"""simple docstring"""




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()

class    a_   (  __UpperCamelCase			,		__UpperCamelCase			,		__UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[str]      =     StableDiffusionInstructPixaPixPipeline
					UpperCamelCase_			:						Optional[Any]      =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					UpperCamelCase_			:						List[Any]      =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCamelCase_			:						Tuple      =     IMAGE_TO_IMAGE_IMAGE_PARAMS
					UpperCamelCase_			:						Union[str, Any]      =     IMAGE_TO_IMAGE_IMAGE_PARAMS




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							torch.manual_seed(0  )
							lowerCAmelCase__   = UNetaDConditionModel(
							    block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=8	,    out_channels=4	,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,    cross_attention_dim=32	,    )
							lowerCAmelCase__   = PNDMScheduler(skip_prk_steps=snake_case__  )
							torch.manual_seed(0  )
							lowerCAmelCase__   = 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__   = 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	,    )
							lowerCAmelCase__   = CLIPTextModel(snake_case__  )
							lowerCAmelCase__   = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

							lowerCAmelCase__   = {
							    """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    :  Optional[int]	,    snake_case__    :  Tuple	,    snake_case__    :  Union[str, Any]=0  ):
							lowerCAmelCase__   = floats_tensor((1, 3, 32, 32)	,    rng=random.Random(snake_case__  )  ).to(snake_case__  )
							lowerCAmelCase__   = image.cpu().permute(0	,    2	,    3	,    1  )[0]
							lowerCAmelCase__   = Image.fromarray(np.uinta(snake_case__  )  ).convert("""RGB"""  )
							if str(snake_case__  ).startswith("""mps"""  ):
									lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							else:
									lowerCAmelCase__   = torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )
							lowerCAmelCase__   = {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """image_guidance_scale""": 1,
							    """output_type""": """numpy""",
							}
							return inputs




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__   = self.get_dummy_components()
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline(**snake_case__  )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = self.get_dummy_inputs(snake_case__  )
							lowerCAmelCase__   = sd_pipe(**snake_case__  ).images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__   = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__   = self.get_dummy_components()
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline(**snake_case__  )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = self.get_dummy_inputs(snake_case__  )
							lowerCAmelCase__   = """french fries"""
							lowerCAmelCase__   = sd_pipe(**snake_case__	,    negative_prompt=snake_case__  )
							lowerCAmelCase__   = output.images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__   = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__   = self.get_dummy_components()
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline(**snake_case__  )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = self.get_dummy_inputs(snake_case__  )
							lowerCAmelCase__   = [inputs["""prompt"""]] * 2

							lowerCAmelCase__   = np.array(inputs["""image"""]  ).astype(np.floataa  ) / 255.0
							lowerCAmelCase__   = torch.from_numpy(snake_case__  ).unsqueeze(0  ).to(snake_case__  )
							lowerCAmelCase__   = image / 2 + 0.5
							lowerCAmelCase__   = image.permute(0	,    3	,    1	,    2  )
							lowerCAmelCase__   = image.repeat(2	,    1	,    1	,    1  )

							lowerCAmelCase__   = sd_pipe(**snake_case__  ).images
							lowerCAmelCase__   = image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							lowerCAmelCase__   = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__   = self.get_dummy_components()
							lowerCAmelCase__   = EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0085	,    beta_end=0.012	,    beta_schedule="""scaled_linear"""  )
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline(**snake_case__  )
							lowerCAmelCase__   = sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = self.get_dummy_inputs(snake_case__  )
							lowerCAmelCase__   = sd_pipe(**snake_case__  ).images
							lowerCAmelCase__   = image[0, -3:, -3:, -1]

							lowerCAmelCase__   = [round(snake_case__	,    4  ) for x in image_slice.flatten().tolist()]
							print(""",""".join([str(snake_case__  ) for x in slice]  )  )

							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__   = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = self.get_dummy_components()
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline(**snake_case__  )
							lowerCAmelCase__   = VaeImageProcessor(do_resize=snake_case__	,    do_normalize=snake_case__  )
							lowerCAmelCase__   = pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )

							lowerCAmelCase__   = pipe(**self.get_dummy_inputs_by_type(snake_case__	,    input_image_type="""pt"""  )  )[0]

							lowerCAmelCase__   = components["""vae"""]
							lowerCAmelCase__   = self.get_dummy_inputs_by_type(snake_case__	,    input_image_type="""pt"""  )

							for image_param in self.image_latents_params:
									if image_param in inputs.keys():
											lowerCAmelCase__   = vae.encode(inputs[image_param]  ).latent_dist.mode()

							lowerCAmelCase__   = pipe(**snake_case__  )[0]

							lowerCAmelCase__   = np.abs(out - out_latents_inputs  ).max()
							self.assertLess(snake_case__	,    1E-4	,    """passing latents as image input generate different result from passing image"""  )

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




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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  int=0  ):
							lowerCAmelCase__   = torch.manual_seed(snake_case__  )
							lowerCAmelCase__   = load_image(
							    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""  )
							lowerCAmelCase__   = {
							    """prompt""": """turn him into a cyborg""",
							    """image""": image,
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """guidance_scale""": 7.5,
							    """image_guidance_scale""": 1.0,
							    """output_type""": """numpy""",
							}
							return inputs




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""	,    safety_checker=snake_case__  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing()

							lowerCAmelCase__   = self.get_inputs()
							lowerCAmelCase__   = pipe(**snake_case__  ).images
							lowerCAmelCase__   = image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""	,    safety_checker=snake_case__  )
							lowerCAmelCase__   = LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing()

							lowerCAmelCase__   = self.get_inputs()
							lowerCAmelCase__   = pipe(**snake_case__  ).images
							lowerCAmelCase__   = image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""	,    safety_checker=snake_case__  )
							lowerCAmelCase__   = DDIMScheduler.from_config(pipe.scheduler.config  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing()

							lowerCAmelCase__   = self.get_inputs()
							lowerCAmelCase__   = pipe(**snake_case__  ).images
							lowerCAmelCase__   = image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							lowerCAmelCase__   = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = 0

							def callback_fn(snake_case__    :  int	,    snake_case__    :  int	,    snake_case__    :  torch.FloatTensor  ) -> None:
									lowerCAmelCase__   = True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
											lowerCAmelCase__   = latents.detach().cpu().numpy()
											assert latents.shape == (1, 4, 64, 64)
											lowerCAmelCase__   = latents[0, -3:, -3:, -1]
											lowerCAmelCase__   = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]  )

											assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2
									elif step == 2:
											lowerCAmelCase__   = latents.detach().cpu().numpy()
											assert latents.shape == (1, 4, 64, 64)
											lowerCAmelCase__   = latents[0, -3:, -3:, -1]
											lowerCAmelCase__   = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]  )

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

							lowerCAmelCase__   = False

							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""	,    safety_checker=snake_case__	,    torch_dtype=torch.floataa  )
							lowerCAmelCase__   = pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing()

							lowerCAmelCase__   = self.get_inputs()
							pipe(**snake_case__	,    callback=snake_case__	,    callback_steps=1  )
							assert callback_fn.has_been_called
							assert number_of_steps == 3




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    """timbrooks/instruct-pix2pix"""	,    safety_checker=snake_case__	,    torch_dtype=torch.floataa  )
							lowerCAmelCase__   = pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing(1  )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase__   = self.get_inputs()
							lowerCAmelCase__   = pipe(**snake_case__  )

							lowerCAmelCase__   = torch.cuda.max_memory_allocated()
							# make sure that less than 2.2 GB is allocated
							assert mem_bytes < 2.2 * 10**9




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							lowerCAmelCase__   = inputs["""image"""].resize((504, 504)  )

							lowerCAmelCase__   = """timbrooks/instruct-pix2pix"""
							lowerCAmelCase__   = StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    snake_case__	,    safety_checker=snake_case__	,    )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing()

							lowerCAmelCase__   = pipe(**snake_case__  )
							lowerCAmelCase__   = output.images[0]

							lowerCAmelCase__   = image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							lowerCAmelCase__   = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-3





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return abs(lowerCamelCase__  ) if a == 0 else greatest_common_divisor(b % a      ,	lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				lowerCAmelCase__ ,       lowerCAmelCase__   = y, x % y
		return abs(lowerCamelCase__  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		try:
				lowerCAmelCase__   = input("""Enter two integers separated by comma (,): """  ).split(""","""  )
				lowerCAmelCase__   = int(nums[0]  )
				lowerCAmelCase__   = int(nums[1]  )
				print(
				    f"""greatest_common_divisor({num_a}, {num_a}) = """
				    f"""{greatest_common_divisor(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
				print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
		except (IndexError, UnboundLocalError, ValueError):
				print("""Wrong input"""  )


if __name__ == "__main__":
			main()




