




# This model implementation is heavily based on:

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,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()







class 		__UpperCAmelCase(		lowercase__							,						lowercase__							,						lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     StableDiffusionControlNetInpaintPipeline
     __UpperCAmelCase						:			Union[str, Any]							=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     __UpperCAmelCase						:			List[Any]							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCAmelCase						:			Tuple							=     frozenset({"""control_image"""}			)  # skip `image` and `mask` for now, only test for control_image
     __UpperCAmelCase						:			Optional[int]							=     TEXT_TO_IMAGE_IMAGE_PARAMS

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      int:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							UNetaDConditionModel(
             block_out_channels=(32, 64)    ,					layers_per_block=2    ,					sample_size=32    ,					in_channels=9    ,					out_channels=4    ,					down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,					up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,					cross_attention_dim=32    ,					)
         torch.manual_seed(0     )
         a__  			=							ControlNetModel(
             block_out_channels=(32, 64)    ,					layers_per_block=2    ,					in_channels=4    ,					down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,					cross_attention_dim=32    ,					conditioning_embedding_out_channels=(16, 32)    ,					)
         torch.manual_seed(0     )
         a__  			=							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    ,					)
         torch.manual_seed(0     )
         a__  			=							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     )
         a__  			=							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=10_00    ,					)
         a__  			=							CLIPTextModel(A     )
         a__  			=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

         a__  			=							{
             """unet""": unet,
             """controlnet""": controlnet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							int    ,					A			:							Optional[int]=0     )      ->      List[str]:






         """simple docstring"""
         if str(A     ).startswith("""mps"""     ):
             a__  			=							torch.manual_seed(A     )
         else:
             a__  			=							torch.Generator(device=A     ).manual_seed(A     )

         a__  			=							2
         a__  			=							randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,					generator=A    ,					device=torch.device(A     )    ,					)
         a__  			=							floats_tensor((1, 3, 32, 32)    ,					rng=random.Random(A     )     ).to(A     )
         a__  			=							init_image.cpu().permute(0    ,					2    ,					3    ,					1     )[0]

         a__  			=							Image.fromarray(np.uinta(A     )     ).convert("""RGB"""     ).resize((64, 64)     )
         a__  			=							Image.fromarray(np.uinta(init_image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )

         a__  			=							{
             """prompt""": """A painting of a squirrel eating a burger""",
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
             """image""": image,
             """mask_image""": mask_image,
             """control_image""": control_image,
         }

         return inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Optional[Any]:






         """simple docstring"""
         return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3     )

     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()    ,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,					)
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Optional[int]:






         """simple docstring"""
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      List[Any]:






         """simple docstring"""
         self._test_inference_batch_single_identical(expected_max_diff=2E-3     )







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     StableDiffusionControlNetInpaintPipeline
     __UpperCAmelCase						:			Tuple							=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     __UpperCAmelCase						:			List[str]							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCAmelCase						:			Optional[int]							=     frozenset([]			)

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






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							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    ,					)
         torch.manual_seed(0     )
         a__  			=							ControlNetModel(
             block_out_channels=(32, 64)    ,					layers_per_block=2    ,					in_channels=4    ,					down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,					cross_attention_dim=32    ,					conditioning_embedding_out_channels=(16, 32)    ,					)
         torch.manual_seed(0     )
         a__  			=							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    ,					)
         torch.manual_seed(0     )
         a__  			=							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     )
         a__  			=							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=10_00    ,					)
         a__  			=							CLIPTextModel(A     )
         a__  			=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

         a__  			=							{
             """unet""": unet,
             """controlnet""": controlnet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components







class 		__UpperCAmelCase(		lowercase__							,						lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Dict							=     StableDiffusionControlNetInpaintPipeline
     __UpperCAmelCase						:			Any							=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     __UpperCAmelCase						:			Optional[Any]							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Dict:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							UNetaDConditionModel(
             block_out_channels=(32, 64)    ,					layers_per_block=2    ,					sample_size=32    ,					in_channels=9    ,					out_channels=4    ,					down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,					up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,					cross_attention_dim=32    ,					)
         torch.manual_seed(0     )

         def init_weights(A			:							Any     ):
             if isinstance(A    ,					torch.nn.Convad     ):
                 torch.nn.init.normal(m.weight     )
                 m.bias.data.fill_(1.0     )

         a__  			=							ControlNetModel(
             block_out_channels=(32, 64)    ,					layers_per_block=2    ,					in_channels=4    ,					down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,					cross_attention_dim=32    ,					conditioning_embedding_out_channels=(16, 32)    ,					)
         controlneta.controlnet_down_blocks.apply(A     )

         torch.manual_seed(0     )
         a__  			=							ControlNetModel(
             block_out_channels=(32, 64)    ,					layers_per_block=2    ,					in_channels=4    ,					down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,					cross_attention_dim=32    ,					conditioning_embedding_out_channels=(16, 32)    ,					)
         controlneta.controlnet_down_blocks.apply(A     )

         torch.manual_seed(0     )
         a__  			=							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    ,					)
         torch.manual_seed(0     )
         a__  			=							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     )
         a__  			=							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=10_00    ,					)
         a__  			=							CLIPTextModel(A     )
         a__  			=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

         a__  			=							MultiControlNetModel([controlneta, controlneta]     )

         a__  			=							{
             """unet""": unet,
             """controlnet""": controlnet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Optional[int]    ,					A			:							Dict=0     )      ->      Union[str, Any]:






         """simple docstring"""
         if str(A     ).startswith("""mps"""     ):
             a__  			=							torch.manual_seed(A     )
         else:
             a__  			=							torch.Generator(device=A     ).manual_seed(A     )

         a__  			=							2

         a__  			=							[
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,					generator=A    ,					device=torch.device(A     )    ,					),
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,					generator=A    ,					device=torch.device(A     )    ,					),
         ]
         a__  			=							floats_tensor((1, 3, 32, 32)    ,					rng=random.Random(A     )     ).to(A     )
         a__  			=							init_image.cpu().permute(0    ,					2    ,					3    ,					1     )[0]

         a__  			=							Image.fromarray(np.uinta(A     )     ).convert("""RGB"""     ).resize((64, 64)     )
         a__  			=							Image.fromarray(np.uinta(init_image + 4     )     ).convert("""RGB"""     ).resize((64, 64)     )

         a__  			=							{
             """prompt""": """A painting of a squirrel eating a burger""",
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
             """image""": image,
             """mask_image""": mask_image,
             """control_image""": control_image,
         }

         return inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      Dict:






         """simple docstring"""
         a__  			=							self.get_dummy_components()
         a__  			=							self.pipeline_class(**A     )
         pipe.to(A     )

         a__  			=							1_0.0
         a__  			=							4

         a__  			=							self.get_dummy_inputs(A     )
         a__  			=							steps
         a__  			=							scale
         a__  			=							pipe(**A     )[0]

         a__  			=							self.get_dummy_inputs(A     )
         a__  			=							steps
         a__  			=							scale
         a__  			=							pipe(**A    ,					control_guidance_start=0.1    ,					control_guidance_end=0.2     )[0]

         a__  			=							self.get_dummy_inputs(A     )
         a__  			=							steps
         a__  			=							scale
         a__  			=							pipe(**A    ,					control_guidance_start=[0.1, 0.3]    ,					control_guidance_end=[0.2, 0.7]     )[0]

         a__  			=							self.get_dummy_inputs(A     )
         a__  			=							steps
         a__  			=							scale
         a__  			=							pipe(**A    ,					control_guidance_start=0.4    ,					control_guidance_end=[0.5, 0.8]     )[0]

         # make sure that all outputs are different
         assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3
         assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3
         assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      Optional[int]:






         """simple docstring"""
         return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3     )

     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()    ,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,					)
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      Tuple:






         """simple docstring"""
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )

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






         """simple docstring"""
         self._test_inference_batch_single_identical(expected_max_diff=2E-3     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Dict:






         """simple docstring"""
         a__  			=							self.get_dummy_components()
         a__  			=							self.pipeline_class(**A     )
         pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )
         with tempfile.TemporaryDirectory() as tmpdir:
             try:
                 # save_pretrained is not implemented for Multi-ControlNet
                 pipe.save_pretrained(A     )
             except NotImplementedError:
                 pass







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





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Any:






         """simple docstring"""
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""     )

         a__  			=							StableDiffusionControlNetInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""    ,					safety_checker=A    ,					controlnet=A     )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=A     )

         a__  			=							torch.Generator(device="""cpu"""     ).manual_seed(0     )
         a__  			=							load_image(
             """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""     ).resize((5_12, 5_12)     )

         a__  			=							load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_inpaint/input_bench_mask.png"""     ).resize((5_12, 5_12)     )

         a__  			=							"""pitch black hole"""

         a__  			=							load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""     ).resize((5_12, 5_12)     )

         a__  			=							pipe(
             A    ,					image=A    ,					mask_image=A    ,					control_image=A    ,					generator=A    ,					output_type="""np"""    ,					num_inference_steps=3    ,					)

         a__  			=							output.images[0]

         assert image.shape == (5_12, 5_12, 3)

         a__  			=							load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""     )

         assert np.abs(expected_image - image     ).max() < 9E-2






     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""     )

         a__  			=							StableDiffusionControlNetInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-v1-5"""    ,					safety_checker=A    ,					controlnet=A     )
         a__  			=							DDIMScheduler.from_config(pipe.scheduler.config     )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=A     )

         a__  			=							torch.Generator(device="""cpu"""     ).manual_seed(33     )

         a__  			=							load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""     )
         a__  			=							init_image.resize((5_12, 5_12)     )

         a__  			=							load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""     )
         a__  			=							mask_image.resize((5_12, 5_12)     )

         a__  			=							"""a handsome man with ray-ban sunglasses"""

         def make_inpaint_condition(A			:							List[Any]    ,					A			:							List[Any]     ):
             a__  			=							np.array(image.convert("""RGB"""     )     ).astype(np.floataa     ) / 2_5_5.0
             a__  			=							np.array(image_mask.convert("""L"""     )     ).astype(np.floataa     ) / 2_5_5.0

             assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
             a__  			=							-1.0  # set as masked pixel
             a__  			=							np.expand_dims(A    ,					0     ).transpose(0    ,					3    ,					1    ,					2     )
             a__  			=							torch.from_numpy(A     )
             return image

         a__  			=							make_inpaint_condition(A    ,					A     )

         a__  			=							pipe(
             A    ,					image=A    ,					mask_image=A    ,					control_image=A    ,					guidance_scale=9.0    ,					eta=1.0    ,					generator=A    ,					num_inference_steps=20    ,					output_type="""np"""    ,					)
         a__  			=							output.images[0]

         assert image.shape == (5_12, 5_12, 3)

         a__  			=							load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""     )

         assert np.abs(expected_image - image     ).max() < 9E-2












from __future__ import annotations

import sys
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""		)
    return _fib(UpperCAmelCase_		)[0]
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    a__			,		a__  			=							_fib(n // 2		)
    a__  			=							a * (b * 2 - a)
    a__  			=							a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_    :       Dict       =							int(sys.argv[1])
 print(F"fibonacci({n}) is {fibonacci(n)}")






