






# 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   SCREAMING_SNAKE_CASE (       snake_case     ,		snake_case     ,		snake_case     ,		unittest.TestCase    ):





							"""simple docstring"""

							A_       =    StableDiffusionControlNetInpaintPipeline
							A_       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							A_       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							A_       =    frozenset({"control_image"}    )  # skip `image` and `mask` for now, only test for control_image
							A_       =    TEXT_TO_IMAGE_IMAGE_PARAMS

							def 					__A       (     self:      int      )			-> Tuple:
												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=0.00_085						,   beta_end=0.012						,   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 					__A       (     self:      Tuple						,   __A:      Any						,   __A:      Any=0      )			-> List[Any]:
												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 					__A       (     self:      List[Any]      )			-> List[Any]:
												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 					__A       (     self:      Tuple      )			-> int:
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )



							def 					__A       (     self:      List[Any]      )			-> Union[str, Any]:
												self._test_inference_batch_single_identical(expected_max_diff=2e-3      )





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    StableDiffusionControlNetInpaintPipeline
							A_       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							A_       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							A_       =    frozenset([]    )

							def 					__A       (     self:      Dict      )			-> str:
												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=0.00_085						,   beta_end=0.012						,   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   SCREAMING_SNAKE_CASE (       snake_case     ,		snake_case     ,		unittest.TestCase    ):





							"""simple docstring"""

							A_       =    StableDiffusionControlNetInpaintPipeline
							A_       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							A_       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

							def 					__A       (     self:      Dict      )			-> str:
												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:      Optional[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=0.00_085						,   beta_end=0.012						,   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 					__A       (     self:      int						,   __A:      Optional[Any]						,   __A:      List[str]=0      )			-> List[str]:
												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 					__A       (     self:      int      )			-> int:
												_A									=       self.get_dummy_components()
												_A									=       self.pipeline_class(**__A      )
												pipe.to(__A      )

												_A									=       10.0
												_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 					__A       (     self:      Optional[Any]      )			-> str:
												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 					__A       (     self:      Dict      )			-> List[str]:
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )

							def 					__A       (     self:      Dict      )			-> Optional[int]:
												self._test_inference_batch_single_identical(expected_max_diff=2e-3      )



							def 					__A       (     self:      str      )			-> List[Any]:
												_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   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

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

							def 					__A       (     self:      Optional[int]      )			-> List[Any]:
												_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 					__A       (     self:      str      )			-> List[Any]:
												_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[str]						,   __A:      List[str]      ):
																	_A									=       np.array(image.convert('''RGB'''      )      ).astype(np.floataa      ) / 255.0
																	_A									=       np.array(image_mask.convert('''L'''      )      ).astype(np.floataa      ) / 255.0

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


__A          = logging.get_logger(__name__)

__A          = {
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "nystromformer"

							def __init__(     self:      List[str]						,   __A:      Dict=3_00_00						,   __A:      int=7_68						,   __A:      int=12						,   __A:      Union[str, Any]=12						,   __A:      List[str]=30_72						,   __A:      Tuple="gelu_new"						,   __A:      Optional[Any]=0.1						,   __A:      Union[str, Any]=0.1						,   __A:      int=5_10						,   __A:      List[Any]=2						,   __A:      List[Any]=64						,   __A:      Optional[Any]=64						,   __A:      str=65						,   __A:      List[str]=False						,   __A:      Tuple=0.02						,   __A:      Dict=1e-5						,   __A:      int=1						,   __A:      str=0						,   __A:      int=2						,   **__A:      Union[str, Any]						,   )			-> int:
												_A									=       vocab_size
												_A									=       max_position_embeddings
												_A									=       hidden_size
												_A									=       num_hidden_layers
												_A									=       num_attention_heads
												_A									=       intermediate_size
												_A									=       hidden_act
												_A									=       hidden_dropout_prob
												_A									=       attention_probs_dropout_prob
												_A									=       initializer_range
												_A									=       type_vocab_size
												_A									=       segment_means_seq_len
												_A									=       num_landmarks
												_A									=       conv_kernel_size
												_A									=       inv_coeff_init_option
												_A									=       layer_norm_eps
												super().__init__(pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   **__A      )



