


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A		:				int									=       logging.get_logger(__name__)

_A		:				Any									=       {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  List[str]						      =	"pix2struct_text_model"
     UpperCAmelCase__ :  int						      =	["past_key_values"]
     UpperCAmelCase__ :  Optional[Any]						      =	{
         "hidden_size": "hidden_size",
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=50244    ,	SCREAMING_SNAKE_CASE_					:				Tuple=768    ,	SCREAMING_SNAKE_CASE_					:				Dict=64    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=2048    ,	SCREAMING_SNAKE_CASE_					:				List[str]=12    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=12    ,	SCREAMING_SNAKE_CASE_					:				Dict=32    ,	SCREAMING_SNAKE_CASE_					:				Dict=128    ,	SCREAMING_SNAKE_CASE_					:				int=0.1    ,	SCREAMING_SNAKE_CASE_					:				List[str]=1E-6    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=1.0    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]="gelu_new"    ,	SCREAMING_SNAKE_CASE_					:				int=0    ,	SCREAMING_SNAKE_CASE_					:				List[str]=False    ,	SCREAMING_SNAKE_CASE_					:				Any=0    ,	SCREAMING_SNAKE_CASE_					:				str=1    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=False    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	):
        __lowerCamelCase:				List[Any]     =	vocab_size
        __lowerCamelCase:				Union[str, Any]     =	hidden_size
        __lowerCamelCase:				Union[str, Any]     =	d_kv
        __lowerCamelCase:				Any     =	d_ff
        __lowerCamelCase:				Optional[Any]     =	num_layers
        __lowerCamelCase:				Union[str, Any]     =	num_heads
        __lowerCamelCase:				List[str]     =	relative_attention_num_buckets
        __lowerCamelCase:				Optional[Any]     =	relative_attention_max_distance
        __lowerCamelCase:				Any     =	dropout_rate
        __lowerCamelCase:				List[Any]     =	layer_norm_epsilon
        __lowerCamelCase:				Optional[Any]     =	initializer_factor
        __lowerCamelCase:				Tuple     =	use_cache

        __lowerCamelCase:				int     =	eos_token_id
        __lowerCamelCase:				Optional[int]     =	decoder_start_token_id

        # for backwards compatibility
        __lowerCamelCase:				Optional[int]     =	dense_act_fn

        super().__init__(
            pad_token_id=SCREAMING_SNAKE_CASE_    ,	eos_token_id=SCREAMING_SNAKE_CASE_    ,	decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,	tie_word_embeddings=SCREAMING_SNAKE_CASE_    ,	is_decoder=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_    ,	)







     @classmethod
     def  SCREAMING_SNAKE_CASE__       (    cls					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Union[str, os.PathLike]    ,	**SCREAMING_SNAKE_CASE_					:				List[str]							):
        cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	cls.get_config_dict(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

        # get the text config dict if we are loading from Pix2StructConfig
        if config_dict.get("""model_type"""							) == "pix2struct":
           __lowerCamelCase:				int     =	config_dict["""text_config"""]

        if "model_type" in config_dict and hasattr(cls    ,	"""model_type"""							) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'							)

        return cls.from_dict(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  List[Any]						      =	"pix2struct_vision_model"





     def __init__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Dict=768    ,	SCREAMING_SNAKE_CASE_					:				int=768    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=2048    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=64    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=12    ,	SCREAMING_SNAKE_CASE_					:				List[str]=12    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]="gelu_new"    ,	SCREAMING_SNAKE_CASE_					:				List[str]=1E-6    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=0.0    ,	SCREAMING_SNAKE_CASE_					:				Any=0.0    ,	SCREAMING_SNAKE_CASE_					:				Dict=1E-1_0    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=1.0    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=4096    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=32    ,	SCREAMING_SNAKE_CASE_					:				str=128    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	):
        super().__init__(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	hidden_size
        __lowerCamelCase:				Dict     =	patch_embed_hidden_size
        __lowerCamelCase:				List[Any]     =	d_ff
        __lowerCamelCase:				Optional[int]     =	dropout_rate
        __lowerCamelCase:				Any     =	num_hidden_layers
        __lowerCamelCase:				List[str]     =	num_attention_heads
        __lowerCamelCase:				Any     =	initializer_range
        __lowerCamelCase:				Optional[Any]     =	initializer_factor
        __lowerCamelCase:				Optional[Any]     =	attention_dropout
        __lowerCamelCase:				Union[str, Any]     =	layer_norm_eps
        __lowerCamelCase:				Tuple     =	dense_act_fn
        __lowerCamelCase:				Tuple     =	seq_len
        __lowerCamelCase:				Optional[int]     =	relative_attention_num_buckets
        __lowerCamelCase:				int     =	relative_attention_max_distance
        __lowerCamelCase:				int     =	d_kv







     @classmethod
     def  SCREAMING_SNAKE_CASE__       (    cls					:				int    ,	SCREAMING_SNAKE_CASE_					:				Union[str, os.PathLike]    ,	**SCREAMING_SNAKE_CASE_					:				List[str]							):
        cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	cls.get_config_dict(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

        # get the vision config dict if we are loading from Pix2StructConfig
        if config_dict.get("""model_type"""							) == "pix2struct":
           __lowerCamelCase:				Optional[Any]     =	config_dict["""vision_config"""]

        if "model_type" in config_dict and hasattr(cls    ,	"""model_type"""							) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'							)

        return cls.from_dict(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  str						      =	"pix2struct"
     UpperCAmelCase__ :  Dict						      =	True





     def __init__(    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=None    ,	SCREAMING_SNAKE_CASE_					:				str=None    ,	SCREAMING_SNAKE_CASE_					:				str=1.0    ,	SCREAMING_SNAKE_CASE_					:				Any=0.02    ,	SCREAMING_SNAKE_CASE_					:				List[str]=False    ,	SCREAMING_SNAKE_CASE_					:				int=False    ,	SCREAMING_SNAKE_CASE_					:				str=True    ,	**SCREAMING_SNAKE_CASE_					:				int    ,	):
        super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE_    ,	is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

        if text_config is None:
           __lowerCamelCase:				Dict     =	{}
           logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""							)

        if vision_config is None:
           __lowerCamelCase:				Optional[Any]     =	{}
           logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""							)

        __lowerCamelCase:				Dict     =	PixaStructTextConfig(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	PixaStructVisionConfig(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	self.text_config.decoder_start_token_id
        __lowerCamelCase:				int     =	self.text_config.pad_token_id
        __lowerCamelCase:				Union[str, Any]     =	self.text_config.eos_token_id

        __lowerCamelCase:				Optional[Any]     =	initializer_factor
        __lowerCamelCase:				Any     =	initializer_range

        __lowerCamelCase:				Union[str, Any]     =	self.initializer_range
        __lowerCamelCase:				Optional[int]     =	self.initializer_range

        __lowerCamelCase:				Optional[Any]     =	is_vqa





     @classmethod
     def  SCREAMING_SNAKE_CASE__       (    cls					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				PixaStructTextConfig    ,	SCREAMING_SNAKE_CASE_					:				PixaStructVisionConfig    ,	**SCREAMING_SNAKE_CASE_					:				str							):
        return cls(text_config=text_config.to_dict()    ,	vision_config=vision_config.to_dict()    ,	**SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Any     =	copy.deepcopy(self.__dict__							)
        __lowerCamelCase:				str     =	self.text_config.to_dict()
        __lowerCamelCase:				Tuple     =	self.vision_config.to_dict()
        __lowerCamelCase:				Optional[int]     =	self.__class__.model_type
        return output






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

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					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	CycleDiffusionPipeline
     UpperCAmelCase__ :  Optional[Any]						      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         "negative_prompt",
         "height",
         "width",
         "negative_prompt_embeds",
     }
     UpperCAmelCase__ :  Union[str, Any]						      =	PipelineTesterMixin.required_optional_params - {"latents"}
     UpperCAmelCase__ :  Optional[int]						      =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
     UpperCAmelCase__ :  Optional[int]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__ :  Optional[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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:				Dict     =	DDIMScheduler(
            beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule="""scaled_linear"""    ,	num_train_timesteps=1000    ,	clip_sample=SCREAMING_SNAKE_CASE_    ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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:				str     =	CLIPTextConfig(
            bos_token_id=0    ,	eos_token_id=2    ,	hidden_size=32    ,	intermediate_size=37    ,	layer_norm_eps=1E-0_5    ,	num_attention_heads=4    ,	num_hidden_layers=5    ,	pad_token_id=1    ,	vocab_size=1000    ,	)
        __lowerCamelCase:				int     =	CLIPTextModel(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

        __lowerCamelCase:				Optional[int]     =	{
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str=0							):
        __lowerCamelCase:				Optional[Any]     =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	image / 2 + 0.5
        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				int     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Tuple     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	{
            """prompt""": """An astronaut riding an elephant""",
            """source_prompt""": """An astronaut riding a horse""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 2,
            """eta""": 0.1,
            """strength""": 0.8,
            """guidance_scale""": 3,
            """source_guidance_scale""": 1,
            """output_type""": """numpy""",
        }
        return inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				Tuple     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	output.images

        __lowerCamelCase:				int     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Dict     =	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]							)

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





     @unittest.skipIf(torch_device != """cuda"""    ,	"""This test requires a GPU"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.get_dummy_components()
        for name, module in components.items():
           if hasattr(SCREAMING_SNAKE_CASE_    ,	"""half"""							):
              __lowerCamelCase:				Tuple     =	module.half()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	output.images

        __lowerCamelCase:				Optional[int]     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				List[str]     =	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]							)

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





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_save_load_local()





     @unittest.skip("""non-deterministic pipeline"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return super().test_inference_batch_single_identical()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        return super().test_save_load_optional_components()







     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_attention_slicing_forward_pass()



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Any     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""							)
        __lowerCamelCase:				str     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				Dict     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[int]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				List[Any]     =	CycleDiffusionPipeline.from_pretrained(
            SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa    ,	revision="""fp16"""							)

        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				List[Any]     =	"""A black colored car"""
        __lowerCamelCase:				List[Any]     =	"""A blue colored car"""

        __lowerCamelCase:				List[Any]     =	torch.manual_seed(0							)
        __lowerCamelCase:				Any     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Dict     =	output.images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(image - expected_image							).max() < 5E-1







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""							)
        __lowerCamelCase:				List[str]     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				List[Any]     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[Any]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				Dict     =	CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_							)

        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Optional[int]     =	"""A black colored car"""
        __lowerCamelCase:				int     =	"""A blue colored car"""

        __lowerCamelCase:				str     =	torch.manual_seed(0							)
        __lowerCamelCase:				Optional[Any]     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Any     =	output.images

        assert np.abs(image - expected_image							).max() < 2E-2


