


'''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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 					lowercase_     (    a__  ,						a__  ,						a__  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	StableDiffusionInpaintPipeline
    __UpperCAmelCase   			=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    __UpperCAmelCase   			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    __UpperCAmelCase   			=	frozenset(
        []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __UpperCAmelCase   			=	frozenset([]   )


    def    __a							(     self    ):
          torch.manual_seed(0    )
          UpperCamelCase__										=				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  ,					attention_head_dim=(2, 4)  ,					use_linear_projection=a  ,					)
          UpperCamelCase__										=				PNDMScheduler(skip_prk_steps=a    )
          torch.manual_seed(0    )
          UpperCamelCase__										=				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  ,					sample_size=1_28  ,					)
          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  ,					)
          UpperCamelCase__										=				CLIPTextModel(a    )
          UpperCamelCase__										=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

          UpperCamelCase__										=				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components


    def    __a							(     self  ,					a  ,					a=0    ):
          # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
          UpperCamelCase__										=				floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(a    )    ).to(a    )
          UpperCamelCase__										=				image.cpu().permute(0  ,					2  ,					3  ,					1    )[0]
          UpperCamelCase__										=				Image.fromarray(np.uinta(a    )    ).convert("RGB"    ).resize((64, 64)    )
          UpperCamelCase__										=				Image.fromarray(np.uinta(image + 4    )    ).convert("RGB"    ).resize((64, 64)    )
          if str(a    ).startswith("mps"    ):
                UpperCamelCase__										=				torch.manual_seed(a    )
          else:
                UpperCamelCase__										=				torch.Generator(device=a    ).manual_seed(a    )
          UpperCamelCase__										=				{
              "prompt": "A painting of a squirrel eating a burger",
              "image": init_image,
              "mask_image": mask_image,
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }
          return inputs


    def    __a							(     self    ):
          UpperCamelCase__										=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          UpperCamelCase__										=				self.get_dummy_components()
          UpperCamelCase__										=				StableDiffusionInpaintPipeline(**a    )
          UpperCamelCase__										=				sd_pipe.to(a    )
          sd_pipe.set_progress_bar_config(disable=a    )

          UpperCamelCase__										=				self.get_dummy_inputs(a    )
          UpperCamelCase__										=				sd_pipe(**a    ).images
          UpperCamelCase__										=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCamelCase__										=				np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]    )

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


    def    __a							(     self    ):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3    )

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


    def    __a							(     self    ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def    __a							(     self    ):
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-inpaint/init_image.png"    )
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )
          UpperCamelCase__										=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
              "/yellow_cat_sitting_on_a_park_bench.npy"    )

          UpperCamelCase__										=				"stabilityai/stable-diffusion-2-inpainting"
          UpperCamelCase__										=				StableDiffusionInpaintPipeline.from_pretrained(a  ,					safety_checker=a    )
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          pipe.enable_attention_slicing()

          UpperCamelCase__										=				"Face of a yellow cat, high resolution, sitting on a park bench"

          UpperCamelCase__										=				torch.manual_seed(0    )
          UpperCamelCase__										=				pipe(
              prompt=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					output_type="np"  ,					)
          UpperCamelCase__										=				output.images[0]

          assert image.shape == (5_12, 5_12, 3)
          assert np.abs(expected_image - image    ).max() < 9e-3


    def    __a							(     self    ):
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-inpaint/init_image.png"    )
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )
          UpperCamelCase__										=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
              "/yellow_cat_sitting_on_a_park_bench_fp16.npy"    )

          UpperCamelCase__										=				"stabilityai/stable-diffusion-2-inpainting"
          UpperCamelCase__										=				StableDiffusionInpaintPipeline.from_pretrained(
              a  ,					torch_dtype=torch.floataa  ,					safety_checker=a  ,					)
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          pipe.enable_attention_slicing()

          UpperCamelCase__										=				"Face of a yellow cat, high resolution, sitting on a park bench"

          UpperCamelCase__										=				torch.manual_seed(0    )
          UpperCamelCase__										=				pipe(
              prompt=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					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    __a							(     self    ):
          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-inpaint/init_image.png"    )
          UpperCamelCase__										=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )

          UpperCamelCase__										=				"stabilityai/stable-diffusion-2-inpainting"
          UpperCamelCase__										=				PNDMScheduler.from_pretrained(a  ,					subfolder="scheduler"    )
          UpperCamelCase__										=				StableDiffusionInpaintPipeline.from_pretrained(
              a  ,					safety_checker=a  ,					scheduler=a  ,					torch_dtype=torch.floataa  ,					)
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          pipe.enable_attention_slicing(1    )
          pipe.enable_sequential_cpu_offload()

          UpperCamelCase__										=				"Face of a yellow cat, high resolution, sitting on a park bench"

          UpperCamelCase__										=				torch.manual_seed(0    )
          UpperCamelCase__										=				pipe(
              prompt=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					num_inference_steps=2  ,					output_type="np"  ,					)

          UpperCamelCase__										=				torch.cuda.max_memory_allocated()
          # make sure that less than 2.65 GB is allocated
          assert mem_bytes < 2.65 * 10**9










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()
A__  :  List[Any]								=					logging.get_logger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Any									= DPTConfig(embedding_type='''hybrid''' )

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

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

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

		return config, expected_shape


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		lowerCAmelCase_			:     int									= ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
		for k in ignore_keys:
				state_dict.pop(__UpperCamelCase     ,__UpperCamelCase )


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

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

		if ".." in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''..'''     ,'''.''' )

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


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


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_			:     Dict									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw )
		return im


@torch.no_grad()
def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		str ):
		lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= get_dpt_config(__UpperCamelCase )
		# load original state_dict from URL
		# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
		lowerCAmelCase_			:     List[str]									= torch.load(__UpperCamelCase     ,map_location='''cpu''' )
		# remove certain keys
		remove_ignore_keys_(__UpperCamelCase )
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase_			:     Any									= state_dict.pop(__UpperCamelCase )
				lowerCAmelCase_			:     Optional[Any]									= val
		# read in qkv matrices
		read_in_q_k_v(__UpperCamelCase     ,__UpperCamelCase )

		# load HuggingFace model
		lowerCAmelCase_			:     List[Any]									= DPTForSemanticSegmentation(__UpperCamelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCamelCase )
		model.load_state_dict(__UpperCamelCase )
		model.eval()

		# Check outputs on an image
		lowerCAmelCase_			:     Tuple									= 480 if '''ade''' in checkpoint_url else 384
		lowerCAmelCase_			:     Optional[int]									= DPTImageProcessor(size=__UpperCamelCase )

		lowerCAmelCase_			:     Union[str, Any]									= prepare_img()
		lowerCAmelCase_			:     str									= image_processor(__UpperCamelCase     ,return_tensors='''pt''' )

		# forward pass
		lowerCAmelCase_			:     Tuple									= model(**__UpperCamelCase ).logits if '''ade''' in checkpoint_url else model(**__UpperCamelCase ).predicted_depth

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

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

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

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


if __name__ == "__main__":
						A__  :  Optional[int]								=					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''',
						)

						A__  :  Union[str, 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
						)





