


import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

_snake_case  :	Dict			      =       "bert-base-cased"
_snake_case  :	Dict			      =       "fp16"
_snake_case  :	Optional[int]			      =       "bf16"
_snake_case  :	str			      =       [FPaa, BFaa]

@require_fsdp
@require_cuda
class        __magic_name__	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Dict       )			->					Optional[int]:
														super().setUp()

														__snake_case	:    Dict											=     dict(
														    ACCELERATE_USE_FSDP="true"					,			MASTER_ADDR="localhost"					,			MASTER_PORT="10999"					,			RANK="0"					,			LOCAL_RANK="0"					,			WORLD_SIZE="1"					,			)

							def 					__snake_case      (							self :						int       )			->					Dict:
														from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

														for i, strategy in enumerate(lowerCamelCase       ):
																					__snake_case	:    Tuple											=     self.dist_env.copy()
																					__snake_case	:    Tuple											=     F'{i + 1}'
																					__snake_case	:    str											=     strategy
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Optional[Any]											=     FullyShardedDataParallelPlugin()
																												self.assertEqual(fsdp_plugin.sharding_strategy					,			ShardingStrategy(i + 1       )       )

							def 					__snake_case      (							self :						Optional[Any]       )			->					int:
														from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

														for i, prefetch_policy in enumerate(lowerCamelCase       ):
																					__snake_case	:    List[str]											=     self.dist_env.copy()
																					__snake_case	:    Any											=     prefetch_policy
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Dict											=     FullyShardedDataParallelPlugin()
																												if prefetch_policy == "NO_PREFETCH":
																																			self.assertIsNone(fsdp_plugin.backward_prefetch       )
																												else:
																																			self.assertEqual(fsdp_plugin.backward_prefetch					,			BackwardPrefetch(i + 1       )       )

							def 					__snake_case      (							self :						str       )			->					Union[str, Any]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

														for i, state_dict_type in enumerate(lowerCamelCase       ):
																					__snake_case	:    Dict											=     self.dist_env.copy()
																					__snake_case	:    Union[str, Any]											=     state_dict_type
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Tuple											=     FullyShardedDataParallelPlugin()
																												self.assertEqual(fsdp_plugin.state_dict_type					,			StateDictType(i + 1       )       )
																												if state_dict_type == "FULL_STATE_DICT":
																																			self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu       )
																																			self.assertTrue(fsdp_plugin.state_dict_config.ranka_only       )

							def 					__snake_case      (							self :						List[Any]       )			->					str:
														__snake_case	:    Dict											=     AutoModel.from_pretrained(lowerCamelCase       )
														for policy in FSDP_AUTO_WRAP_POLICY:
																					__snake_case	:    Optional[int]											=     self.dist_env.copy()
																					__snake_case	:    List[Any]											=     policy
																					if policy == "TRANSFORMER_BASED_WRAP":
																												__snake_case	:    List[Any]											=     "BertLayer"
																					elif policy == "SIZE_BASED_WRAP":
																												__snake_case	:    List[str]											=     "2000"
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Tuple											=     FullyShardedDataParallelPlugin()
																												fsdp_plugin.set_auto_wrap_policy(lowerCamelCase       )
																												if policy == "NO_WRAP":
																																			self.assertIsNone(fsdp_plugin.auto_wrap_policy       )
																												else:
																																			self.assertIsNotNone(fsdp_plugin.auto_wrap_policy       )

														__snake_case	:    str											=     self.dist_env.copy()
														__snake_case	:    str											=     "TRANSFORMER_BASED_WRAP"
														__snake_case	:    List[str]											=     "T5Layer"
														with mockenv_context(**lowerCamelCase       ):
																					__snake_case	:    Union[str, Any]											=     FullyShardedDataParallelPlugin()
																					with self.assertRaises(lowerCamelCase       ) as cm:
																												fsdp_plugin.set_auto_wrap_policy(lowerCamelCase       )
																					self.assertTrue("Could not find the transformer layer class to wrap in the model." in str(cm.exception       )       )

														__snake_case	:    Tuple											=     self.dist_env.copy()
														__snake_case	:    Optional[Any]											=     "SIZE_BASED_WRAP"
														__snake_case	:    str											=     "0"
														with mockenv_context(**lowerCamelCase       ):
																					__snake_case	:    List[str]											=     FullyShardedDataParallelPlugin()
																					fsdp_plugin.set_auto_wrap_policy(lowerCamelCase       )
																					self.assertIsNone(fsdp_plugin.auto_wrap_policy       )

							def 					__snake_case      (							self :						int       )			->					List[Any]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
														from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

														for mp_dtype in dtypes:
																					__snake_case	:    List[Any]											=     self.dist_env.copy()
																					__snake_case	:    Any											=     mp_dtype
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Any											=     Accelerator()
																												if mp_dtype == "fp16":
																																			__snake_case	:    Any											=     torch.floataa
																												elif mp_dtype == "bf16":
																																			__snake_case	:    str											=     torch.bfloataa
																												__snake_case	:    int											=     MixedPrecision(param_dtype=lowerCamelCase					,			reduce_dtype=lowerCamelCase					,			buffer_dtype=lowerCamelCase       )
																												self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy					,			lowerCamelCase       )
																												if mp_dtype == FPaa:
																																			self.assertTrue(isinstance(accelerator.scaler					,			lowerCamelCase       )       )
																												elif mp_dtype == BFaa:
																																			self.assertIsNone(accelerator.scaler       )
																												AcceleratorState._reset_state(lowerCamelCase       )






							def 					__snake_case      (							self :						Tuple       )			->					Any:
														from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

														for flag in [True, False]:
																					__snake_case	:    Any											=     self.dist_env.copy()
																					__snake_case	:    List[Any]											=     str(lowerCamelCase       ).lower()
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Dict											=     FullyShardedDataParallelPlugin()
																												self.assertEqual(fsdp_plugin.cpu_offload					,			CPUOffload(offload_params=lowerCamelCase       )       )

@require_fsdp
@require_multi_gpu
@slow
class        __magic_name__	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Optional[int]       )			->					Optional[Any]:
														super().setUp()
														__snake_case	:    Optional[int]											=     0.82
														__snake_case	:    str											=     [
														    "fsdp_shard_grad_op_transformer_based_wrap",
														    "fsdp_full_shard_transformer_based_wrap",
														]
														__snake_case	:    Union[str, Any]											=     {
														    "multi_gpu_fp16": 3200,
														    "fsdp_shard_grad_op_transformer_based_wrap_fp16": 2000,
														    "fsdp_full_shard_transformer_based_wrap_fp16": 1900,
														    # Disabling below test as it overwhelms the RAM memory usage
														    # on CI self-hosted runner leading to tests getting killed.
														    # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
														}
														__snake_case	:    Any											=     160
														__snake_case	:    Tuple											=     160

														__snake_case	:    List[Any]											=     inspect.getfile(accelerate.test_utils       )
														__snake_case	:    str											=     os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ["scripts", "external_deps"]       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					int:
														__snake_case	:    int											=     os.path.join(self.test_scripts_folder					,			"test_performance.py"       )
														__snake_case	:    Optional[int]											=     ["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
														for config in self.performance_configs:
																					__snake_case	:    Tuple											=     cmd.copy()
																					for i, strategy in enumerate(lowerCamelCase       ):
																												if strategy.lower() in config:
																																			cmd_config.append(F'--fsdp_sharding_strategy={i+1}'       )
																																			break

																					if "fp32" in config:
																												cmd_config.append("--mixed_precision=no"       )
																					else:
																												cmd_config.append("--mixed_precision=fp16"       )

																					if "cpu_offload" in config:
																												cmd_config.append("--fsdp_offload_params=True"       )

																					for policy in FSDP_AUTO_WRAP_POLICY:
																												if policy.lower() in config:
																																			cmd_config.append(F'--fsdp_auto_wrap_policy={policy}'       )
																																			break

																					if policy == "TRANSFORMER_BASED_WRAP":
																												cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer"       )
																					elif policy == "SIZE_BASED_WRAP":
																												cmd_config.append("--fsdp_min_num_params=2000"       )

																					cmd_config.extend(
																					    [
																					        self.test_file_path,
																					        F'--output_dir={self.tmpdir}',
																					        F'--performance_lower_bound={self.performance_lower_bound}',
																					    ]       )
																					with patch_environment(omp_num_threads=1       ):
																												execute_subprocess_async(lowerCamelCase					,			env=os.environ.copy()       )

							def 					__snake_case      (							self :						Dict       )			->					Dict:
														__snake_case	:    Tuple											=     os.path.join(self.test_scripts_folder					,			"test_checkpointing.py"       )
														__snake_case	:    Union[str, Any]											=     [
														    "accelerate",
														    "launch",
														    "--num_processes=2",
														    "--num_machines=1",
														    "--machine_rank=0",
														    "--use_fsdp",
														    "--mixed_precision=fp16",
														    "--fsdp_transformer_layer_cls_to_wrap=BertLayer",
														]

														for i, strategy in enumerate(lowerCamelCase       ):
																					__snake_case	:    List[str]											=     cmd.copy()
																					cmd_config.append(F'--fsdp_sharding_strategy={i+1}'       )
																					if strategy != "FULL_SHARD":
																												continue
																					__snake_case	:    Optional[int]											=     len(lowerCamelCase       )
																					for state_dict_type in FSDP_STATE_DICT_TYPE:
																												__snake_case	:    List[Any]											=     cmd_config[:state_dict_config_index]
																												cmd_config.append(F'--fsdp_state_dict_type={state_dict_type}'       )
																												cmd_config.extend(
																												    [
																												        self.test_file_path,
																												        F'--output_dir={self.tmpdir}',
																												        "--partial_train_epoch=1",
																												    ]       )
																												with patch_environment(omp_num_threads=1       ):
																																			execute_subprocess_async(lowerCamelCase					,			env=os.environ.copy()       )

																												__snake_case	:    Dict											=     cmd_config[:-1]
																												__snake_case	:    List[Any]											=     os.path.join(self.tmpdir					,			"epoch_0"       )
																												cmd_config.extend(
																												    [
																												        F'--resume_from_checkpoint={resume_from_checkpoint}',
																												    ]       )
																												with patch_environment(omp_num_threads=1       ):
																																			execute_subprocess_async(lowerCamelCase					,			env=os.environ.copy()       )






							def 					__snake_case      (							self :						Union[str, Any]       )			->					List[str]:
														__snake_case	:    List[str]											=     os.path.join(self.test_scripts_folder					,			"test_peak_memory_usage.py"       )
														__snake_case	:    Optional[Any]											=     [
														    "accelerate",
														    "launch",
														    "--num_processes=2",
														    "--num_machines=1",
														    "--machine_rank=0",
														]
														for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
																					__snake_case	:    Optional[int]											=     cmd.copy()
																					if "fp16" in spec:
																												cmd_config.extend(["--mixed_precision=fp16"]       )
																					else:
																												cmd_config.extend(["--mixed_precision=no"]       )

																					if "multi_gpu" in spec:
																												continue
																					else:
																												cmd_config.extend(["--use_fsdp"]       )
																												for i, strategy in enumerate(lowerCamelCase       ):
																																			if strategy.lower() in spec:
																																										cmd_config.append(F'--fsdp_sharding_strategy={i+1}'       )
																																										break

																												if "cpu_offload" in spec:
																																			cmd_config.append("--fsdp_offload_params=True"       )

																												for policy in FSDP_AUTO_WRAP_POLICY:
																																			if policy.lower() in spec:
																																										cmd_config.append(F'--fsdp_auto_wrap_policy={policy}'       )
																																										break

																												if policy == "TRANSFORMER_BASED_WRAP":
																																			cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer"       )
																												elif policy == "SIZE_BASED_WRAP":
																																			cmd_config.append("--fsdp_min_num_params=2000"       )

																					cmd_config.extend(
																					    [
																					        self.test_file_path,
																					        F'--output_dir={self.tmpdir}',
																					        F'--peak_memory_upper_bound={peak_mem_upper_bound}',
																					        F'--n_train={self.n_train}',
																					        F'--n_val={self.n_val}',
																					    ]       )
																					with patch_environment(omp_num_threads=1       ):
																												execute_subprocess_async(lowerCamelCase					,			env=os.environ.copy()       )





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[Any]  						=						StableDiffusionXLImgaImgPipeline
							__UpperCAmelCase					:							Any  						=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
							__UpperCAmelCase					:							Dict  						=						PipelineTesterMixin.required_optional_params - {"latents"}
							__UpperCAmelCase					:							Union[str, Any]  						=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							__UpperCAmelCase					:							Tuple  						=						IMAGE_TO_IMAGE_IMAGE_PARAMS
							__UpperCAmelCase					:							str  						=						IMAGE_TO_IMAGE_IMAGE_PARAMS

							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														torch.manual_seed(0       )
														__snake_case	:    Optional[int]											=     UNetaDConditionModel(
														    block_out_channels=(32, 64)					,			layers_per_block=2					,			sample_size=32					,			in_channels=4					,			out_channels=4					,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,			attention_head_dim=(2, 4)					,			use_linear_projection=lowerCamelCase					,			addition_embed_type="text_time"					,			addition_time_embed_dim=8					,			transformer_layers_per_block=(1, 2)					,			projection_class_embeddings_input_dim=80					,			cross_attention_dim=64					,			)
														__snake_case	:    Tuple											=     EulerDiscreteScheduler(
														    beta_start=0.0_00_85					,			beta_end=0.0_12					,			steps_offset=1					,			beta_schedule="scaled_linear"					,			timestep_spacing="leading"					,			)
														torch.manual_seed(0       )
														__snake_case	:    int											=     AutoencoderKL(
														    block_out_channels=[32, 64]					,			in_channels=3					,			out_channels=3					,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,			latent_channels=4					,			sample_size=128					,			)
														torch.manual_seed(0       )
														__snake_case	:    Optional[int]											=     CLIPTextConfig(
														    bos_token_id=0					,			eos_token_id=2					,			hidden_size=32					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1000					,			hidden_act="gelu"					,			projection_dim=32					,			)
														__snake_case	:    List[str]											=     CLIPTextModel(lowerCamelCase       )
														__snake_case	:    Any											=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					,			local_files_only=lowerCamelCase       )

														__snake_case	:    List[str]											=     CLIPTextModelWithProjection(lowerCamelCase       )
														__snake_case	:    List[Any]											=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					,			local_files_only=lowerCamelCase       )

														__snake_case	:    Optional[int]											=     {
														    "unet": unet,
														    "scheduler": scheduler,
														    "vae": vae,
														    "text_encoder": text_encoder,
														    "tokenizer": tokenizer,
														    "text_encoder_2": text_encoder_a,
														    "tokenizer_2": tokenizer_a,
														    # "safety_checker": None,
														    # "feature_extractor": None,
														}
														return components

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						List[Any]=0       )			->					Union[str, Any]:
														__snake_case	:    int											=     floats_tensor((1, 3, 32, 32)					,			rng=random.Random(lowerCamelCase       )       ).to(lowerCamelCase       )
														__snake_case	:    Any											=     image / 2 + 0.5
														if str(lowerCamelCase       ).startswith("mps"       ):
																					__snake_case	:    Dict											=     torch.manual_seed(lowerCamelCase       )
														else:
																					__snake_case	:    int											=     torch.Generator(device=lowerCamelCase       ).manual_seed(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     {
														    "prompt": "A painting of a squirrel eating a burger",
														    "image": image,
														    "generator": generator,
														    "num_inference_steps": 2,
														    "guidance_scale": 5.0,
														    "output_type": "numpy",
														    "strength": 0.75,
														}
														return inputs

							def 					__snake_case      (							self :						Dict       )			->					Any:
														__snake_case	:    Dict											=     "cpu"  # ensure determinism for the device-dependent torch.Generator
														__snake_case	:    Any											=     self.get_dummy_components()
														__snake_case	:    int											=     StableDiffusionXLImgaImgPipeline(**lowerCamelCase       )
														__snake_case	:    List[str]											=     sd_pipe.to(lowerCamelCase       )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    Tuple											=     self.get_dummy_inputs(lowerCamelCase       )
														__snake_case	:    Dict											=     sd_pipe(**lowerCamelCase       ).images
														__snake_case	:    Dict											=     image[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)

														__snake_case	:    str											=     np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]       )

														assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2

							def 					__snake_case      (							self :						str       )			->					Optional[Any]:
														super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )

							def 					__snake_case      (							self :						Any       )			->					Dict:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3       )

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






							def 					__snake_case      (							self :						Tuple       )			->					Union[str, Any]:
														__snake_case	:    str											=     self.get_dummy_components()
														__snake_case	:    List[Any]											=     StableDiffusionXLImgaImgPipeline(**lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     sd_pipe.to(lowerCamelCase       )
														__snake_case	:    int											=     sd_pipe.to(lowerCamelCase       )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														# forward without prompt embeds
														__snake_case	:    List[str]											=     self.get_dummy_inputs(lowerCamelCase       )
														__snake_case	:    str											=     3 * ["this is a negative prompt"]
														__snake_case	:    Any											=     negative_prompt
														__snake_case	:    Optional[Any]											=     3 * [inputs["prompt"]]

														__snake_case	:    int											=     sd_pipe(**lowerCamelCase       )
														__snake_case	:    List[Any]											=     output.images[0, -3:, -3:, -1]

														# forward with prompt embeds
														__snake_case	:    List[Any]											=     self.get_dummy_inputs(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     3 * ["this is a negative prompt"]
														__snake_case	:    int											=     3 * [inputs.pop("prompt"       )]

														(
														    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    (
														    __snake_case
														)				,    
														)	:    Dict											=     sd_pipe.encode_prompt(lowerCamelCase					,			negative_prompt=lowerCamelCase       )

														__snake_case	:    Tuple											=     sd_pipe(
														    **lowerCamelCase					,			prompt_embeds=lowerCamelCase					,			negative_prompt_embeds=lowerCamelCase					,			pooled_prompt_embeds=lowerCamelCase					,			negative_pooled_prompt_embeds=lowerCamelCase					,			)
														__snake_case	:    List[str]											=     output.images[0, -3:, -3:, -1]

														# make sure that it's equal
														assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4

@slow
@require_torch_gpu
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Optional[int]       )			->					Dict:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						Any					,			lowerCamelCase :						Optional[Any]="cpu"					,			lowerCamelCase :						str=torch.floataa					,			lowerCamelCase :						int=0       )			->					Dict:
														__snake_case	:    int											=     torch.Generator(device=lowerCamelCase       ).manual_seed(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     np.random.RandomState(lowerCamelCase       ).standard_normal((1, 4, 64, 64)       )
														__snake_case	:    Optional[Any]											=     torch.from_numpy(lowerCamelCase       ).to(device=lowerCamelCase					,			dtype=lowerCamelCase       )
														__snake_case	:    List[str]											=     {
														    "prompt": "a photograph of an astronaut riding a horse",
														    "latents": latents,
														    "generator": generator,
														    "num_inference_steps": 3,
														    "guidance_scale": 7.5,
														    "output_type": "numpy",
														}
														return inputs






							def 					__snake_case      (							self :						str       )			->					Any:
														__snake_case	:    List[str]											=     DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"       )
														pipe.to(lowerCamelCase       )
														pipe.set_progress_bar_config(disable=lowerCamelCase       )

														__snake_case	:    int											=     self.get_inputs(lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     pipe(**lowerCamelCase       ).images
														__snake_case	:    Any											=     image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 512, 512, 3)
														__snake_case	:    Optional[int]											=     np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]       )
														assert np.abs(image_slice - expected_slice       ).max() < 7E-3

