






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()
__magic_name__:					int							=     logging.get_logger(__name__)

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Optional[Any]       						= DPTConfig(embedding_type="""hybrid"""			)

						if "large" in checkpoint_url:
												__magic_name__      :       int       						= 1024
												__magic_name__      :       Optional[int]       						= 4096
												__magic_name__      :       int       						= 24
												__magic_name__      :       Union[str, Any]       						= 16
												__magic_name__      :       Optional[Any]       						= [5, 11, 17, 23]
												__magic_name__      :       Tuple       						= [256, 512, 1024, 1024]
												__magic_name__      :       int       						= (1, 384, 384)

						if "nyu" or "midas" in checkpoint_url:
												__magic_name__      :       int       						= 768
												__magic_name__      :       Optional[Any]       						= [1, 1, 1, 0.5]
												__magic_name__      :       int       						= [256, 512, 768, 768]
												__magic_name__      :       List[Any]       						= 150
												__magic_name__      :       Optional[Any]       						= 16
												__magic_name__      :       Tuple       						= (1, 384, 384)
												__magic_name__      :       Tuple       						= False
												__magic_name__      :       Union[str, Any]       						= """project"""

						if "ade" in checkpoint_url:
												__magic_name__      :       List[str]       						= True
												__magic_name__      :       Optional[int]       						= 768
												__magic_name__      :       Any       						= [1, 1, 1, 0.5]
												__magic_name__      :       List[Any]       						= 150
												__magic_name__      :       int       						= 16
												__magic_name__      :       Optional[int]       						= """huggingface/label-files"""
												__magic_name__      :       Tuple       						= """ade20k-id2label.json"""
												__magic_name__      :       int       						= json.load(open(cached_download(hf_hub_url(_A,   _A,   repo_type="""dataset"""			)			),   """r"""			)			)
												__magic_name__      :       List[str]       						= {int(_A			): v for k, v in idalabel.items()}
												__magic_name__      :       List[Any]       						= idalabel
												__magic_name__      :       int       						= {v: k for k, v in idalabel.items()}
												__magic_name__      :       Union[str, Any]       						= [1, 150, 480, 480]

						return config, expected_shape

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Optional[Any]       						= ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(_A,   _A			)

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												__magic_name__      :       Dict       						= name.replace("""pretrained.model""",   """dpt.encoder"""			)
						if "pretrained.model" in name:
												__magic_name__      :       Optional[Any]       						= name.replace("""pretrained.model""",   """dpt.embeddings"""			)
						if "patch_embed" in name:
												__magic_name__      :       Any       						= name.replace("""patch_embed""",   """"""			)
						if "pos_embed" in name:
												__magic_name__      :       List[Any]       						= name.replace("""pos_embed""",   """position_embeddings"""			)
						if "attn.proj" in name:
												__magic_name__      :       str       						= name.replace("""attn.proj""",   """attention.output.dense"""			)
						if "proj" in name and "project" not in name:
												__magic_name__      :       List[str]       						= name.replace("""proj""",   """projection"""			)
						if "blocks" in name:
												__magic_name__      :       Optional[int]       						= name.replace("""blocks""",   """layer"""			)
						if "mlp.fc1" in name:
												__magic_name__      :       Any       						= name.replace("""mlp.fc1""",   """intermediate.dense"""			)
						if "mlp.fc2" in name:
												__magic_name__      :       Tuple       						= name.replace("""mlp.fc2""",   """output.dense"""			)
						if "norm1" in name and "backbone" not in name:
												__magic_name__      :       List[str]       						= name.replace("""norm1""",   """layernorm_before"""			)
						if "norm2" in name and "backbone" not in name:
												__magic_name__      :       Optional[Any]       						= name.replace("""norm2""",   """layernorm_after"""			)
						if "scratch.output_conv" in name:
												__magic_name__      :       Any       						= name.replace("""scratch.output_conv""",   """head"""			)
						if "scratch" in name:
												__magic_name__      :       Optional[int]       						= name.replace("""scratch""",   """neck"""			)
						if "layer1_rn" in name:
												__magic_name__      :       str       						= name.replace("""layer1_rn""",   """convs.0"""			)
						if "layer2_rn" in name:
												__magic_name__      :       Any       						= name.replace("""layer2_rn""",   """convs.1"""			)
						if "layer3_rn" in name:
												__magic_name__      :       Optional[Any]       						= name.replace("""layer3_rn""",   """convs.2"""			)
						if "layer4_rn" in name:
												__magic_name__      :       Dict       						= name.replace("""layer4_rn""",   """convs.3"""			)
						if "refinenet" in name:
												__magic_name__      :       List[Any]       						= 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
												__magic_name__      :       Dict       						= name.replace(f'refinenet{layer_idx}',   f'fusion_stage.layers.{abs(layer_idx-4			)}'			)
						if "out_conv" in name:
												__magic_name__      :       Any       						= name.replace("""out_conv""",   """projection"""			)
						if "resConfUnit1" in name:
												__magic_name__      :       Optional[int]       						= name.replace("""resConfUnit1""",   """residual_layer1"""			)
						if "resConfUnit2" in name:
												__magic_name__      :       Optional[int]       						= name.replace("""resConfUnit2""",   """residual_layer2"""			)
						if "conv1" in name:
												__magic_name__      :       Union[str, Any]       						= name.replace("""conv1""",   """convolution1"""			)
						if "conv2" in name:
												__magic_name__      :       Optional[Any]       						= name.replace("""conv2""",   """convolution2"""			)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												__magic_name__      :       str       						= name.replace("""pretrained.act_postprocess1.0.project.0""",   """neck.reassemble_stage.readout_projects.0.0"""			)
						if "pretrained.act_postprocess2.0.project.0" in name:
												__magic_name__      :       List[str]       						= name.replace("""pretrained.act_postprocess2.0.project.0""",   """neck.reassemble_stage.readout_projects.1.0"""			)
						if "pretrained.act_postprocess3.0.project.0" in name:
												__magic_name__      :       Optional[int]       						= name.replace("""pretrained.act_postprocess3.0.project.0""",   """neck.reassemble_stage.readout_projects.2.0"""			)
						if "pretrained.act_postprocess4.0.project.0" in name:
												__magic_name__      :       int       						= name.replace("""pretrained.act_postprocess4.0.project.0""",   """neck.reassemble_stage.readout_projects.3.0"""			)

						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												__magic_name__      :       List[str]       						= name.replace("""pretrained.act_postprocess1.3""",   """neck.reassemble_stage.layers.0.projection"""			)
						if "pretrained.act_postprocess1.4" in name:
												__magic_name__      :       List[Any]       						= name.replace("""pretrained.act_postprocess1.4""",   """neck.reassemble_stage.layers.0.resize"""			)
						if "pretrained.act_postprocess2.3" in name:
												__magic_name__      :       Optional[int]       						= name.replace("""pretrained.act_postprocess2.3""",   """neck.reassemble_stage.layers.1.projection"""			)
						if "pretrained.act_postprocess2.4" in name:
												__magic_name__      :       Dict       						= name.replace("""pretrained.act_postprocess2.4""",   """neck.reassemble_stage.layers.1.resize"""			)
						if "pretrained.act_postprocess3.3" in name:
												__magic_name__      :       List[str]       						= name.replace("""pretrained.act_postprocess3.3""",   """neck.reassemble_stage.layers.2.projection"""			)
						if "pretrained.act_postprocess4.3" in name:
												__magic_name__      :       Any       						= name.replace("""pretrained.act_postprocess4.3""",   """neck.reassemble_stage.layers.3.projection"""			)
						if "pretrained.act_postprocess4.4" in name:
												__magic_name__      :       Union[str, Any]       						= name.replace("""pretrained.act_postprocess4.4""",   """neck.reassemble_stage.layers.3.resize"""			)
						if "pretrained" in name:
												__magic_name__      :       Optional[Any]       						= name.replace("""pretrained""",   """dpt"""			)
						if "bn" in name:
												__magic_name__      :       Any       						= name.replace("""bn""",   """batch_norm"""			)
						if "head" in name:
												__magic_name__      :       Dict       						= name.replace("""head""",   """head.head"""			)
						if "encoder.norm" in name:
												__magic_name__      :       Optional[Any]       						= name.replace("""encoder.norm""",   """layernorm"""			)
						if "auxlayer" in name:
												__magic_name__      :       Optional[Any]       						= name.replace("""auxlayer""",   """auxiliary_head.head"""			)
						if "backbone" in name:
												__magic_name__      :       List[Any]       						= name.replace("""backbone""",   """backbone.bit.encoder"""			)

						if ".." in name:
												__magic_name__      :       Union[str, Any]       						= name.replace("""..""",   """."""			)

						if "stem.conv" in name:
												__magic_name__      :       Optional[int]       						= name.replace("""stem.conv""",   """bit.embedder.convolution"""			)
						if "blocks" in name:
												__magic_name__      :       List[str]       						= name.replace("""blocks""",   """layers"""			)
						if "convolution" in name and "backbone" in name:
												__magic_name__      :       Union[str, Any]       						= name.replace("""convolution""",   """conv"""			)
						if "layer" in name and "backbone" in name:
												__magic_name__      :       Dict       						= name.replace("""layer""",   """layers"""			)
						if "backbone.bit.encoder.bit" in name:
												__magic_name__      :       Union[str, Any]       						= name.replace("""backbone.bit.encoder.bit""",   """backbone.bit"""			)
						if "embedder.conv" in name:
												__magic_name__      :       Tuple       						= name.replace("""embedder.conv""",   """embedder.convolution"""			)
						if "backbone.bit.encoder.stem.norm" in name:
												__magic_name__      :       int       						= name.replace("""backbone.bit.encoder.stem.norm""",   """backbone.bit.embedder.norm"""			)
						return name

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						for i in range(config.num_hidden_layers			):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												__magic_name__      :       Union[str, Any]       						= state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'			)
												__magic_name__      :       int       						= state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'			)
												# next, add query, keys and values (in that order) to the state dict
												__magic_name__      :       Optional[Any]       						= in_proj_weight[: config.hidden_size, :]
												__magic_name__      :       Optional[Any]       						= in_proj_bias[: config.hidden_size]
												__magic_name__      :       Union[str, Any]       						= in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__magic_name__      :       Any       						= in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												__magic_name__      :       Tuple       						= in_proj_weight[
												    -config.hidden_size :, :
												]
												__magic_name__      :       Optional[Any]       						= in_proj_bias[-config.hidden_size :]

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Tuple       						= """http://images.cocodataset.org/val2017/000000039769.jpg"""
						__magic_name__      :       Dict       						= Image.open(requests.get(_A,   stream=_A			).raw			)
						return im

@torch.no_grad()
def    UpperCamelCase						(	_A,   _A,   _A,   _A,   _A			):






						"""simple docstring"""







						__magic_name__						,__magic_name__      :       List[Any]       						= get_dpt_config(_A			)
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						__magic_name__      :       int       						= torch.load(_A,   map_location="""cpu"""			)
						# remove certain keys
						remove_ignore_keys_(_A			)
						# rename keys
						for key in state_dict.copy().keys():
												__magic_name__      :       List[str]       						= state_dict.pop(_A			)
												__magic_name__      :       str       						= val
						# read in qkv matrices
						read_in_q_k_v(_A,   _A			)

						# load HuggingFace model
						__magic_name__      :       Any       						= DPTForSemanticSegmentation(_A			) if """ade""" in checkpoint_url else DPTForDepthEstimation(_A			)
						model.load_state_dict(_A			)
						model.eval()

						# Check outputs on an image
						__magic_name__      :       Optional[Any]       						= 480 if """ade""" in checkpoint_url else 384
						__magic_name__      :       Any       						= DPTImageProcessor(size=_A			)

						__magic_name__      :       Union[str, Any]       						= prepare_img()
						__magic_name__      :       Tuple       						= image_processor(_A,   return_tensors="""pt"""			)

						# forward pass
						__magic_name__      :       Dict       						= model(**_A			).logits if """ade""" in checkpoint_url else model(**_A			).predicted_depth

						if show_prediction:
												__magic_name__      :       List[str]       						= (
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1			),   size=(image.size[1], image.size[0]),   mode="""bicubic""",   align_corners=_A,   )
												    .squeeze()
												    .cpu()
												    .numpy()
												)

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

						if pytorch_dump_folder_path is not None:
												Path(_A			).mkdir(exist_ok=_A			)
												print(f'Saving model to {pytorch_dump_folder_path}'			)
												model.save_pretrained(_A			)
												print(f'Saving image processor to {pytorch_dump_folder_path}'			)
												image_processor.save_pretrained(_A			)

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


if __name__ == "__main__":
		__magic_name__:					Tuple							=     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",
		)

		__magic_name__:					Any							=     parser.parse_args()
		convert_dpt_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
		)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					str											=   StableUnCLIPImgaImgPipeline
	lowercase__						:					Any											=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	lowercase__						:					Tuple											=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowercase__						:					Union[str, Any]											=   frozenset(
	    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase__						:					Union[str, Any]											=   frozenset([]				)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Any       						= 32
							__magic_name__      :       Union[str, Any]       						= embedder_hidden_size

							# image encoding components

							__magic_name__      :       Optional[int]       						= CLIPImageProcessor(crop_size=32      ,       size=32			)

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= CLIPVisionModelWithProjection(
							    CLIPVisionConfig(
							        hidden_size=lowerCAmelCase__      ,       projection_dim=lowerCAmelCase__      ,       num_hidden_layers=5      ,       num_attention_heads=4      ,       image_size=32      ,       intermediate_size=37      ,       patch_size=1      ,       )			)

							# regular denoising components

							torch.manual_seed(0			)
							__magic_name__      :       Any       						= StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase__			)
							__magic_name__      :       int       						= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""			)

							torch.manual_seed(0			)
							__magic_name__      :       Tuple       						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=lowerCAmelCase__      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=10_00      ,       )			)

							torch.manual_seed(0			)
							__magic_name__      :       str       						= UNetaDConditionModel(
							    sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      ,       block_out_channels=(32, 64)      ,       attention_head_dim=(2, 4)      ,       class_embed_type="""projection"""      ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,       cross_attention_dim=lowerCAmelCase__      ,       layers_per_block=1      ,       upcast_attention=lowerCAmelCase__      ,       use_linear_projection=lowerCAmelCase__      ,       )

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= DDIMScheduler(
							    beta_schedule="""scaled_linear"""      ,       beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       prediction_type="""v_prediction"""      ,       set_alpha_to_one=lowerCAmelCase__      ,       steps_offset=1      ,       )

							torch.manual_seed(0			)
							__magic_name__      :       List[str]       						= AutoencoderKL()

							__magic_name__      :       List[str]       						= {
							    # image encoding components
							    """feature_extractor""": feature_extractor,
							    """image_encoder""": image_encoder.eval(),
							    # image noising components
							    """image_normalizer""": image_normalizer.eval(),
							    """image_noising_scheduler""": image_noising_scheduler,
							    # regular denoising components
							    """tokenizer""": tokenizer,
							    """text_encoder""": text_encoder.eval(),
							    """unet""": unet.eval(),
							    """scheduler""": scheduler,
							    """vae""": vae.eval(),
							}

							return components
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=0      ,       lowerCAmelCase__=True			)							->				List[Any]:
							if str(lowerCAmelCase__			).startswith("""mps"""			):
													__magic_name__      :       Optional[int]       						= torch.manual_seed(lowerCAmelCase__			)
							else:
													__magic_name__      :       Optional[int]       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)

							__magic_name__      :       Any       						= floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(lowerCAmelCase__			)			).to(lowerCAmelCase__			)

							if pil_image:
													__magic_name__      :       Optional[Any]       						= input_image * 0.5 + 0.5
													__magic_name__      :       int       						= input_image.clamp(0      ,       1			)
													__magic_name__      :       Union[str, Any]       						= input_image.cpu().permute(0      ,       2      ,       3      ,       1			).float().numpy()
													__magic_name__      :       Optional[int]       						= DiffusionPipeline.numpy_to_pil(lowerCAmelCase__			)[0]

							return {
							    "prompt": "An anime racoon running a marathon",
							    "image": input_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}
	@skip_mps
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
							__magic_name__      :       List[str]       						= self.get_dummy_components()
							__magic_name__      :       int       						= StableUnCLIPImgaImgPipeline(**lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= sd_pipe.to(lowerCAmelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							__magic_name__      :       Any       						= self.get_dummy_inputs(lowerCAmelCase__			)
							inputs.update({"""image_embeds""": None}			)
							__magic_name__      :       List[str]       						= sd_pipe(**lowerCAmelCase__			).images
							__magic_name__      :       Dict       						= image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							__magic_name__      :       int       						= np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= torch_device in ["""cpu""", """mps"""]

							self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Tuple       						= torch_device in ["""cpu""", """mps"""]

							self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase__			)







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





@slow
@require_torch_gpu
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				int:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							__magic_name__      :       int       						= load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"""			)

							__magic_name__      :       List[Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-l-img2img"""      ,       torch_dtype=torch.floataa			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       Union[str, Any]       						= torch.Generator(device="""cpu"""			).manual_seed(0			)
							__magic_name__      :       Union[str, Any]       						= pipe(lowerCAmelCase__      ,       """anime turle"""      ,       generator=lowerCAmelCase__      ,       output_type="""np"""			)

							__magic_name__      :       Optional[Any]       						= output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Union[str, Any]       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							__magic_name__      :       Dict       						= load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"""			)

							__magic_name__      :       Union[str, Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""      ,       torch_dtype=torch.floataa			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       List[str]       						= torch.Generator(device="""cpu"""			).manual_seed(0			)
							__magic_name__      :       Any       						= pipe(lowerCAmelCase__      ,       """anime turle"""      ,       generator=lowerCAmelCase__      ,       output_type="""np"""			)

							__magic_name__      :       Any       						= output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(lowerCAmelCase__      ,       lowerCAmelCase__			)







	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Any       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							__magic_name__      :       Union[str, Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""      ,       torch_dtype=torch.floataa			)
							__magic_name__      :       int       						= pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       List[Any]       						= pipe(
							    lowerCAmelCase__      ,       """anime turtle"""      ,       num_inference_steps=2      ,       output_type="""np"""      ,       )

							__magic_name__      :       List[str]       						= torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9
