



'''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 logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed




class        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Optional[Any]=10_24   , __lowerCamelCase							:	str=10_24   , __lowerCamelCase							:	Any=3.6   , __lowerCamelCase							:	List[Any]=False   , )   ->	Optional[Any]:
							a       =     tokenizer
							a       =     tokenizer.bos_token_id
							a       =     dataset
							a       =     seq_length
							a       =     0
							a       =     infinite
							a       =     0
							a       =     tokenized

							if self.tokenized:
										a       =     seq_length * num_of_sequences
										a       =     "input_ids"
							else:
										a       =     seq_length * chars_per_token * num_of_sequences
										a       =     "content"





				def __iter__( self							:	Union[str, Any]				)   ->	List[str]:
							a       =     iter(self.dataset				)
							a       =     True
							while more_examples:
										a       ,						a       =     [], 0
										while True:
													if buffer_len >= self.max_buffer_size:
																break
													try:
																buffer.append(next(__lowerCamelCase				)[self.content_field]				)
																buffer_len += len(buffer[-1]				)
													except StopIteration:
																if self.infinite:
																			a       =     iter(self.dataset				)
																			self.epoch += 1
																			logger.info(f"""Dataset epoch: {self.epoch}"""				)
																else:
																			a       =     False
																			break
										if self.tokenized:
													a       =     buffer
										else:
													a       =     self.tokenizer(__lowerCamelCase   , truncation=__lowerCamelCase				)["input_ids"]
										a       =     []
										for tokenized_input in tokenized_inputs:
													all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
										for i in range(0   , len(__lowerCamelCase				)   , self.seq_length				):
													a       =     all_token_ids[i : i + self.seq_length]
													if len(__lowerCamelCase				) == self.seq_length:
																self.current_size += 1
																yield torch.tensor(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Optional[int]=10_00				)   ->	Union[str, Any]:
							return ShufflerIterDataPipe(self   , buffer_size=__lowerCamelCase				)




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







			a       =     args.model_ckpt.split("/"  )[-1]
			a       =     logging.getLogger(__name__  )
			a       =     Path(args.save_dir  ) / "log/"
			log_dir.mkdir(exist_ok=A  )
			a       =     F"""debug_{accelerator.process_index}.log"""
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",    datefmt="%m/%d/%Y %H:%M:%S",    level=logging.INFO,    handlers=[logging.FileHandler(log_dir / filename  ), logging.StreamHandler()],    )
			if accelerator.is_main_process:  # we only want to setup logging once
						accelerator.init_trackers(A,    vars(A  )  )
						a       =     accelerator.trackers[0].run.name
						logger.setLevel(logging.INFO  )
						datasets.utils.logging.set_verbosity_info()
						transformers.utils.logging.set_verbosity_info()
			else:
						a       =     ""
						logger.setLevel(logging.ERROR  )
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			return logger, run_name




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







			a       =     {"streaming": True}
			a       =     load_dataset(args.dataset_name_train,    split="train",    **A  )
			a       =     train_data.shuffle(buffer_size=args.shuffle_buffer,    seed=args.seed  )
			a       =     load_dataset(args.dataset_name_valid,    split="train",    **A  )
			a       =     ConstantLengthDataset(
			    A,    A,    infinite=A,    seq_length=args.seq_length,    tokenized=args.tokenized  )
			a       =     ConstantLengthDataset(
			    A,    A,    infinite=A,    seq_length=args.seq_length,    tokenized=args.tokenized  )
			a       =     train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
			a       =     DataLoader(A,    batch_size=args.train_batch_size,    shuffle=A  )
			a       =     DataLoader(A,    batch_size=args.valid_batch_size  )
			return train_dataloader, eval_dataloader




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any],    A						:   Tuple=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):






			'''simple docstring'''







			a       ,						a       =     [], []
			for n, p in model.named_parameters():
						if any(nd in n for nd in no_decay  ):
									params_without_wd.append(A  )
						else:
									params_with_wd.append(A  )
			return [
			    {"params": params_with_wd, "weight_decay": args.weight_decay},
			    {"params": params_without_wd, "weight_decay": 0.0},
			]




def      __magic_name__							(					A						:   List[str],    A						:   List[str]  ):






			'''simple docstring'''







			logger.info(F"""Step {step}: {metrics}"""  )
			if accelerator.is_main_process:
						accelerator.log(A,    A  )




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[int],    A						:   List[str]  ):






			'''simple docstring'''







			a       =     accelerator.unwrap_model(A  ).config
			a       =     4 if args.gradient_checkpointing else 3
			a       =     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			a       =     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			a       =     factor * (
			    1.0
			    + (args.seq_length / (6.0 * config_model.n_embd))
			    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
			)
			a       =     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops




def      __magic_name__							(					A						:   List[Any]  ):






			'''simple docstring'''







			model.eval()
			a       =     []
			for step, batch in enumerate(A  ):
						with torch.no_grad():
									a       =     model(A,    labels=A  )
						a       =     outputs.loss.repeat(args.valid_batch_size  )
						losses.append(accelerator.gather(A  )  )
						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			a       =     torch.cat(A  )
			a       =     losses[: eval_dataloader.dataset.current_size].mean()
			try:
						a       =     torch.exp(A  )
			except OverflowError:
						a       =     float("inf"  )
			return loss.item(), perplexity.item()


# Settings
__lowerCAmelCase						:							Optional[int]			     =						HfArgumentParser(TrainingArguments)
__lowerCAmelCase						:							Optional[Any]			     =						parser.parse_args()

# Accelerator
__lowerCAmelCase						:							int			     =						ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
__lowerCAmelCase						:							Union[str, Any]			     =						Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
__lowerCAmelCase						:							Tuple			     =						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__lowerCAmelCase						:							Dict			     =						Namespace(**vars(args), **acc_state)
__lowerCAmelCase						:							Optional[int]			     =						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		__lowerCAmelCase						:							List[Any]			     =						Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowerCAmelCase     ,	__lowerCAmelCase						:							Dict			     =						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__lowerCAmelCase						:							Union[str, Any]			     =						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
__lowerCAmelCase						:							str			     =						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowerCAmelCase     ,	__lowerCAmelCase						:							Tuple			     =						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowerCAmelCase						:							Any			     =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCAmelCase						:							Optional[Any]			     =						get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)




def      __magic_name__							(					):






			'''simple docstring'''







			return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowerCAmelCase     ,	__lowerCAmelCase     ,	__lowerCAmelCase     ,	__lowerCAmelCase						:							List[str]			     =						accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
				accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
				accelerator.load_state(args.resume_from_checkpoint)
				__lowerCAmelCase						:							Dict			     =						os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				__lowerCAmelCase						:							Optional[Any]			     =						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
				dirs.sort(key=os.path.getctime)
				__lowerCAmelCase						:							List[Any]			     =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		__lowerCAmelCase						:							Dict			     =						os.path.splitext(path)[0]
		__lowerCAmelCase						:							List[Any]			     =						int(training_difference.replace('step_', ''))

# Train model
model.train()
__lowerCAmelCase						:							int			     =						0
__lowerCAmelCase						:							Union[str, Any]			     =						time.time()
__lowerCAmelCase						:							List[Any]			     =						0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		__lowerCAmelCase						:							List[str]			     =						model(batch, labels=batch, use_cache=False).loss
		__lowerCAmelCase						:							Any			     =						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
		__lowerCAmelCase						:							Tuple			     =						loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				__lowerCAmelCase						:							Optional[Any]			     =						get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				__lowerCAmelCase						:							str			     =						time.time() - t_start
				__lowerCAmelCase						:							Optional[Any]			     =						compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        'steps': completed_steps,
				        'loss/train': loss_tracking,
				        'lr': lr,
				        'tflops': tflops,
				        'time_per_iteration': elapsed_time,
				    },
				)
				__lowerCAmelCase						:							Dict			     =						time.time()
				__lowerCAmelCase						:							str			     =						0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info('Evaluating and saving model checkpoint')
				__lowerCAmelCase     ,	__lowerCAmelCase						:							Any			     =						evaluate(args)
				log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
				accelerator.wait_for_everyone()
				__lowerCAmelCase						:							Tuple			     =						os.path.join(args.save_dir, F'''step_{step}''')
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=F'''step {step}''')
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__lowerCAmelCase     ,	__lowerCAmelCase						:							str			     =						evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
__lowerCAmelCase						:							Optional[Any]			     =						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowerCAmelCase						:							Any			     =						os.path.join(args.save_dir, F'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message='final model')






