





'''simple docstring'''






import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
	import tensorflow as tf
	from tensorflow.python.framework.errors_impl import ResourceExhaustedError

	from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
	import pyanvml.pyanvml as nvml

__UpperCamelCase					    =	logging.get_logger(__name__)







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



			"""simple docstring"""


			def run_func(_lowerCamelCase ):
						@wraps(_lowerCamelCase )
						def run_in_eager_mode(*_lowerCamelCase    ,	**_lowerCamelCase ):
									return func(*_lowerCamelCase    ,	**_lowerCamelCase )

						@wraps(_lowerCamelCase )
						@tf.function(experimental_compile=_lowerCamelCase )
						def run_in_graph_mode(*_lowerCamelCase    ,	**_lowerCamelCase ):
									return func(*_lowerCamelCase    ,	**_lowerCamelCase )

						if do_eager_mode is True:
									if use_xla is not False:
												raise ValueError(
												    """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
									return run_in_eager_mode
						else:
									return run_in_graph_mode

			return run_func







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				["tf.Tensor"]:



			"""simple docstring"""


			__snake_case					:      Dict							      =     random.Random()
			__snake_case					:      str							      =     [rng.randint(0    ,	vocab_size - 1 ) for i in range(batch_size * sequence_length )]
			return tf.constant(_lowerCamelCase    ,	shape=(batch_size, sequence_length)    ,	dtype=tf.intaa )




class  _A (					__lowercase		):
		lowercase__:   TensorFlowBenchmarkArguments
		lowercase__:   PretrainedConfig
		lowercase__:   str				     =   "TensorFlow"

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


					"""simple docstring"""

					return tf.__version__

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    float:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     self.args.strategy
					if strategy is None:
								raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)
					__snake_case					:      Any							      =     self._prepare_inference_func(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
					return self._measure_speed(_inference			)

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    float:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     self.args.strategy
					if strategy is None:
								raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)
					__snake_case					:      List[Any]							      =     self._prepare_train_func(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
					return self._measure_speed(_train			)

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    [Memory, Optional[MemorySummary]]:


					"""simple docstring"""

					if self.args.is_gpu:
								tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]  ,      __magic_name__			)
					__snake_case					:      List[Any]							      =     self.args.strategy
					if strategy is None:
								raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)
					__snake_case					:      Optional[Any]							      =     self._prepare_inference_func(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
					return self._measure_memory(_inference			)

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    [Memory, Optional[MemorySummary]]:


					"""simple docstring"""

					if self.args.is_gpu:
								tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]  ,      __magic_name__			)
					__snake_case					:      Tuple							      =     self.args.strategy
					if strategy is None:
								raise ValueError("""A device strategy has to be initialized before using TensorFlow."""			)

					__snake_case					:      List[str]							      =     self._prepare_train_func(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
					return self._measure_memory(_train			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    Callable[[], None]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.config_dict[model_name]

					if self.args.fpaa:
								raise NotImplementedError("""Mixed precision is currently not supported."""			)

					__snake_case					:      str							      =     (
					    hasattr(__magic_name__  ,      """architectures"""			)
					    and isinstance(config.architectures  ,      __magic_name__			)
					    and len(config.architectures			) > 0
					)
					if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
											__snake_case					:      List[Any]							      =     """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
											__snake_case					:      Any							      =     __import__("""transformers"""  ,      fromlist=[model_class]			)
											__snake_case					:      Union[str, Any]							      =     getattr(__magic_name__  ,      __magic_name__			)
											__snake_case					:      Dict							      =     model_cls(__magic_name__			)
								except ImportError:
											raise ImportError(
											    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
											    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""			)
					else:
								__snake_case					:      int							      =     TF_MODEL_MAPPING[config.__class__](__magic_name__			)

					# encoder-decoder has vocab size saved differently
					__snake_case					:      Optional[int]							      =     config.vocab_size if hasattr(__magic_name__  ,      """vocab_size"""			) else config.encoder.vocab_size
					__snake_case					:      str							      =     random_input_ids(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

					@run_with_tf_optimizations(self.args.eager_mode  ,      self.args.use_xla			)
					def encoder_decoder_forward():
								return model(__magic_name__  ,      decoder_input_ids=__magic_name__  ,      training=__magic_name__			)

					@run_with_tf_optimizations(self.args.eager_mode  ,      self.args.use_xla			)
					def encoder_forward():
								return model(__magic_name__  ,      training=__magic_name__			)

					__snake_case					:      Any							      =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

					return _inference

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    Callable[[], None]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     self.config_dict[model_name]

					if self.args.eager_mode is not False:
								raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."""			)

					if self.args.fpaa:
								raise NotImplementedError("""Mixed precision is currently not supported."""			)

					__snake_case					:      Optional[int]							      =     (
					    hasattr(__magic_name__  ,      """architectures"""			)
					    and isinstance(config.architectures  ,      __magic_name__			)
					    and len(config.architectures			) > 0
					)
					if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
											__snake_case					:      Optional[int]							      =     """TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
											__snake_case					:      Dict							      =     __import__("""transformers"""  ,      fromlist=[model_class]			)
											__snake_case					:      List[Any]							      =     getattr(__magic_name__  ,      __magic_name__			)
											__snake_case					:      Tuple							      =     model_cls(__magic_name__			)
								except ImportError:
											raise ImportError(
											    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
											    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""			)
					else:
								__snake_case					:      Union[str, Any]							      =     TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__magic_name__			)

					# encoder-decoder has vocab size saved differently
					__snake_case					:      Optional[Any]							      =     config.vocab_size if hasattr(__magic_name__  ,      """vocab_size"""			) else config.encoder.vocab_size
					__snake_case					:      List[str]							      =     random_input_ids(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

					@run_with_tf_optimizations(self.args.eager_mode  ,      self.args.use_xla			)
					def encoder_decoder_train():
								__snake_case					:      Tuple							      =     model(__magic_name__  ,      decoder_input_ids=__magic_name__  ,      labels=__magic_name__  ,      training=__magic_name__			)[0]
								__snake_case					:      Dict							      =     tf.gradients(__magic_name__  ,      model.trainable_variables			)
								return gradients

					@run_with_tf_optimizations(self.args.eager_mode  ,      self.args.use_xla			)
					def encoder_train():
								__snake_case					:      Optional[Any]							      =     model(__magic_name__  ,      labels=__magic_name__  ,      training=__magic_name__			)[0]
								__snake_case					:      Optional[int]							      =     tf.gradients(__magic_name__  ,      model.trainable_variables			)
								return gradients

					__snake_case					:      str							      =     encoder_decoder_train if config.is_encoder_decoder else encoder_train

					return _train

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Tuple			)					->    float:


					"""simple docstring"""

					with self.args.strategy.scope():
								try:
											if self.args.is_tpu or self.args.use_xla:
														# run additional 10 times to stabilize compilation for tpu
														logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation"""			)
														timeit.repeat(__magic_name__  ,      repeat=1  ,      number=5			)

											# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
											__snake_case					:      Optional[int]							      =     timeit.repeat(
											    __magic_name__  ,      repeat=self.args.repeat  ,      number=10  ,      )

											return min(__magic_name__			) / 10.0
								except ResourceExhaustedError as e:
											self.print_fn(f'''Doesn\'t fit on GPU. {e}'''			)

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   Callable[[], None]			)					->    [Memory, MemorySummary]:


					"""simple docstring"""

					logger.info(
					    """Note that TensorFlow allocates more memory than """
					    """it might need to speed up computation. """
					    """The memory reported here corresponds to the memory """
					    """reported by `nvidia-smi`, which can vary depending """
					    """on total available memory on the GPU that is used."""			)
					with self.args.strategy.scope():
								try:
											if self.args.trace_memory_line_by_line:
														if not self.args.eager_mode:
																	raise ValueError(
																	    """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
																	    """ consumption line by line."""			)
														__snake_case					:      Dict							      =     start_memory_tracing("""transformers"""			)

											if self.args.is_tpu:
														# tpu
														raise NotImplementedError(
														    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
														    """ with `args.memory=False`"""			)
											elif self.args.is_gpu:
														# gpu
														if not is_pyanvml_available():
																	logger.warning(
																	    """py3nvml not installed, we won't log GPU memory usage. """
																	    """Install py3nvml (pip install py3nvml) to log information about GPU."""			)
																	__snake_case					:      int							      =     """N/A"""
														else:
																	logger.info(
																	    """Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
																	    """ running on the same GPU."""			)
																	# init nvml
																	nvml.nvmlInit()
																	func()
																	__snake_case					:      Any							      =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
																	__snake_case					:      Union[str, Any]							      =     nvml.nvmlDeviceGetMemoryInfo(__magic_name__			)
																	__snake_case					:      Union[str, Any]							      =     meminfo.used
																	__snake_case					:      List[Any]							      =     Memory(__magic_name__			)
																	# shutdown nvml
																	nvml.nvmlShutdown()
											else:
														# cpu
														if self.args.trace_memory_line_by_line:
																	logger.info(
																	    """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
																	    """ TensorFlow."""			)
																	__snake_case					:      int							      =     None
														else:
																	__snake_case					:      Dict							      =     measure_peak_memory_cpu(__magic_name__			)
																	__snake_case					:      Union[str, Any]							      =     Memory(__magic_name__			) if isinstance(__magic_name__  ,      __magic_name__			) else memory_bytes
											if self.args.trace_memory_line_by_line:
														__snake_case					:      Tuple							      =     stop_memory_tracing(__magic_name__			)
														if memory is None:
																	__snake_case					:      Any							      =     summary.total
											else:
														__snake_case					:      Any							      =     None

											return memory, summary
								except ResourceExhaustedError as e:
											self.print_fn(f'''Doesn\'t fit on GPU. {e}'''			)
											return "N/A", None



'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							a__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			KandinskyImgaImgPipeline
			UpperCAmelCase_     :	Union[str, Any]						=			['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
			UpperCAmelCase_     :	List[str]						=			[
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			]
			UpperCAmelCase_     :	Dict						=			[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''strength''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCAmelCase_     :	str						=			False



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim * 4



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 100



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
					return tokenizer



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    MCLIPConfig(
					    numDims=self.cross_attention_dim			,       transformerDimensions=self.text_embedder_hidden_size			,       hidden_size=self.text_embedder_hidden_size			,       intermediate_size=37			,       num_attention_heads=4			,       num_hidden_layers=5			,       vocab_size=1005			,       )

					lowerCAmelCase     =    MultilingualCLIP(__lowerCAmelCase)
					lowerCAmelCase     =    text_encoder.eval()

					return text_encoder



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase     =    {
					    """in_channels""": 4,
					    # Out channels is double in channels because predicts mean and variance
					    """out_channels""": 8,
					    """addition_embed_type""": """text_image""",
					    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
					    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
					    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
					    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    """layers_per_block""": 1,
					    """encoder_hid_dim""": self.text_embedder_hidden_size,
					    """encoder_hid_dim_type""": """text_image_proj""",
					    """cross_attention_dim""": self.cross_attention_dim,
					    """attention_head_dim""": 4,
					    """resnet_time_scale_shift""": """scale_shift""",
					    """class_embed_type""": None,
					}

					lowerCAmelCase     =    UNetaDConditionModel(**__lowerCAmelCase)
					return model



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return {
					    "block_out_channels": [32, 64],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    VQModel(**self.dummy_movq_kwargs)
					return model



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.dummy_text_encoder
					lowerCAmelCase     =    self.dummy_tokenizer
					lowerCAmelCase     =    self.dummy_unet
					lowerCAmelCase     =    self.dummy_movq

					lowerCAmelCase     =    {
					    """num_train_timesteps""": 1000,
					    """beta_schedule""": """linear""",
					    """beta_start""": 0.00085,
					    """beta_end""": 0.012,
					    """clip_sample""": False,
					    """set_alpha_to_one""": False,
					    """steps_offset""": 0,
					    """prediction_type""": """epsilon""",
					    """thresholding""": False,
					}

					lowerCAmelCase     =    DDIMScheduler(**__lowerCAmelCase)

					lowerCAmelCase     =    {
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=0):




					"""simple docstring"""


					lowerCAmelCase     =    floats_tensor((1, self.cross_attention_dim)			,       rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase     =    floats_tensor((1, self.cross_attention_dim)			,       rng=random.Random(seed + 1)).to(__lowerCAmelCase)
					# create init_image
					lowerCAmelCase     =    floats_tensor((1, 3, 64, 64)			,       rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase     =    image.cpu().permute(0			,       2			,       3			,       1)[0]
					lowerCAmelCase     =    Image.fromarray(np.uinta(__lowerCAmelCase)).convert("""RGB""").resize((256, 256))

					if str(__lowerCAmelCase).startswith("""mps"""):
							lowerCAmelCase     =    torch.manual_seed(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
					lowerCAmelCase     =    {
					    """prompt""": """horse""",
					    """image""": init_image,
					    """image_embeds""": image_embeds,
					    """negative_image_embeds""": negative_image_embeds,
					    """generator""": generator,
					    """height""": 64,
					    """width""": 64,
					    """num_inference_steps""": 10,
					    """guidance_scale""": 7.0,
					    """strength""": 0.2,
					    """output_type""": """np""",
					}
					return inputs







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """cpu"""

					lowerCAmelCase     =    self.get_dummy_components()

					lowerCAmelCase     =    self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase     =    pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    pipe(**self.get_dummy_inputs(__lowerCAmelCase))
					lowerCAmelCase     =    output.images

					lowerCAmelCase     =    pipe(
					    **self.get_dummy_inputs(__lowerCAmelCase)			,       return_dict=__lowerCAmelCase			,       )[0]

					lowerCAmelCase     =    image[0, -3:, -3:, -1]
					lowerCAmelCase     =    image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)

					lowerCAmelCase     =    np.array(
					    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
					assert (
					    np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
					), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







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


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


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







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinsky/kandinsky_img2img_frog.npy""")

					lowerCAmelCase     =    load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
					lowerCAmelCase     =    """A red cartoon frog, 4k"""

					lowerCAmelCase     =    KandinskyPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-prior"""			,       torch_dtype=torch.floataa)
					pipe_prior.to(__lowerCAmelCase)

					lowerCAmelCase     =    KandinskyImgaImgPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1"""			,       torch_dtype=torch.floataa)
					lowerCAmelCase     =    pipeline.to(__lowerCAmelCase)

					pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    torch.Generator(device="""cpu""").manual_seed(0)
					lowerCAmelCase   ,	lowerCAmelCase     =    pipe_prior(
					    __lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=5			,       negative_prompt=""""""			,       ).to_tuple()

					lowerCAmelCase     =    pipeline(
					    __lowerCAmelCase			,       image=__lowerCAmelCase			,       image_embeds=__lowerCAmelCase			,       negative_image_embeds=__lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=100			,       height=768			,       width=768			,       strength=0.2			,       output_type="""np"""			,       )

					lowerCAmelCase     =    output.images[0]

					assert image.shape == (768, 768, 3)

					assert_mean_pixel_difference(__lowerCAmelCase			,       __lowerCAmelCase)




