



'''simple docstring'''




import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
a   :			Union[str, Any]					  =    [
    """kernels/rwkv/wkv_cuda.cu""",
    """kernels/rwkv/wkv_op.cpp""",
    """kernels/deformable_detr/ms_deform_attn.h""",
    """kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh""",
    """models/graphormer/algos_graphormer.pyx""",
]



def       __lowerCamelCase  (					_lowercase		)      ->			int:
							# Test all the extensions added in the setup
							for file in FILES_TO_FIND:
														if not (transformers_path / file).exists():
																					return False
							return True


if __name__ == "__main__":
							a   :			str					  =    argparse.ArgumentParser()
							parser.add_argument("""--check_lib""", action="""store_true""", help="""Whether to check the build or the actual package.""")
							a   :			Optional[Any]					  =    parser.parse_args()
							if args.check_lib:
														a   :			Optional[Any]					  =    importlib.import_module("""transformers""")
														a   :			Tuple					  =    Path(transformers_module.__file__).parent
							else:
														a   :			Dict					  =    Path.cwd() / """build/lib/transformers"""
							if not test_custom_files_are_present(transformers_path):
														raise ValueError("""The built release does not contain the custom files. Fix this before going further!""")






'''simple docstring'''




import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	lowercase          = StableDiffusionDiffEditPipeline
	lowercase          = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
	lowercase          = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
	lowercase          = frozenset(
	    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase          = frozenset([]			)





	def  _lowercase(  self    )			->			Optional[int]:
								torch.manual_seed(0    )
								UpperCAmelCase     :		Dict							     =		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		,		attention_head_dim=(2, 4)		,		use_linear_projection=A		,		)
								UpperCAmelCase     :		int							     =		DDIMScheduler(
								    beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule="""scaled_linear"""		,		clip_sample=A		,		set_alpha_to_one=A		,		)
								UpperCAmelCase     :		List[Any]							     =		DDIMInverseScheduler(
								    beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule="""scaled_linear"""		,		clip_sample=A		,		set_alpha_to_zero=A		,		)
								torch.manual_seed(0    )
								UpperCAmelCase     :		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    )
								UpperCAmelCase     :		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=512		,		)
								UpperCAmelCase     :		Optional[Any]							     =		CLIPTextModel(A    )
								UpperCAmelCase     :		List[str]							     =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

								UpperCAmelCase     :		int							     =		{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """inverse_scheduler""": inverse_scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}

								return components





	def  _lowercase(  self		,		A		,		A=0    )			->			Optional[Any]:
								UpperCAmelCase     :		Any							     =		floats_tensor((1, 16, 16)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		Any							     =		floats_tensor((1, 2, 4, 16, 16)		,		rng=random.Random(A    )    ).to(A    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		List[Any]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		List[Any]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		int							     =		{
								    """prompt""": """a dog and a newt""",
								    """mask_image""": mask,
								    """image_latents""": latents,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """inpaint_strength""": 1.0,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}

								return inputs





	def  _lowercase(  self		,		A		,		A=0    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		floats_tensor((1, 3, 32, 32)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		image.cpu().permute(0		,		2		,		3		,		1    )[0]
								UpperCAmelCase     :		int							     =		Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		Optional[int]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		Optional[Any]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		Any							     =		{
								    """image""": image,
								    """source_prompt""": """a cat and a frog""",
								    """target_prompt""": """a dog and a newt""",
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """num_maps_per_mask""": 2,
								    """mask_encode_strength""": 1.0,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}

								return inputs





	def  _lowercase(  self		,		A		,		A=0    )			->			str:
								UpperCAmelCase     :		int							     =		floats_tensor((1, 3, 32, 32)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		str							     =		image.cpu().permute(0		,		2		,		3		,		1    )[0]
								UpperCAmelCase     :		int							     =		Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		Optional[int]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		Optional[int]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		str							     =		{
								    """image""": image,
								    """prompt""": """a cat and a frog""",
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """inpaint_strength""": 1.0,
								    """guidance_scale""": 6.0,
								    """decode_latents""": True,
								    """output_type""": """numpy""",
								}
								return inputs





	def  _lowercase(  self    )			->			List[Any]:
								if not hasattr(self.pipeline_class		,		"""_optional_components"""    ):
															return

								UpperCAmelCase     :		Dict							     =		self.get_dummy_components()
								UpperCAmelCase     :		int							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								# set all optional components to None and update pipeline config accordingly
								for optional_component in pipe._optional_components:
															setattr(A		,		A		,		A    )
								pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

								UpperCAmelCase     :		Any							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Optional[Any]							     =		pipe(**A    )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(A    )
															UpperCAmelCase     :		Dict							     =		self.pipeline_class.from_pretrained(A    )
															pipe_loaded.to(A    )
															pipe_loaded.set_progress_bar_config(disable=A    )

								for optional_component in pipe._optional_components:
															self.assertTrue(
															    getattr(A		,		A    ) is None		,		f'''`{optional_component}` did not stay set to None after loading.'''		,		)

								UpperCAmelCase     :		Union[str, Any]							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Tuple							     =		pipe_loaded(**A    )[0]

								UpperCAmelCase     :		Optional[Any]							     =		np.abs(output - output_loaded    ).max()
								self.assertLess(A		,		1e-4    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[int]							     =		"""cpu"""

								UpperCAmelCase     :		Optional[Any]							     =		self.get_dummy_components()
								UpperCAmelCase     :		List[str]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Union[str, Any]							     =		self.get_dummy_mask_inputs(A    )
								UpperCAmelCase     :		List[Any]							     =		pipe.generate_mask(**A    )
								UpperCAmelCase     :		Dict							     =		mask[0, -3:, -3:]

								self.assertEqual(mask.shape		,		(1, 16, 16)    )
								UpperCAmelCase     :		Optional[int]							     =		np.array([0] * 9    )
								UpperCAmelCase     :		Union[str, Any]							     =		np.abs(mask_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )
								self.assertEqual(mask[0, -3, -4]		,		0    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		"""cpu"""

								UpperCAmelCase     :		List[str]							     =		self.get_dummy_components()
								UpperCAmelCase     :		Optional[Any]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Optional[int]							     =		self.get_dummy_inversion_inputs(A    )
								UpperCAmelCase     :		List[str]							     =		pipe.invert(**A    ).images
								UpperCAmelCase     :		Union[str, Any]							     =		image[0, -1, -3:, -3:]

								self.assertEqual(image.shape		,		(2, 32, 32, 3)    )
								UpperCAmelCase     :		Dict							     =		np.array(
								    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]		,		)
								UpperCAmelCase     :		Dict							     =		np.abs(image_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )





	def  _lowercase(  self    )			->			Dict:
								super().test_inference_batch_single_identical(expected_max_diff=5e-3    )






	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		List[Any]							     =		"""cpu"""

								UpperCAmelCase     :		int							     =		self.get_dummy_components()

								UpperCAmelCase     :		List[Any]							     =		{"""beta_start""": 0.0_0_0_8_5, """beta_end""": 0.0_1_2, """beta_schedule""": """scaled_linear"""}
								UpperCAmelCase     :		int							     =		DPMSolverMultistepScheduler(**A    )
								UpperCAmelCase     :		int							     =		DPMSolverMultistepInverseScheduler(**A    )

								UpperCAmelCase     :		List[str]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Tuple							     =		self.get_dummy_inversion_inputs(A    )
								UpperCAmelCase     :		Any							     =		pipe.invert(**A    ).images
								UpperCAmelCase     :		Dict							     =		image[0, -1, -3:, -3:]

								self.assertEqual(image.shape		,		(2, 32, 32, 3)    )
								UpperCAmelCase     :		Any							     =		np.array(
								    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]		,		)
								UpperCAmelCase     :		Optional[int]							     =		np.abs(image_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )




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





	def  _lowercase(  self    )			->			int:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





	@classmethod
	def  _lowercase(  cls    )			->			Dict:
								UpperCAmelCase     :		Tuple							     =		load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""    )

								UpperCAmelCase     :		Union[str, Any]							     =		raw_image.convert("""RGB"""    ).resize((768, 768)    )

								UpperCAmelCase     :		List[str]							     =		raw_image





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Dict							     =		torch.manual_seed(0    )

								UpperCAmelCase     :		int							     =		StableDiffusionDiffEditPipeline.from_pretrained(
								    """stabilityai/stable-diffusion-2-1"""		,		safety_checker=A		,		torch_dtype=torch.floataa    )
								UpperCAmelCase     :		Optional[int]							     =		DDIMScheduler.from_config(pipe.scheduler.config    )
								UpperCAmelCase     :		Optional[int]							     =		DDIMInverseScheduler.from_config(pipe.scheduler.config    )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Tuple							     =		"""a bowl of fruit"""
								UpperCAmelCase     :		List[Any]							     =		"""a bowl of pears"""

								UpperCAmelCase     :		str							     =		pipe.generate_mask(
								    image=self.raw_image		,		source_prompt=A		,		target_prompt=A		,		generator=A		,		)

								UpperCAmelCase     :		Tuple							     =		pipe.invert(
								    prompt=A		,		image=self.raw_image		,		inpaint_strength=0.7		,		generator=A    ).latents

								UpperCAmelCase     :		Any							     =		pipe(
								    prompt=A		,		mask_image=A		,		image_latents=A		,		generator=A		,		negative_prompt=A		,		inpaint_strength=0.7		,		output_type="""numpy"""		,		).images[0]

								UpperCAmelCase     :		List[str]							     =		(
								    np.array(
								        load_image(
								            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								            """/diffedit/pears.png"""    ).resize((768, 768)    )    )
								    / 255
								)
								assert np.abs((expected_image - image).max()    ) < 5e-1






	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.manual_seed(0    )

								UpperCAmelCase     :		Union[str, Any]							     =		StableDiffusionDiffEditPipeline.from_pretrained(
								    """stabilityai/stable-diffusion-2-1"""		,		safety_checker=A		,		torch_dtype=torch.floataa    )
								UpperCAmelCase     :		List[str]							     =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
								UpperCAmelCase     :		Any							     =		DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		int							     =		"""a bowl of fruit"""
								UpperCAmelCase     :		int							     =		"""a bowl of pears"""

								UpperCAmelCase     :		str							     =		pipe.generate_mask(
								    image=self.raw_image		,		source_prompt=A		,		target_prompt=A		,		generator=A		,		)

								UpperCAmelCase     :		Any							     =		pipe.invert(
								    prompt=A		,		image=self.raw_image		,		inpaint_strength=0.7		,		generator=A		,		num_inference_steps=25		,		).latents

								UpperCAmelCase     :		str							     =		pipe(
								    prompt=A		,		mask_image=A		,		image_latents=A		,		generator=A		,		negative_prompt=A		,		inpaint_strength=0.7		,		num_inference_steps=25		,		output_type="""numpy"""		,		).images[0]

								UpperCAmelCase     :		Tuple							     =		(
								    np.array(
								        load_image(
								            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								            """/diffedit/pears.png"""    ).resize((768, 768)    )    )
								    / 255
								)
								assert np.abs((expected_image - image).max()    ) < 5e-1

