



"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			DPTConfig(embedding_type='hybrid'						)

						if "large" in checkpoint_url:
												UpperCAmelCase					=			1024
												UpperCAmelCase					=			4096
												UpperCAmelCase					=			24
												UpperCAmelCase					=			16
												UpperCAmelCase					=			[5, 11, 17, 23]
												UpperCAmelCase					=			[256, 512, 1024, 1024]
												UpperCAmelCase					=			(1, 384, 384)

						if "nyu" or "midas" in checkpoint_url:
												UpperCAmelCase					=			768
												UpperCAmelCase					=			[1, 1, 1, 0.5]
												UpperCAmelCase					=			[256, 512, 768, 768]
												UpperCAmelCase					=			150
												UpperCAmelCase					=			16
												UpperCAmelCase					=			(1, 384, 384)
												UpperCAmelCase					=			False
												UpperCAmelCase					=			'project'

						if "ade" in checkpoint_url:
												UpperCAmelCase					=			True
												UpperCAmelCase					=			768
												UpperCAmelCase					=			[1, 1, 1, 0.5]
												UpperCAmelCase					=			150
												UpperCAmelCase					=			16
												UpperCAmelCase					=			'huggingface/label-files'
												UpperCAmelCase					=			'ade20k-id2label.json'
												UpperCAmelCase					=			json.load(open(cached_download(hf_hub_url(lowercase_		,     lowercase_		,     repo_type='dataset'						)						)		,     'r'						)						)
												UpperCAmelCase					=			{int(lowercase_						): v for k, v in idalabel.items()}
												UpperCAmelCase					=			idalabel
												UpperCAmelCase					=			{v: k for k, v in idalabel.items()}
												UpperCAmelCase					=			[1, 150, 480, 480]

						return config, expected_shape

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			['pretrained.model.head.weight', 'pretrained.model.head.bias']
						for k in ignore_keys:
												state_dict.pop(lowercase_		,     lowercase_						)

def 			_lowerCAmelCase				(       lowercase_						):
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												UpperCAmelCase					=			name.replace('pretrained.model'		,     'dpt.encoder'						)
						if "pretrained.model" in name:
												UpperCAmelCase					=			name.replace('pretrained.model'		,     'dpt.embeddings'						)
						if "patch_embed" in name:
												UpperCAmelCase					=			name.replace('patch_embed'		,     ''						)
						if "pos_embed" in name:
												UpperCAmelCase					=			name.replace('pos_embed'		,     'position_embeddings'						)
						if "attn.proj" in name:
												UpperCAmelCase					=			name.replace('attn.proj'		,     'attention.output.dense'						)
						if "proj" in name and "project" not in name:
												UpperCAmelCase					=			name.replace('proj'		,     'projection'						)
						if "blocks" in name:
												UpperCAmelCase					=			name.replace('blocks'		,     'layer'						)
						if "mlp.fc1" in name:
												UpperCAmelCase					=			name.replace('mlp.fc1'		,     'intermediate.dense'						)
						if "mlp.fc2" in name:
												UpperCAmelCase					=			name.replace('mlp.fc2'		,     'output.dense'						)
						if "norm1" in name and "backbone" not in name:
												UpperCAmelCase					=			name.replace('norm1'		,     'layernorm_before'						)
						if "norm2" in name and "backbone" not in name:
												UpperCAmelCase					=			name.replace('norm2'		,     'layernorm_after'						)
						if "scratch.output_conv" in name:
												UpperCAmelCase					=			name.replace('scratch.output_conv'		,     'head'						)
						if "scratch" in name:
												UpperCAmelCase					=			name.replace('scratch'		,     'neck'						)
						if "layer1_rn" in name:
												UpperCAmelCase					=			name.replace('layer1_rn'		,     'convs.0'						)
						if "layer2_rn" in name:
												UpperCAmelCase					=			name.replace('layer2_rn'		,     'convs.1'						)
						if "layer3_rn" in name:
												UpperCAmelCase					=			name.replace('layer3_rn'		,     'convs.2'						)
						if "layer4_rn" in name:
												UpperCAmelCase					=			name.replace('layer4_rn'		,     'convs.3'						)
						if "refinenet" in name:
												UpperCAmelCase					=			int(name[len('neck.refinenet'						) : len('neck.refinenet'						) + 1]						)
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												UpperCAmelCase					=			name.replace(F"""refinenet{layer_idx}"""		,     F"""fusion_stage.layers.{abs(layer_idx-4						)}"""						)
						if "out_conv" in name:
												UpperCAmelCase					=			name.replace('out_conv'		,     'projection'						)
						if "resConfUnit1" in name:
												UpperCAmelCase					=			name.replace('resConfUnit1'		,     'residual_layer1'						)
						if "resConfUnit2" in name:
												UpperCAmelCase					=			name.replace('resConfUnit2'		,     'residual_layer2'						)
						if "conv1" in name:
												UpperCAmelCase					=			name.replace('conv1'		,     'convolution1'						)
						if "conv2" in name:
												UpperCAmelCase					=			name.replace('conv2'		,     'convolution2'						)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess1.0.project.0'		,     'neck.reassemble_stage.readout_projects.0.0'						)
						if "pretrained.act_postprocess2.0.project.0" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess2.0.project.0'		,     'neck.reassemble_stage.readout_projects.1.0'						)
						if "pretrained.act_postprocess3.0.project.0" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess3.0.project.0'		,     'neck.reassemble_stage.readout_projects.2.0'						)
						if "pretrained.act_postprocess4.0.project.0" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess4.0.project.0'		,     'neck.reassemble_stage.readout_projects.3.0'						)

						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess1.3'		,     'neck.reassemble_stage.layers.0.projection'						)
						if "pretrained.act_postprocess1.4" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess1.4'		,     'neck.reassemble_stage.layers.0.resize'						)
						if "pretrained.act_postprocess2.3" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess2.3'		,     'neck.reassemble_stage.layers.1.projection'						)
						if "pretrained.act_postprocess2.4" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess2.4'		,     'neck.reassemble_stage.layers.1.resize'						)
						if "pretrained.act_postprocess3.3" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess3.3'		,     'neck.reassemble_stage.layers.2.projection'						)
						if "pretrained.act_postprocess4.3" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess4.3'		,     'neck.reassemble_stage.layers.3.projection'						)
						if "pretrained.act_postprocess4.4" in name:
												UpperCAmelCase					=			name.replace('pretrained.act_postprocess4.4'		,     'neck.reassemble_stage.layers.3.resize'						)
						if "pretrained" in name:
												UpperCAmelCase					=			name.replace('pretrained'		,     'dpt'						)
						if "bn" in name:
												UpperCAmelCase					=			name.replace('bn'		,     'batch_norm'						)
						if "head" in name:
												UpperCAmelCase					=			name.replace('head'		,     'head.head'						)
						if "encoder.norm" in name:
												UpperCAmelCase					=			name.replace('encoder.norm'		,     'layernorm'						)
						if "auxlayer" in name:
												UpperCAmelCase					=			name.replace('auxlayer'		,     'auxiliary_head.head'						)
						if "backbone" in name:
												UpperCAmelCase					=			name.replace('backbone'		,     'backbone.bit.encoder'						)

						if ".." in name:
												UpperCAmelCase					=			name.replace('..'		,     '.'						)

						if "stem.conv" in name:
												UpperCAmelCase					=			name.replace('stem.conv'		,     'bit.embedder.convolution'						)
						if "blocks" in name:
												UpperCAmelCase					=			name.replace('blocks'		,     'layers'						)
						if "convolution" in name and "backbone" in name:
												UpperCAmelCase					=			name.replace('convolution'		,     'conv'						)
						if "layer" in name and "backbone" in name:
												UpperCAmelCase					=			name.replace('layer'		,     'layers'						)
						if "backbone.bit.encoder.bit" in name:
												UpperCAmelCase					=			name.replace('backbone.bit.encoder.bit'		,     'backbone.bit'						)
						if "embedder.conv" in name:
												UpperCAmelCase					=			name.replace('embedder.conv'		,     'embedder.convolution'						)
						if "backbone.bit.encoder.stem.norm" in name:
												UpperCAmelCase					=			name.replace('backbone.bit.encoder.stem.norm'		,     'backbone.bit.embedder.norm'						)
						return name

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						for i in range(config.num_hidden_layers						):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												UpperCAmelCase					=			state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""						)
												UpperCAmelCase					=			state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""						)
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase					=			in_proj_weight[: config.hidden_size, :]
												UpperCAmelCase					=			in_proj_bias[: config.hidden_size]
												UpperCAmelCase					=			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCAmelCase					=			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												UpperCAmelCase					=			in_proj_weight[
												    -config.hidden_size :, :
												]
												UpperCAmelCase					=			in_proj_bias[-config.hidden_size :]

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						UpperCAmelCase					=			Image.open(requests.get(lowercase_		,     stream=lowercase_						).raw						)
						return im

@torch.no_grad()
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase				,   UpperCAmelCase					=			get_dpt_config(lowercase_						)
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						UpperCAmelCase					=			torch.load(lowercase_		,     map_location='cpu'						)
						# remove certain keys
						remove_ignore_keys_(lowercase_						)
						# rename keys
						for key in state_dict.copy().keys():
												UpperCAmelCase					=			state_dict.pop(lowercase_						)
												UpperCAmelCase					=			val
						# read in qkv matrices
						read_in_q_k_v(lowercase_		,     lowercase_						)

						# load HuggingFace model
						UpperCAmelCase					=			DPTForSemanticSegmentation(lowercase_						) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase_						)
						model.load_state_dict(lowercase_						)
						model.eval()

						# Check outputs on an image
						UpperCAmelCase					=			480 if 'ade' in checkpoint_url else 384
						UpperCAmelCase					=			DPTImageProcessor(size=lowercase_						)

						UpperCAmelCase					=			prepare_img()
						UpperCAmelCase					=			image_processor(lowercase_		,     return_tensors='pt'						)

						# forward pass
						UpperCAmelCase					=			model(**lowercase_						).logits if 'ade' in checkpoint_url else model(**lowercase_						).predicted_depth

						if show_prediction:
												UpperCAmelCase					=			(
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1						)		,     size=(image.size[1], image.size[0])		,     mode='bicubic'		,     align_corners=lowercase_		,     )
												    .squeeze()
												    .cpu()
												    .numpy()
												)

												Image.fromarray((prediction / prediction.max()) * 255						).show()

						if pytorch_dump_folder_path is not None:
												Path(lowercase_						).mkdir(exist_ok=lowercase_						)
												print(F"""Saving model to {pytorch_dump_folder_path}"""						)
												model.save_pretrained(lowercase_						)
												print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
												image_processor.save_pretrained(lowercase_						)

						if push_to_hub:
												model.push_to_hub('ybelkada/dpt-hybrid-midas'						)
												image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'						)


if __name__ == "__main__":
				snake_case_    					=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--checkpoint_url""",
				    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
				    type=str,
				    help="""URL of the original DPT checkpoint you'd like to convert.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""",
				    default=None,
				    type=str,
				    required=False,
				    help="""Path to the output PyTorch model directory.""",
				)
				parser.add_argument(
				    """--push_to_hub""",
				    action="""store_true""",
				)
				parser.add_argument(
				    """--model_name""",
				    default="""dpt-large""",
				    type=str,
				    help="""Name of the model, in case you're pushing to the hub.""",
				)
				parser.add_argument(
				    """--show_prediction""",
				    action="""store_true""",
				)

				snake_case_    					=	parser.parse_args()
				convert_dpt_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
				)










"""simple docstring"""


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						@property
						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			1
												UpperCAmelCase					=			3
												UpperCAmelCase					=			(32, 32)

												UpperCAmelCase					=			floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0 ) ).to(lowercase_ )
												return image






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Tuple:
												torch.manual_seed(0 )
												UpperCAmelCase					=			UNetaDConditionModel(
												    block_out_channels=(32, 32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=7       ,  out_channels=4       ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')       ,  up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')       ,  cross_attention_dim=32       ,  attention_head_dim=8       ,  use_linear_projection=lowercase_       ,  only_cross_attention=(True, True, False)       ,  num_class_embeds=1_00       ,  )
												return model






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Any:
												torch.manual_seed(0 )
												UpperCAmelCase					=			AutoencoderKL(
												    block_out_channels=[32, 32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']       ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']       ,  latent_channels=4       ,  )
												return model






						@property
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			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       ,  hidden_act='gelu'       ,  projection_dim=5_12       ,  )
												return CLIPTextModel(lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )

												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  return_dict=lowercase_       ,  )[0]

												UpperCAmelCase					=			image[0, -3:, -3:, -1]
												UpperCAmelCase					=			image_from_tuple[0, -3:, -3:, -1]

												UpperCAmelCase					=			low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)
												UpperCAmelCase					=			np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Any:
												UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			sd_pipe(
												    2 * [prompt]       ,  image=2 * [low_res_image]       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images
												assert image.shape[0] == 2

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_images_per_prompt=2       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images
												assert image.shape[0] == 2






						@unittest.skipIf(torch_device != 'cuda'       ,  'This test requires a GPU' )
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# put models in fp16, except vae as it overflows in fp16
												UpperCAmelCase					=			unet.half()
												UpperCAmelCase					=			text_encoder.half()

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=2       ,  output_type='np'       ,  ).images

												UpperCAmelCase					=			low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)







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



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Dict )    ->      str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      int:
												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat.npy' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(lowercase_ )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image ).max() < 1E-3






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat_fp16.npy' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
												    lowercase_       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image ).max() < 5E-1






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
												    lowercase_       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing(1 )
												pipe.enable_sequential_cpu_offload()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=5       ,  output_type='np'       ,  )

												UpperCAmelCase					=			torch.cuda.max_memory_allocated()
												# make sure that less than 2.9 GB is allocated
												assert mem_bytes < 2.9 * 10**9





