



'''simple docstring'''







# 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 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		Any		=      StableDiffusionControlNetInpaintPipeline
			__snake_case  :		Optional[Any]		=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__snake_case  :		Optional[Any]		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__snake_case  :		Dict		=      frozenset({'control_image'}      )  # skip `image` and `mask` for now, only test for control_image
			__snake_case  :		Dict		=      TEXT_TO_IMAGE_IMAGE_PARAMS






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      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	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDIMScheduler(
					    beta_start=0.0_00_85      ,       beta_end=0.0_12      ,       beta_schedule='scaled_linear'      ,       clip_sample=SCREAMING_SNAKE_CASE      ,       set_alpha_to_one=SCREAMING_SNAKE_CASE      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      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	)
					SCREAMING_SNAKE_CASE_						:Any										=      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=1_000      ,       )
					SCREAMING_SNAKE_CASE_						:Tuple										=      CLIPTextModel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

					SCREAMING_SNAKE_CASE_						:str										=      {
					    'unet': unet,
					    'controlnet': controlnet,
					    'scheduler': scheduler,
					    'vae': vae,
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'safety_checker': None,
					    'feature_extractor': None,
					}
					return components






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Dict=0	):




					"""simple docstring"""







					if str(SCREAMING_SNAKE_CASE	).startswith('mps'	):
							SCREAMING_SNAKE_CASE_						:Any										=      torch.manual_seed(SCREAMING_SNAKE_CASE	)
					else:
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      2
					SCREAMING_SNAKE_CASE_						:str										=      randn_tensor(
					    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,       generator=SCREAMING_SNAKE_CASE      ,       device=torch.device(SCREAMING_SNAKE_CASE	)      ,       )
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      init_image.cpu().permute(0      ,       2      ,       3      ,       1	)[0]

					SCREAMING_SNAKE_CASE_						:Dict										=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE	)	).convert('RGB'	).resize((64, 64)	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      Image.fromarray(np.uinta(init_image + 4	)	).convert('RGB'	).resize((64, 64)	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      {
					    '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 				_lowercase	(   self  :							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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					self._test_inference_batch_single_identical(expected_max_diff=2E-3	)




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[Any]		=      StableDiffusionControlNetInpaintPipeline
			__snake_case  :		int		=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__snake_case  :		Optional[Any]		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__snake_case  :		Tuple		=      frozenset([]      )






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:str										=      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	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      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	)
					SCREAMING_SNAKE_CASE_						:int										=      DDIMScheduler(
					    beta_start=0.0_00_85      ,       beta_end=0.0_12      ,       beta_schedule='scaled_linear'      ,       clip_sample=SCREAMING_SNAKE_CASE      ,       set_alpha_to_one=SCREAMING_SNAKE_CASE      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Dict										=      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	)
					SCREAMING_SNAKE_CASE_						: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=1_000      ,       )
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      CLIPTextModel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

					SCREAMING_SNAKE_CASE_						:Any										=      {
					    'unet': unet,
					    'controlnet': controlnet,
					    'scheduler': scheduler,
					    'vae': vae,
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'safety_checker': None,
					    'feature_extractor': None,
					}
					return components




class 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		str		=      StableDiffusionControlNetInpaintPipeline
			__snake_case  :		List[str]		=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__snake_case  :		List[Any]		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      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(SCREAMING_SNAKE_CASE  :							Dict	):
							if isinstance(SCREAMING_SNAKE_CASE      ,       torch.nn.Convad	):
									torch.nn.init.normal(m.weight	)
									m.bias.data.fill_(1.0	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      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(SCREAMING_SNAKE_CASE	)

					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      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(SCREAMING_SNAKE_CASE	)

					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      DDIMScheduler(
					    beta_start=0.0_00_85      ,       beta_end=0.0_12      ,       beta_schedule='scaled_linear'      ,       clip_sample=SCREAMING_SNAKE_CASE      ,       set_alpha_to_one=SCREAMING_SNAKE_CASE      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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	)
					SCREAMING_SNAKE_CASE_						:str										=      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=1_000      ,       )
					SCREAMING_SNAKE_CASE_						:Dict										=      CLIPTextModel(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

					SCREAMING_SNAKE_CASE_						:Dict										=      MultiControlNetModel([controlneta, controlneta]	)

					SCREAMING_SNAKE_CASE_						:Dict										=      {
					    'unet': unet,
					    'controlnet': controlnet,
					    'scheduler': scheduler,
					    'vae': vae,
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'safety_checker': None,
					    'feature_extractor': None,
					}
					return components






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=0	):




					"""simple docstring"""







					if str(SCREAMING_SNAKE_CASE	).startswith('mps'	):
							SCREAMING_SNAKE_CASE_						:int										=      torch.manual_seed(SCREAMING_SNAKE_CASE	)
					else:
							SCREAMING_SNAKE_CASE_						:Tuple										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Dict										=      2

					SCREAMING_SNAKE_CASE_						:Tuple										=      [
					    randn_tensor(
					        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,       generator=SCREAMING_SNAKE_CASE      ,       device=torch.device(SCREAMING_SNAKE_CASE	)      ,       ),
					    randn_tensor(
					        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,       generator=SCREAMING_SNAKE_CASE      ,       device=torch.device(SCREAMING_SNAKE_CASE	)      ,       ),
					]
					SCREAMING_SNAKE_CASE_						:List[str]										=      floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      init_image.cpu().permute(0      ,       2      ,       3      ,       1	)[0]

					SCREAMING_SNAKE_CASE_						:List[Any]										=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE	)	).convert('RGB'	).resize((64, 64)	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      Image.fromarray(np.uinta(init_image + 4	)	).convert('RGB'	).resize((64, 64)	)

					SCREAMING_SNAKE_CASE_						:str										=      {
					    '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 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_dummy_components()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.pipeline_class(**SCREAMING_SNAKE_CASE	)
					pipe.to(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      10.0
					SCREAMING_SNAKE_CASE_						:Any										=      4

					SCREAMING_SNAKE_CASE_						:str										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      steps
					SCREAMING_SNAKE_CASE_						:List[str]										=      scale
					SCREAMING_SNAKE_CASE_						:str										=      pipe(**SCREAMING_SNAKE_CASE	)[0]

					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      steps
					SCREAMING_SNAKE_CASE_						:Tuple										=      scale
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      pipe(**SCREAMING_SNAKE_CASE      ,       control_guidance_start=0.1      ,       control_guidance_end=0.2	)[0]

					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Dict										=      steps
					SCREAMING_SNAKE_CASE_						:List[str]										=      scale
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      pipe(**SCREAMING_SNAKE_CASE      ,       control_guidance_start=[0.1, 0.3]      ,       control_guidance_end=[0.2, 0.7]	)[0]

					SCREAMING_SNAKE_CASE_						:int										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      steps
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      scale
					SCREAMING_SNAKE_CASE_						:int										=      pipe(**SCREAMING_SNAKE_CASE      ,       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 				_lowercase	(   self  :							Tuple	):




					"""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 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					self._test_inference_batch_single_identical(expected_max_diff=2E-3	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      self.get_dummy_components()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.pipeline_class(**SCREAMING_SNAKE_CASE	)
					pipe.to(SCREAMING_SNAKE_CASE	)
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)
					with tempfile.TemporaryDirectory() as tmpdir:
							try:
									# save_pretrained is not implemented for Multi-ControlNet
									pipe.save_pretrained(SCREAMING_SNAKE_CASE	)
							except NotImplementedError:
									pass




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






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'	)

					SCREAMING_SNAKE_CASE_						:Dict										=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
					    'runwayml/stable-diffusion-inpainting'      ,       safety_checker=SCREAMING_SNAKE_CASE      ,       controlnet=SCREAMING_SNAKE_CASE	)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.Generator(device='cpu'	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      load_image(
					    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'	).resize((512, 512)	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_inpaint/input_bench_mask.png'	).resize((512, 512)	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      'pitch black hole'

					SCREAMING_SNAKE_CASE_						:str										=      load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	).resize((512, 512)	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      pipe(
					    SCREAMING_SNAKE_CASE      ,       image=SCREAMING_SNAKE_CASE      ,       mask_image=SCREAMING_SNAKE_CASE      ,       control_image=SCREAMING_SNAKE_CASE      ,       generator=SCREAMING_SNAKE_CASE      ,       output_type='np'      ,       num_inference_steps=3      ,       )

					SCREAMING_SNAKE_CASE_						:str										=      output.images[0]

					assert image.shape == (512, 512, 3)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      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 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
					    'runwayml/stable-diffusion-v1-5'      ,       safety_checker=SCREAMING_SNAKE_CASE      ,       controlnet=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      DDIMScheduler.from_config(pipe.scheduler.config	)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.Generator(device='cpu'	).manual_seed(33	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      init_image.resize((512, 512)	)

					SCREAMING_SNAKE_CASE_						:Any										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      mask_image.resize((512, 512)	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      'a handsome man with ray-ban sunglasses'

					def make_inpaint_condition(SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]	):
							SCREAMING_SNAKE_CASE_						:List[Any]										=      np.array(image.convert('RGB'	)	).astype(np.floataa	) / 2_55.0
							SCREAMING_SNAKE_CASE_						:Any										=      np.array(image_mask.convert('L'	)	).astype(np.floataa	) / 2_55.0

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

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      make_inpaint_condition(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      pipe(
					    SCREAMING_SNAKE_CASE      ,       image=SCREAMING_SNAKE_CASE      ,       mask_image=SCREAMING_SNAKE_CASE      ,       control_image=SCREAMING_SNAKE_CASE      ,       guidance_scale=9.0      ,       eta=1.0      ,       generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=20      ,       output_type='np'      ,       )
					SCREAMING_SNAKE_CASE_						:List[str]										=      output.images[0]

					assert image.shape == (512, 512, 3)

					SCREAMING_SNAKE_CASE_						:Any										=      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






import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_UpperCAmelCase						:    Dict      =  logging.getLogger(__name__)



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    argparse.ArgumentParser(description="PyTorch Transformer Language Model" )
  parser.add_argument("--model_name",      type=lowerCamelCase,      default="transfo-xl-wt103",      help="pretrained model name" )
  parser.add_argument(
      "--split",      type=lowerCamelCase,      default="test",      choices=["all", "valid", "test"],      help="which split to evaluate" )
  parser.add_argument("--batch_size",      type=lowerCamelCase,      default=10,      help="batch size" )
  parser.add_argument("--tgt_len",      type=lowerCamelCase,      default=128,      help="number of tokens to predict" )
  parser.add_argument("--ext_len",      type=lowerCamelCase,      default=0,      help="length of the extended context" )
  parser.add_argument("--mem_len",      type=lowerCamelCase,      default=1600,      help="length of the retained previous heads" )
  parser.add_argument("--clamp_len",      type=lowerCamelCase,      default=1000,      help="max positional embedding index" )
  parser.add_argument("--no_cuda",      action="store_true",      help="Do not use CUDA even though CUA is available" )
  parser.add_argument("--work_dir",      type=lowerCamelCase,      required=lowerCamelCase,      help="path to the work_dir" )
  parser.add_argument("--no_log",      action="store_true",      help="do not log the eval result" )
  parser.add_argument("--same_length",      action="store_true",      help="set same length attention with masking" )
  parser.add_argument("--server_ip",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  parser.add_argument("--server_port",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  lowercase     :Any   						=    parser.parse_args()
  assert args.ext_len >= 0, "extended context length must be non-negative"

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print("Waiting for debugger attach" )
    ptvsd.enable_attach(address=(args.server_ip, args.server_port),      redirect_output=lowerCamelCase )
    ptvsd.wait_for_attach()

  lowercase     :Any   						=    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  logger.info("device: {}".format(lowerCamelCase ) )

  # Load a pre-processed dataset
  # You can also build the corpus yourself using TransfoXLCorpus methods
  # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
  # and tokenizing the dataset
  # The pre-processed corpus is a convertion (using the conversion script )
  lowercase     :Dict   						=    TransfoXLCorpus.from_pretrained(args.model_name )

  lowercase     :Optional[int]   						=    corpus.get_iterator("valid",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )
  lowercase     :Union[str, Any]   						=    corpus.get_iterator("test",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )

  # Load a pre-trained model
  lowercase     :List[Any]   						=    TransfoXLLMHeadModel.from_pretrained(args.model_name )
  model.to(lowerCamelCase )

  logger.info(
      "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
          args.batch_size,      args.tgt_len,      args.ext_len,      args.mem_len,      args.clamp_len ) )

  model.reset_memory_length(args.mem_len )
  if args.clamp_len > 0:
    lowercase     :Tuple   						=    args.clamp_len
  if args.same_length:
    lowercase     :Tuple   						=    True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(lowerCamelCase ):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    lowercase						,     lowercase     :Optional[Any]   						=    0, 0.0
    lowercase     :Optional[Any]   						=    time.time()
    with torch.no_grad():
      lowercase     :Dict   						=    None
      for idx, (data, target, seq_len) in enumerate(lowerCamelCase ):
        lowercase     :Optional[Any]   						=    model(lowerCamelCase,      lm_labels=lowerCamelCase,      mems=lowerCamelCase )
        lowercase						,     lowercase						,     lowercase     :Optional[int]   						=    ret
        lowercase     :Optional[Any]   						=    loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      lowercase     :List[Any]   						=    time.time() - start_time
    logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(lowerCamelCase,      1000 * total_time / (idx + 1) ) )
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    lowercase     :Union[str, Any]   						=    evaluate(lowerCamelCase )
    lowercase     :List[str]   						=    evaluate(lowerCamelCase )
  elif args.split == "valid":
    lowercase     :Optional[int]   						=    evaluate(lowerCamelCase )
    lowercase     :Dict   						=    None
  elif args.split == "test":
    lowercase     :int   						=    evaluate(lowerCamelCase )
    lowercase     :Optional[Any]   						=    None

  def format_log(lowerCamelCase,      lowerCamelCase ):
    lowercase     :List[str]   						=    "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(lowerCamelCase,      lowerCamelCase,      math.exp(lowerCamelCase ) )
    return log_str

  lowercase     :Optional[int]   						=    ""
  if valid_loss is not None:
    log_str += format_log(lowerCamelCase,      "valid" )
  if test_loss is not None:
    log_str += format_log(lowerCamelCase,      "test" )

  logger.info("=" * 100 )
  logger.info(lowerCamelCase )
  logger.info("=" * 100 )


if __name__ == "__main__":
     main()



