tests == 0:
								lowerCAmelCase__  :       str				=  unittest.skip('''test is packaged''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				if not _run_remote_tests or _run_remote_tests == 0:
								lowerCAmelCase__  :       Optional[int]				=  unittest.skip('''test requires remote''')(lowerCamelCase_)
				return test_case






def 			lowerCAmelCase__	(					*lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				def decorate(cls	:	Tuple):
								for name, fn in cls.__dict__.items():
												if callable(lowerCamelCase_) and name.startswith('''test'''):
																for decorator in decorators:
																				lowerCAmelCase__  :       Tuple				=  decorator(lowerCamelCase_)
																setattr(cls ,lowerCamelCase_ ,lowerCamelCase_)
								return cls

				return decorate



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		pass



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=0
		snake_case_			=1
		snake_case_			=2






@contextmanager
def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]=OfflineSimulationMode.CONNECTION_FAILS ,lowerCamelCase_	:	Optional[int]=1E-16):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  requests.Session().request

				def timeout_request(lowerCamelCase_	:	str ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	List[Any] ,**lowerCamelCase_	:	List[str]):
								# Change the url to an invalid url so that the connection hangs
								lowerCAmelCase__  :       Optional[Any]				=  '''https://10.255.255.1'''
								if kwargs.get('''timeout''') is None:
												raise RequestWouldHangIndefinitelyError(
												    f"""Tried a call to {url} in offline mode with no timeout set. Please set a timeout.""")
								lowerCAmelCase__  :       Tuple				=  timeout
								try:
												return online_request(lowerCamelCase_ ,lowerCamelCase_ ,**lowerCamelCase_)
								except Exception as e:
												# The following changes in the error are just here to make the offline timeout error prettier
												lowerCAmelCase__  :       str				=  url
												lowerCAmelCase__  :       Union[str, Any]				=  e.args[0]
												lowerCAmelCase__  :       Optional[Any]				=  (max_retry_error.args[0].replace('''10.255.255.1''' ,f"""OfflineMock[{url}]"""),)
												lowerCAmelCase__  :       Any				=  (max_retry_error,)
												raise

				def raise_connection_error(lowerCamelCase_	:	Dict ,lowerCamelCase_	:	int ,**lowerCamelCase_	:	Optional[int]):
								raise requests.ConnectionError('''Offline mode is enabled.''' ,request=lowerCamelCase_)

				if mode is OfflineSimulationMode.CONNECTION_FAILS:
								with patch('''requests.Session.send''' ,lowerCamelCase_):
												yield
				elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
								# inspired from https://stackoverflow.com/a/904609
								with patch('''requests.Session.request''' ,lowerCamelCase_):
												yield
				elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
								with patch('''datasets.config.HF_DATASETS_OFFLINE''' ,lowerCamelCase_):
												yield
				else:
								raise ValueError('''Please use a value from the OfflineSimulationMode enum.''')






@contextmanager
def 			lowerCAmelCase__	(					*lowerCamelCase_	:	List[str] ,**lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  str(Path().resolve())
				with tempfile.TemporaryDirectory(*lowerCamelCase_ ,**lowerCamelCase_) as tmp_dir:
								try:
												os.chdir(lowerCamelCase_)
												yield
								finally:
												os.chdir(lowerCamelCase_)






@contextmanager
def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				import gc

				gc.collect()
				lowerCAmelCase__  :       List[Any]				=  pa.total_allocated_bytes()
				yield
				assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."






@contextmanager
def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				import gc

				gc.collect()
				lowerCAmelCase__  :       Optional[Any]				=  pa.total_allocated_bytes()
				yield
				assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	str):





				'''simple docstring'''

				return deepcopy(lowerCamelCase_).integers(0 ,100 ,10).tolist() == deepcopy(lowerCamelCase_).integers(0 ,100 ,10).tolist()






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				import decorator
				from requests.exceptions import HTTPError

				def _wrapper(lowerCamelCase_	:	str ,*lowerCamelCase_	:	Dict ,**lowerCamelCase_	:	List[str]):
								try:
												return func(*lowerCamelCase_ ,**lowerCamelCase_)
								except HTTPError as err:
												if str(lowerCamelCase_).startswith('''500''') or str(lowerCamelCase_).startswith('''502'''):
																pytest.xfail(str(lowerCamelCase_))
												raise err

				return decorator.decorator(_wrapper ,lowerCamelCase_)



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  returncode
						lowerCAmelCase__  :       List[Any]				=  stdout
						lowerCAmelCase__  :       Tuple				=  stderr






async def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	int):





				'''simple docstring'''

				while True:
								lowerCAmelCase__  :       Union[str, Any]				=  await stream.readline()
								if line:
												callback(lowerCamelCase_)
								else:
												break






async def 			lowerCAmelCase__	(					lowerCamelCase_	:	str ,lowerCamelCase_	:	Tuple=None ,lowerCamelCase_	:	List[str]=None ,lowerCamelCase_	:	Dict=None ,lowerCamelCase_	:	int=False ,lowerCamelCase_	:	Dict=False):





				'''simple docstring'''

				if echo:
								print('''\nRunning: ''' ,''' '''.join(lowerCamelCase_))

				lowerCAmelCase__  :       Dict				=  await asyncio.create_subprocess_exec(
				    cmd[0] ,*cmd[1:] ,stdin=lowerCamelCase_ ,stdout=asyncio.subprocess.PIPE ,stderr=asyncio.subprocess.PIPE ,env=lowerCamelCase_ ,)

				# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
				# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
				#
				# If it starts hanging, will need to switch to the following code. The problem is that no data
				# will be seen until it's done and if it hangs for example there will be no debug info.
				# out, err = await p.communicate()
				# return _RunOutput(p.returncode, out, err)

				lowerCAmelCase__  :       List[str]				=  []
				lowerCAmelCase__  :       Optional[int]				=  []

				def tee(lowerCamelCase_	:	Optional[int] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Optional[int] ,lowerCamelCase_	:	Optional[Any]=""):
								lowerCAmelCase__  :       str				=  line.decode('''utf-8''').rstrip()
								sink.append(lowerCamelCase_)
								if not quiet:
												print(lowerCamelCase_ ,lowerCamelCase_ ,file=lowerCamelCase_)

    # XXX: the timeout doesn't seem to make any difference here
				await asyncio.wait(
				    [
				        _read_stream(p.stdout ,lambda lowerCamelCase_: tee(lowerCamelCase_ ,lowerCamelCase_ ,sys.stdout ,label='''stdout:''')),
				        _read_stream(p.stderr ,lambda lowerCamelCase_: tee(lowerCamelCase_ ,lowerCamelCase_ ,sys.stderr ,label='''stderr:''')),
				    ] ,timeout=lowerCamelCase_ ,)
				return _RunOutput(await p.wait() ,lowerCamelCase_ ,lowerCamelCase_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Any=None ,lowerCamelCase_	:	Dict=None ,lowerCamelCase_	:	Optional[int]=180 ,lowerCamelCase_	:	str=False ,lowerCamelCase_	:	Dict=True):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  asyncio.get_event_loop()
				lowerCAmelCase__  :       str				=  loop.run_until_complete(
				    _stream_subprocess(lowerCamelCase_ ,env=lowerCamelCase_ ,stdin=lowerCamelCase_ ,timeout=lowerCamelCase_ ,quiet=lowerCamelCase_ ,echo=lowerCamelCase_))

				lowerCAmelCase__  :       List[Any]				=  ''' '''.join(lowerCamelCase_)
				if result.returncode > 0:
								lowerCAmelCase__  :       List[str]				=  '''\n'''.join(result.stderr)
								raise RuntimeError(
								    f"""\'{cmd_str}\' failed with returncode {result.returncode}\n\n"""
								    f"""The combined stderr from workers follows:\n{stderr}""")

				# check that the subprocess actually did run and produced some output, should the test rely on
				# the remote side to do the testing
				if not result.stdout and not result.stderr:
								raise RuntimeError(f"""\'{cmd_str}\' produced no output.""")

				return result






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  os.environ.get('''PYTEST_XDIST_WORKER''' ,'''gw0''')
				lowerCAmelCase__  :       Union[str, Any]				=  re.sub(r'''^gw''' ,'''''' ,lowerCamelCase_ ,0 ,re.M)
				return int(lowerCamelCase_)






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  29500
				lowerCAmelCase__  :       Any				=  pytest_xdist_worker_id()
				return port + uniq_delta






import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=AutoencoderKL
		snake_case_			="""sample"""
		snake_case_			=1e-2



		@property
		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






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

						lowerCAmelCase__  :       Tuple				=  floats_tensor((batch_size, num_channels) + sizes				).to(__lowerCamelCase				)

						return {"sample": image}



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

						"""simple docstring"""






						return (3, 32, 32)



		@property
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						return (3, 32, 32)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  {
						    '''block_out_channels''': [32, 64],
						    '''in_channels''': 3,
						    '''out_channels''': 3,
						    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
						    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
						    '''latent_channels''': 4,
						}
						lowerCAmelCase__  :       str				=  self.dummy_input
						return init_dict, inputs_dict



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						pass



		@unittest.skipIf(torch_device == '''mps'''	,'''Gradient checkpointing skipped on MPS'''				)
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  self.prepare_init_args_and_inputs_for_common()
						lowerCAmelCase__  :       Tuple				=  self.model_class(**__lowerCamelCase				)
						model.to(__lowerCamelCase				)

						assert not model.is_gradient_checkpointing and model.training

						lowerCAmelCase__  :       Tuple				=  model(**__lowerCamelCase				).sample
						# run the backwards pass on the model. For backwards pass, for simplicity purpose,
						# we won't calculate the loss and rather backprop on out.sum()
						model.zero_grad()

						lowerCAmelCase__  :       Optional[Any]				=  torch.randn_like(__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  (out - labels).mean()
						loss.backward()

						# re-instantiate the model now enabling gradient checkpointing
						lowerCAmelCase__  :       str				=  self.model_class(**__lowerCamelCase				)
						# clone model
						model_a.load_state_dict(model.state_dict()				)
						model_a.to(__lowerCamelCase				)
						model_a.enable_gradient_checkpointing()

						assert model_a.is_gradient_checkpointing and model_a.training

						lowerCAmelCase__  :       Dict				=  model_a(**__lowerCamelCase				).sample
						# run the backwards pass on the model. For backwards pass, for simplicity purpose,
						# we won't calculate the loss and rather backprop on out.sum()
						model_a.zero_grad()
						lowerCAmelCase__  :       Any				=  (out_a - labels).mean()
						loss_a.backward()

						# compare the output and parameters gradients
						self.assertTrue((loss - loss_a).abs() < 1e-5				)
						lowerCAmelCase__  :       List[str]				=  dict(model.named_parameters()				)
						lowerCAmelCase__  :       Optional[Any]				=  dict(model_a.named_parameters()				)
						for name, param in named_params.items():
										self.assertTrue(torch_all_close(param.grad.data	,named_params_a[name].grad.data	,atol=5e-5				)				)



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       str				=  AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''	,output_loading_info=__lowerCamelCase				)
						self.assertIsNotNone(__lowerCamelCase				)
						self.assertEqual(len(loading_info['''missing_keys''']				)	,0				)

						model.to(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  model(**self.dummy_input				)

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



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

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
						lowerCAmelCase__  :       Tuple				=  model.to(__lowerCamelCase				)
						model.eval()

						if torch_device == "mps":
										lowerCAmelCase__  :       Union[str, Any]				=  torch.manual_seed(0				)
						else:
										lowerCAmelCase__  :       Any				=  torch.Generator(device=__lowerCamelCase				).manual_seed(0				)

						lowerCAmelCase__  :       Optional[int]				=  torch.randn(
						    1	,model.config.in_channels	,model.config.sample_size	,model.config.sample_size	,generator=torch.manual_seed(0				)	,)
						lowerCAmelCase__  :       Dict				=  image.to(__lowerCamelCase				)
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(__lowerCamelCase	,sample_posterior=__lowerCamelCase	,generator=__lowerCamelCase				).sample

						lowerCAmelCase__  :       Optional[Any]				=  output[0, -1, -3:, -3:].flatten().cpu()

						# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
						# the expected output slices are not the same for CPU and GPU.
						if torch_device == "mps":
										lowerCAmelCase__  :       List[str]				=  torch.tensor(
										    [
										        -4.0078e-01,
										        -3.8323e-04,
										        -1.2681e-01,
										        -1.1462e-01,
										        2.0095e-01,
										        1.0893e-01,
										        -8.8247e-02,
										        -3.0361e-01,
										        -9.8644e-03,
										    ]				)
						elif torch_device == "cpu":
										lowerCAmelCase__  :       str				=  torch.tensor(
										    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]				)
						else:
										lowerCAmelCase__  :       Any				=  torch.tensor(
										    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]				)

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



@slow
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						return f"""gaussian_noise_s={seed}_shape={'_'.join([str(__lowerCamelCase				) for s in shape]				)}.npy"""



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=0	,__lowerCamelCase=(4, 3, 5_12, 5_12)	,__lowerCamelCase=False				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  torch.floataa if fpaa else torch.floataa
						lowerCAmelCase__  :       Dict				=  torch.from_numpy(load_hf_numpy(self.get_file_format(__lowerCamelCase	,__lowerCamelCase				)				)				).to(__lowerCamelCase				).to(__lowerCamelCase				)
						return image



		def 	lowerCAmelCase__   (self	,__lowerCamelCase="CompVis/stable-diffusion-v1-4"	,__lowerCamelCase=False				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  '''fp16''' if fpaa else None
						lowerCAmelCase__  :       Optional[Any]				=  torch.floataa if fpaa else torch.floataa

						lowerCAmelCase__  :       List[Any]				=  AutoencoderKL.from_pretrained(
						    __lowerCamelCase	,subfolder='''vae'''	,torch_dtype=__lowerCamelCase	,revision=__lowerCamelCase	,)
						model.to(__lowerCamelCase				).eval()

						return model



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=0				)  ->    str:

						"""simple docstring"""






						if torch_device == "mps":
										return torch.manual_seed(__lowerCamelCase				)
						return torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ]				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Any				=  self.get_sd_image(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  self.get_generator(__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase	,generator=__lowerCamelCase	,sample_posterior=__lowerCamelCase				).sample

						assert sample.shape == image.shape

						lowerCAmelCase__  :       Optional[Any]				=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
						lowerCAmelCase__  :       Any				=  torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=3e-3				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
		        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
		        # fmt: on
		    ]				)
		@require_torch_gpu
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.get_sd_vae_model(fpaa=__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  self.get_sd_image(__lowerCamelCase	,fpaa=__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  self.get_generator(__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase	,generator=__lowerCamelCase	,sample_posterior=__lowerCamelCase				).sample

						assert sample.shape == image.shape

						lowerCAmelCase__  :       Optional[Any]				=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
						lowerCAmelCase__  :       Any				=  torch.tensor(__lowerCamelCase				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=1e-2				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ]				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Dict				=  self.get_sd_image(__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Tuple				=  model(__lowerCamelCase				).sample

						assert sample.shape == image.shape

						lowerCAmelCase__  :       Dict				=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
						lowerCAmelCase__  :       List[Any]				=  torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=3e-3				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
		        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
		        # fmt: on
		    ]				)
		@require_torch_gpu
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Union[str, Any]				=  self.get_sd_image(__lowerCamelCase	,shape=(3, 4, 64, 64)				)

						with torch.no_grad():
										lowerCAmelCase__  :       str				=  model.decode(__lowerCamelCase				).sample

						assert list(sample.shape				) == [3, 3, 5_12, 5_12]

						lowerCAmelCase__  :       Dict				=  sample[-1, -2:, :2, -2:].flatten().cpu()
						lowerCAmelCase__  :       Any				=  torch.tensor(__lowerCamelCase				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=1e-3				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
		        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
		        # fmt: on
		    ]				)
		@require_torch_gpu
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.get_sd_vae_model(fpaa=__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  self.get_sd_image(__lowerCamelCase	,shape=(3, 4, 64, 64)	,fpaa=__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Optional[Any]				=  model.decode(__lowerCamelCase				).sample

						assert list(sample.shape				) == [3, 3, 5_12, 5_12]

						lowerCAmelCase__  :       Dict				=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
						lowerCAmelCase__  :       Union[str, Any]				=  torch.tensor(__lowerCamelCase				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=5e-3				)



		@parameterized.expand([(13,), (16,), (27,)]				)
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()	,reason='''xformers is not required when using PyTorch 2.0.'''				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.get_sd_vae_model(fpaa=__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  self.get_sd_image(__lowerCamelCase	,shape=(3, 4, 64, 64)	,fpaa=__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Any				=  model.decode(__lowerCamelCase				).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
										lowerCAmelCase__  :       Optional[Any]				=  model.decode(__lowerCamelCase				).sample

						assert list(sample.shape				) == [3, 3, 5_12, 5_12]

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=1e-1				)



		@parameterized.expand([(13,), (16,), (37,)]				)
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()	,reason='''xformers is not required when using PyTorch 2.0.'''				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Dict				=  self.get_sd_image(__lowerCamelCase	,shape=(3, 4, 64, 64)				)

						with torch.no_grad():
										lowerCAmelCase__  :       Tuple				=  model.decode(__lowerCamelCase				).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
										lowerCAmelCase__  :       Optional[Any]				=  model.decode(__lowerCamelCase				).sample

						assert list(sample.shape				) == [3, 3, 5_12, 5_12]

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=1e-2				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
		        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
		        # fmt: on
		    ]				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Tuple				=  self.get_sd_image(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  self.get_generator(__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model.encode(__lowerCamelCase				).latent_dist
										lowerCAmelCase__  :       int				=  dist.sample(generator=__lowerCamelCase				)

						assert list(sample.shape				) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

						lowerCAmelCase__  :       Optional[int]				=  sample[0, -1, -3:, -3:].flatten().cpu()
						lowerCAmelCase__  :       int				=  torch.tensor(__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  3e-3 if torch_device != '''mps''' else 1e-2
						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=__lowerCamelCase				)

