






"""simple docstring"""

import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
					from tests.trainer.test_trainer import (  # noqa
					    RegressionModelConfig,
					    RegressionPreTrainedModel,
					    get_regression_trainer,
					)


set_seed(42)

lowercase__      =				get_tests_dir("""fixtures""")
lowercase__      =				os.path.join(dirname(get_tests_dir()))
lowercase__      =				dirname(os.path.abspath(__file__))

# default torch.distributed port
lowercase__      =				"""10999"""

lowercase__      =				"""t5-small"""

# *** Working Models ***
lowercase__      =				"""hf-internal-testing/tiny-albert"""
lowercase__      =				"""sshleifer/bart-tiny-random"""
lowercase__      =				"""hf-internal-testing/tiny-bert"""
lowercase__      =				"""hf-internal-testing/tiny-random-bigbird_pegasus"""
lowercase__      =				"""hf-internal-testing/tiny-random-big_bird"""
lowercase__      =				"""hf-internal-testing/tiny-random-blenderbot"""
lowercase__      =				"""bigscience/bigscience-small-testing"""
lowercase__      =				"""hf-internal-testing/tiny-random-deberta"""
lowercase__      =				"""hf-internal-testing/tiny-random-deberta-v2"""
lowercase__      =				"""sshleifer/tiny-distilbert-base-cased"""
lowercase__      =				"""hf-internal-testing/tiny-electra"""
lowercase__      =				"""hf-internal-testing/tiny-random-flaubert"""
lowercase__      =				"""stas/tiny-wmt19-en-de"""
lowercase__      =				"""hf-internal-testing/tiny-random-funnel"""
lowercase__      =				"""sshleifer/tiny-gpt2"""
lowercase__      =				"""hf-internal-testing/tiny-random-gptj"""
lowercase__      =				"""hf-internal-testing/tiny-random-gpt_neo"""
lowercase__      =				"""hf-internal-testing/tiny-layoutlm"""
lowercase__      =				"""hf-internal-testing/tiny-random-led"""
lowercase__      =				"""hf-internal-testing/tiny-random-longformer"""
lowercase__      =				"""stas/tiny-m2m_100"""  # hf tiny model is unsuitable
lowercase__      =				"""sshleifer/tiny-marian-en-de"""
lowercase__      =				"""sshleifer/tiny-mbart"""
lowercase__      =				"""hf-internal-testing/tiny-random-mobilebert"""
lowercase__      =				"""hf-internal-testing/tiny-random-mpnet"""
lowercase__      =				"""stas/pegasus-cnn_dailymail-tiny-random"""
lowercase__      =				"""hf-internal-testing/tiny-random-prophetnet"""
lowercase__      =				"""sshleifer/tiny-distilroberta-base"""
lowercase__      =				"""hf-internal-testing/tiny-random-squeezebert"""
lowercase__      =				"""patrickvonplaten/t5-tiny-random"""
lowercase__      =				"""hf-internal-testing/tiny-random-t5-v1.1"""
lowercase__      =				"""hf-internal-testing/tiny-random-vit"""
lowercase__      =				"""hf-internal-testing/tiny-xlm-roberta"""
lowercase__      =				"""sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowercase__      =				"""hf-internal-testing/tiny-random-mt5"""
lowercase__      =				"""hf-internal-testing/tiny-random-camembert"""
lowercase__      =				"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
lowercase__      =				"""hf-internal-testing/tiny-random-convbert"""
lowercase__      =				"""hf-internal-testing/tiny-random-layoutlmv2"""
lowercase__      =				"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
lowercase__      =				"""hf-internal-testing/tiny-random-ctrl"""
lowercase__      =				"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
lowercase__      =				"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
lowercase__      =				"""hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
lowercase__      =				"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowercase__      =				"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
lowercase__      =				""""""
# - "luke" Entities classes - no plan to make such example
lowercase__      =				"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
lowercase__      =				"""hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowercase__      =				"""hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
lowercase__      =				"""hf-internal-testing/tiny-random-tapas"""

def 	__lowerCamelCase			(						__UpperCamelCase=False )		->					str:

		"""simple docstring"""







		lowerCAmelCase_     :							List[str]             =    min(2   , get_gpu_count() ) if distributed else 1
		lowerCAmelCase_     :							Tuple             =    os.environ.get("DS_TEST_PORT"   , __UpperCamelCase )
		return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()

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

		"""simple docstring"""







		lowerCAmelCase_     :							Tuple             =    f'''{FIXTURE_DIRECTORY}/tests_samples'''
		lowerCAmelCase_     :							List[Any]             =    f'''{data_dir_samples}/wmt_en_ro'''
		lowerCAmelCase_     :							Optional[int]             =    f'''{data_dir_samples}/xsum'''
		lowerCAmelCase_     :							Any             =    "\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".split()

		# try to cover as many models as possible once (it's enough to run on one task per model)
		# but need a tiny model for each
		#
		# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
		lowerCAmelCase_     :							Any             =    {
		    "trans": [
		        "bart",
		        "fsmt",
		        "m2m_100",
		        "marian",
		        "mbart",
		        "t5",
		        "t5_v1",
		        # "mt5", missing model files
		    ],
		    "sum": [
		        "pegasus",
		    ],
		    "clm": [
		        "big_bird",
		        "bigbird_pegasus",
		        "blenderbot",
		        "bloom",
		        "gpt2",
		        "gpt_neo",
		        "gptj",
		        "xlm-roberta",
		        "prophetnet",
		        # "camembert", missing model files
		    ],
		    "mlm": [
		        "albert",
		        "deberta",
		        "deberta-v2",
		        "distilbert",
		        "electra",
		        "flaubert",
		        "funnel",
		        "layoutlm",
		        # "reformer", # multiple issues with either mlm/qa/clas
		    ],
		    "qa": [
		        "led",
		        "longformer",
		        "mobilebert",
		        "mpnet",
		        "roberta",
		        "squeezebert",
		        # "convbert", # missing tokenizer files
		        # "layoutlmv2", missing model files
		    ],
		    "clas": [
		        "bert",
		        "xlnet",
		        # "hubert", # missing tokenizer files
		        # "ibert", # multiple issues with either mlm/qa/clas
		        # "transfo-xl", # tokenizer issues as ctrl
		        # "ctrl", # tokenizer issues
		        # "openai-gpt", missing model files
		        # "tapas", multiple issues
		    ],
		    "img_clas": [
		        "vit",
		    ],
		}

		lowerCAmelCase_     :							Optional[int]             =    f'''{ROOT_DIRECTORY}/examples/pytorch'''

		lowerCAmelCase_     :							List[Any]             =    {
		    "trans": f'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
		    "sum": f'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
		    "clm": f'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
		    "mlm": f'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
		    "qa": f'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
		    "clas": f'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
		    "img_clas": f'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
		}

		lowerCAmelCase_     :							List[Any]             =    get_launcher(distributed=__UpperCamelCase )

		lowerCAmelCase_     :							Dict             =    {}
		for task, args in tasks.items():
				lowerCAmelCase_     :							Any             =    args.split()
				for model in tasksamodels[task]:
						lowerCAmelCase_     :							List[str]             =    globals()[f'''{model.upper().replace("-"   , "_" )}_TINY''']
						lowerCAmelCase_     :							Any             =    f'''--model_name_or_path {model_name}'''.split()
						lowerCAmelCase_     :							Tuple             =    launcher + args + args_model + args_main

						# # generation special case
						# if task == "gen":
						#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
						#     args_model += f"--model_type {model}".split()
						#     cmds[f"{task}_{model}"] = launcher + args + args_model
						# else:

		return cmds


lowercase__      =				make_task_cmds()

lowercase__      =				"""zero2"""
lowercase__      =				"""zero3"""

lowercase__      =				[ZEROa, ZEROa]

def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					List[str]:

		"""simple docstring"""







		lowerCAmelCase_     :							Optional[Any]             =    parameterized.to_safe_name("_".join(str(__UpperCamelCase ) for x in param.args ) )
		return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowercase__      =				list(itertools.product(stages, task_cmds.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			List[Any]		,    a_							:			Tuple			):
				# return a ready to run train cmd
				if task not in task_cmds:
						raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''			)

				lowerCAmelCase_     :							Any             =    task_cmds[task]
				lowerCAmelCase_     :							Optional[Any]             =    f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

				lowerCAmelCase_     :							Dict             =    self.get_auto_remove_tmp_dir()
				lowerCAmelCase_     :							str             =    f'''--output_dir {output_dir}'''.split()

				cmd += args_ds + args_out

				return cmd, output_dir





		@parameterized.expand(a_		,    name_func=a_			)
		def      lowerCamelCase (     self							:			int		,    a_							:			str		,    a_							:			Tuple			):
				# testing the ability to do a run followed by recovery of full fp32 weights

				lowerCAmelCase_       ,			lowerCAmelCase_     :							Union[str, Any]             =    self.get_task_cmd(a_		,    a_			)

				# 1. generate the checkpoint
				cmd += "--save_steps 1".split()
				# keep for quick debug
				# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
				execute_subprocess_async(a_		,    env=self.get_env()			)

				# 2. test that the fp32 weights get reconsolidated
				lowerCAmelCase_     :							List[str]             =    f'''{output_dir}/checkpoint-1'''
				lowerCAmelCase_     :							Optional[int]             =    f'''{chkpt_dir}/out.bin'''
				lowerCAmelCase_     :							Dict             =    f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
				# keep for quick debug
				# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
				subprocess.check_call(a_		,    shell=a_			)
				assert os.path.exists(a_			), f'''{recovered_model_path} was not found'''

				# possibly could also test that the resulting saved model is usable but given that we use
				# random models we won't know if it's any good








"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 	__lowerCamelCase       (				A__				,							A__				,							A__				,							unittest.TestCase    ):





		'''simple docstring'''






		a_			:       Union[str, Any]      =							StableDiffusionImageVariationPipeline
		a_			:       Tuple      =							IMAGE_VARIATION_PARAMS
		a_			:       Optional[Any]      =							IMAGE_VARIATION_BATCH_PARAMS
		a_			:       int      =							frozenset([]    )
		# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		a_			:       Optional[int]      =							frozenset([]    )





		def      lowerCamelCase (     self							:			Tuple			):
				torch.manual_seed(0			)
				lowerCAmelCase_     :							List[Any]             =    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")		,    cross_attention_dim=32		,    )
				lowerCAmelCase_     :							int             =    PNDMScheduler(skip_prk_steps=a_			)
				torch.manual_seed(0			)
				lowerCAmelCase_     :							List[Any]             =    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		,    )
				torch.manual_seed(0			)
				lowerCAmelCase_     :							Dict             =    CLIPVisionConfig(
				    hidden_size=32		,    projection_dim=32		,    intermediate_size=37		,    layer_norm_eps=1e-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    image_size=32		,    patch_size=4		,    )
				lowerCAmelCase_     :							List[Any]             =    CLIPVisionModelWithProjection(a_			)
				lowerCAmelCase_     :							Union[str, Any]             =    CLIPImageProcessor(crop_size=32		,    size=32			)

				lowerCAmelCase_     :							List[Any]             =    {
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "image_encoder": image_encoder,
				    "feature_extractor": feature_extractor,
				    "safety_checker": None,
				}
				return components





		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Tuple		,    a_							:			str=0			):
				lowerCAmelCase_     :							List[Any]             =    floats_tensor((1, 3, 32, 32)		,    rng=random.Random(a_			)			)
				lowerCAmelCase_     :							int             =    image.cpu().permute(0		,    2		,    3		,    1			)[0]
				lowerCAmelCase_     :							List[str]             =    Image.fromarray(np.uinta(a_			)			).convert("RGB"			).resize((32, 32)			)
				if str(a_			).startswith("mps"			):
						lowerCAmelCase_     :							Optional[int]             =    torch.manual_seed(a_			)
				else:
						lowerCAmelCase_     :							Optional[Any]             =    torch.Generator(device=a_			).manual_seed(a_			)
				lowerCAmelCase_     :							str             =    {
				    "image": image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs





		def      lowerCamelCase (     self							:			Any			):
				lowerCAmelCase_     :							int             =    "cpu"  # ensure determinism for the device-dependent torch.Generator
				lowerCAmelCase_     :							Dict             =    self.get_dummy_components()
				lowerCAmelCase_     :							Union[str, Any]             =    StableDiffusionImageVariationPipeline(**a_			)
				lowerCAmelCase_     :							int             =    sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							Tuple             =    self.get_dummy_inputs(a_			)
				lowerCAmelCase_     :							List[str]             =    sd_pipe(**a_			).images
				lowerCAmelCase_     :							List[Any]             =    image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				lowerCAmelCase_     :							List[str]             =    np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3





		def      lowerCamelCase (     self							:			Union[str, Any]			):
				lowerCAmelCase_     :							int             =    "cpu"  # ensure determinism for the device-dependent torch.Generator
				lowerCAmelCase_     :							Optional[Any]             =    self.get_dummy_components()
				lowerCAmelCase_     :							Tuple             =    StableDiffusionImageVariationPipeline(**a_			)
				lowerCAmelCase_     :							List[str]             =    sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							List[str]             =    self.get_dummy_inputs(a_			)
				lowerCAmelCase_     :							Optional[Any]             =    2 * [inputs["image"]]
				lowerCAmelCase_     :							Optional[Any]             =    sd_pipe(**a_			)

				lowerCAmelCase_     :							Union[str, Any]             =    output.images

				lowerCAmelCase_     :							Optional[Any]             =    image[-1, -3:, -3:, -1]

				assert image.shape == (2, 64, 64, 3)
				lowerCAmelCase_     :							int             =    np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3





		def      lowerCamelCase (     self							:			str			):
				super().test_inference_batch_single_identical(expected_max_diff=3e-3			)







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





		'''simple docstring'''






		def      lowerCamelCase (     self							:			int			):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			Optional[Any]		,    a_							:			List[str]="cpu"		,    a_							:			Optional[Any]=torch.floataa		,    a_							:			Optional[int]=0			):
				lowerCAmelCase_     :							str             =    torch.Generator(device=a_			).manual_seed(a_			)
				lowerCAmelCase_     :							str             =    load_image(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
				    "/stable_diffusion_imgvar/input_image_vermeer.png"			)
				lowerCAmelCase_     :							Optional[Any]             =    np.random.RandomState(a_			).standard_normal((1, 4, 64, 64)			)
				lowerCAmelCase_     :							List[Any]             =    torch.from_numpy(a_			).to(device=a_		,    dtype=a_			)
				lowerCAmelCase_     :							Any             =    {
				    "image": init_image,
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 3,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				}
				return inputs





		def      lowerCamelCase (     self							:			Any			):
				lowerCAmelCase_     :							Tuple             =    StableDiffusionImageVariationPipeline.from_pretrained(
				    "lambdalabs/sd-image-variations-diffusers"		,    safety_checker=a_			)
				lowerCAmelCase_     :							int             =    sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							Union[str, Any]             =    self.get_inputs(a_			)
				lowerCAmelCase_     :							Union[str, Any]             =    sd_pipe(**a_			).images
				lowerCAmelCase_     :							Optional[int]             =    image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 5_12, 3)
				lowerCAmelCase_     :							Optional[Any]             =    np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]			)
				assert np.abs(image_slice - expected_slice			).max() < 6e-3





		def      lowerCamelCase (     self							:			Dict			):
				lowerCAmelCase_     :							List[str]             =    0

				def callback_fn(a_							:			int		,    a_							:			int		,    a_							:			torch.FloatTensor			) -> None:
						lowerCAmelCase_     :							str             =    True
						nonlocal number_of_steps
						number_of_steps += 1
						if step == 1:
								lowerCAmelCase_     :							str             =    latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 64, 64)
								lowerCAmelCase_     :							Dict             =    latents[0, -3:, -3:, -1]
								lowerCAmelCase_     :							List[Any]             =    np.array(
								    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]			)

								assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
						elif step == 2:
								lowerCAmelCase_     :							Dict             =    latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 64, 64)
								lowerCAmelCase_     :							Tuple             =    latents[0, -3:, -3:, -1]
								lowerCAmelCase_     :							Dict             =    np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]			)

								assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2

				lowerCAmelCase_     :							Union[str, Any]             =    False

				lowerCAmelCase_     :							str             =    StableDiffusionImageVariationPipeline.from_pretrained(
				    "fusing/sd-image-variations-diffusers"		,    safety_checker=a_		,    torch_dtype=torch.floataa		,    )
				pipe.to(a_			)
				pipe.set_progress_bar_config(disable=a_			)
				pipe.enable_attention_slicing()

				lowerCAmelCase_     :							Tuple             =    self.get_inputs(a_		,    dtype=torch.floataa			)
				pipe(**a_		,    callback=a_		,    callback_steps=1			)
				assert callback_fn.has_been_called
				assert number_of_steps == inputs["num_inference_steps"]





		def      lowerCamelCase (     self							:			Optional[Any]			):
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				lowerCAmelCase_     :							Tuple             =    "fusing/sd-image-variations-diffusers"
				lowerCAmelCase_     :							str             =    StableDiffusionImageVariationPipeline.from_pretrained(
				    a_		,    safety_checker=a_		,    torch_dtype=torch.floataa			)
				lowerCAmelCase_     :							int             =    pipe.to(a_			)
				pipe.set_progress_bar_config(disable=a_			)
				pipe.enable_attention_slicing(1			)
				pipe.enable_sequential_cpu_offload()

				lowerCAmelCase_     :							List[str]             =    self.get_inputs(a_		,    dtype=torch.floataa			)
				lowerCAmelCase_     :							Any             =    pipe(**a_			)

				lowerCAmelCase_     :							List[Any]             =    torch.cuda.max_memory_allocated()
				# make sure that less than 2.6 GB is allocated
				assert mem_bytes < 2.6 * 10**9







@nightly
@require_torch_gpu
class 	__lowerCamelCase       (				unittest.TestCase    ):





		'''simple docstring'''






		def      lowerCamelCase (     self							:			Tuple			):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			int		,    a_							:			int="cpu"		,    a_							:			str=torch.floataa		,    a_							:			Optional[Any]=0			):
				lowerCAmelCase_     :							str             =    torch.Generator(device=a_			).manual_seed(a_			)
				lowerCAmelCase_     :							int             =    load_image(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
				    "/stable_diffusion_imgvar/input_image_vermeer.png"			)
				lowerCAmelCase_     :							Optional[Any]             =    np.random.RandomState(a_			).standard_normal((1, 4, 64, 64)			)
				lowerCAmelCase_     :							Optional[int]             =    torch.from_numpy(a_			).to(device=a_		,    dtype=a_			)
				lowerCAmelCase_     :							Union[str, Any]             =    {
				    "image": init_image,
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 50,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				}
				return inputs





		def      lowerCamelCase (     self							:			Optional[int]			):
				lowerCAmelCase_     :							Any             =    StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"			)
				sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							List[str]             =    self.get_inputs(a_			)
				lowerCAmelCase_     :							int             =    sd_pipe(**a_			).images[0]

				lowerCAmelCase_     :							Optional[Any]             =    load_numpy(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
				    "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"			)
				lowerCAmelCase_     :							List[Any]             =    np.abs(expected_image - image			).max()
				assert max_diff < 1e-3





		def      lowerCamelCase (     self							:			Any			):
				lowerCAmelCase_     :							List[Any]             =    StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"			)
				lowerCAmelCase_     :							Tuple             =    DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config			)
				sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							Optional[int]             =    self.get_inputs(a_			)
				lowerCAmelCase_     :							Tuple             =    25
				lowerCAmelCase_     :							int             =    sd_pipe(**a_			).images[0]

				lowerCAmelCase_     :							Union[str, Any]             =    load_numpy(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
				    "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"			)
				lowerCAmelCase_     :							Optional[Any]             =    np.abs(expected_image - image			).max()
				assert max_diff < 1e-3
