



'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()



class 							lowercase       (  _lowerCamelCase					,					_lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  StableDiffusionXLImgaImgPipeline
 UpperCAmelCase							=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
 UpperCAmelCase							=  PipelineTesterMixin.required_optional_params - {"""latents"""}
 UpperCAmelCase							=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 UpperCAmelCase							=  IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase							=  IMAGE_TO_IMAGE_IMAGE_PARAMS







 def _snake_case (							self							)		->   Union[str, Any]:
        torch.manual_seed(0							)
        _UpperCAmelCase  :						Any			     = 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""")  ,attention_head_dim=(2, 4)  ,use_linear_projection=a_  ,addition_embed_type="""text_time"""  ,addition_time_embed_dim=8  ,transformer_layers_per_block=(1, 2)  ,projection_class_embeddings_input_dim=80  ,cross_attention_dim=64  ,)
        _UpperCAmelCase  :						List[str]			     = EulerDiscreteScheduler(
            beta_start=0.0_0085  ,beta_end=0.012  ,steps_offset=1  ,beta_schedule="""scaled_linear"""  ,timestep_spacing="""leading"""  ,)
        torch.manual_seed(0							)
        _UpperCAmelCase  :						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  ,sample_size=128  ,)
        torch.manual_seed(0							)
        _UpperCAmelCase  :						List[Any]			     = CLIPTextConfig(
            bos_token_id=0  ,eos_token_id=2  ,hidden_size=32  ,intermediate_size=37  ,layer_norm_eps=1E-0_5  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1_000  ,hidden_act="""gelu"""  ,projection_dim=32  ,)
        _UpperCAmelCase  :						Union[str, Any]			     = CLIPTextModel(a_							)
        _UpperCAmelCase  :						str			     = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  ,local_files_only=a_							)

        _UpperCAmelCase  :						str			     = CLIPTextModelWithProjection(a_							)
        _UpperCAmelCase  :						Optional[Any]			     = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  ,local_files_only=a_							)

        _UpperCAmelCase  :						Optional[int]			     = {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """text_encoder_2""": text_encoder_a,
            """tokenizer_2""": tokenizer_a,
            # "safety_checker": None,
            # "feature_extractor": None,
        }
        return components







 def _snake_case (							self  ,a_  ,a_=0							)		->   int:
        _UpperCAmelCase  :						str			     = floats_tensor((1, 3, 32, 32)  ,rng=random.Random(a_							)							).to(a_							)
        _UpperCAmelCase  :						Dict			     = image / 2 + 0.5
        if str(a_							).startswith("""mps"""							):
               _UpperCAmelCase  :						List[Any]			     = torch.manual_seed(a_							)
        else:
               _UpperCAmelCase  :						Optional[Any]			     = torch.Generator(device=a_							).manual_seed(a_							)
        _UpperCAmelCase  :						List[str]			     = {
            """prompt""": """A painting of a squirrel eating a burger""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 5.0,
            """output_type""": """numpy""",
            """strength""": 0.75,
        }
        return inputs







 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						int			     = """cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase  :						Optional[int]			     = self.get_dummy_components()
        _UpperCAmelCase  :						str			     = StableDiffusionXLImgaImgPipeline(**a_							)
        _UpperCAmelCase  :						Any			     = sd_pipe.to(a_							)
        sd_pipe.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						Optional[Any]			     = self.get_dummy_inputs(a_							)
        _UpperCAmelCase  :						Optional[Any]			     = sd_pipe(**a_							).images
        _UpperCAmelCase  :						Tuple			     = image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)

        _UpperCAmelCase  :						List[str]			     = np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]							)

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







 def _snake_case (							self							)		->   Optional[int]:
        super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)







 def _snake_case (							self							)		->   int:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







 def _snake_case (							self							)		->   Tuple:
        pass







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						str			     = self.get_dummy_components()
        _UpperCAmelCase  :						Tuple			     = StableDiffusionXLImgaImgPipeline(**a_							)
        _UpperCAmelCase  :						Any			     = sd_pipe.to(a_							)
        _UpperCAmelCase  :						Optional[int]			     = sd_pipe.to(a_							)
        sd_pipe.set_progress_bar_config(disable=a_							)

        # forward without prompt embeds
        _UpperCAmelCase  :						Any			     = self.get_dummy_inputs(a_							)
        _UpperCAmelCase  :						List[str]			     = 3 * ["""this is a negative prompt"""]
        _UpperCAmelCase  :						str			     = negative_prompt
        _UpperCAmelCase  :						str			     = 3 * [inputs["""prompt"""]]

        _UpperCAmelCase  :						Any			     = sd_pipe(**a_							)
        _UpperCAmelCase  :						int			     = output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        _UpperCAmelCase  :						Dict			     = self.get_dummy_inputs(a_							)
        _UpperCAmelCase  :						int			     = 3 * ["""this is a negative prompt"""]
        _UpperCAmelCase  :						Tuple			     = 3 * [inputs.pop("""prompt"""							)]

        (
            (
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,
        )  :						List[str]			     = sd_pipe.encode_prompt(a_  ,negative_prompt=a_							)

        _UpperCAmelCase  :						Any			     = sd_pipe(
            **a_  ,prompt_embeds=a_  ,negative_prompt_embeds=a_  ,pooled_prompt_embeds=a_  ,negative_pooled_prompt_embeds=a_  ,)
        _UpperCAmelCase  :						Union[str, Any]			     = output.images[0, -3:, -3:, -1]

        # make sure that it's equal
        assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1E-4







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


 """simple docstring"""


 def _snake_case (							self							)		->   Union[str, Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 def _snake_case (							self  ,a_  ,a_="cpu"  ,a_=torch.floataa  ,a_=0							)		->   List[Any]:
        _UpperCAmelCase  :						Optional[int]			     = torch.Generator(device=a_							).manual_seed(a_							)
        _UpperCAmelCase  :						List[Any]			     = np.random.RandomState(a_							).standard_normal((1, 4, 64, 64)							)
        _UpperCAmelCase  :						List[Any]			     = torch.from_numpy(a_							).to(device=a_  ,dtype=a_							)
        _UpperCAmelCase  :						Optional[Any]			     = {
            """prompt""": """a photograph of an astronaut riding a horse""",
            """latents""": latents,
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						Tuple			     = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""							)
        pipe.to(a_							)
        pipe.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						Any			     = self.get_inputs(a_							)
        _UpperCAmelCase  :						Dict			     = pipe(**a_							).images
        _UpperCAmelCase  :						List[str]			     = image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        _UpperCAmelCase  :						Dict			     = np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]							)
        assert np.abs(image_slice - expected_slice							).max() < 7E-3








'''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()
A_							: Optional[Any]						 =	logging.get_logger(__name__)




def     snake_case_					(     lowerCAmelCase_		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = DPTConfig()

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

       if "ade" in checkpoint_url:
              _UpperCAmelCase  :						Optional[int]			     = True

              _UpperCAmelCase  :						Tuple			     = 150
              _UpperCAmelCase  :						Tuple			     = """huggingface/label-files"""
              _UpperCAmelCase  :						int			     = """ade20k-id2label.json"""
              _UpperCAmelCase  :						List[str]			     = json.load(open(cached_download(hf_hub_url(lowerCAmelCase_       ,   lowerCAmelCase_       ,   repo_type="""dataset"""		)		)       ,   """r"""		)		)
              _UpperCAmelCase  :						List[Any]			     = {int(lowerCAmelCase_		): v for k, v in idalabel.items()}
              _UpperCAmelCase  :						Tuple			     = idalabel
              _UpperCAmelCase  :						Optional[int]			     = {v: k for k, v in idalabel.items()}
              _UpperCAmelCase  :						Optional[int]			     = [1, 150, 480, 480]

       return config, expected_shape




def     snake_case_					(     lowerCAmelCase_		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Tuple			     = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
       for k in ignore_keys:
              state_dict.pop(lowerCAmelCase_       ,   lowerCAmelCase_		)




def     snake_case_					(     lowerCAmelCase_		)->		Any:





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

       return name




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''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)
              _UpperCAmelCase  :						Any			     = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''		)
              _UpperCAmelCase  :						Union[str, Any]			     = 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  :						Optional[int]			     = in_proj_weight[: config.hidden_size, :]
              _UpperCAmelCase  :						Optional[Any]			     = in_proj_bias[: config.hidden_size]
              _UpperCAmelCase  :						Optional[Any]			     = in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _UpperCAmelCase  :						Tuple			     = in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              _UpperCAmelCase  :						Tuple			     = in_proj_weight[
                  -config.hidden_size :, :
              ]
              _UpperCAmelCase  :						Dict			     = in_proj_bias[-config.hidden_size :]




def     snake_case_					(     )->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase  :						List[str]			     = Image.open(requests.get(lowerCAmelCase_       ,   stream=lowerCAmelCase_		).raw		)
       return im




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


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

       # load HuggingFace model
       _UpperCAmelCase  :						Optional[int]			     = DPTForSemanticSegmentation(lowerCAmelCase_		) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase_		)
       model.load_state_dict(lowerCAmelCase_		)
       model.eval()

       # Check outputs on an image
       _UpperCAmelCase  :						Tuple			     = 480 if """ade""" in checkpoint_url else 384
       _UpperCAmelCase  :						List[str]			     = DPTImageProcessor(size=lowerCAmelCase_		)

       _UpperCAmelCase  :						Optional[Any]			     = prepare_img()
       _UpperCAmelCase  :						Dict			     = image_processor(lowerCAmelCase_       ,   return_tensors="""pt"""		)

       # forward pass
       _UpperCAmelCase  :						Optional[Any]			     = model(**lowerCAmelCase_		).logits if """ade""" in checkpoint_url else model(**lowerCAmelCase_		).predicted_depth

       # Assert logits
       _UpperCAmelCase  :						Optional[int]			     = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]		)
       if "ade" in checkpoint_url:
              _UpperCAmelCase  :						str			     = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]		)
       assert outputs.shape == torch.Size(lowerCAmelCase_		)
       assert (
           torch.allclose(outputs[0, 0, :3, :3]       ,   lowerCAmelCase_       ,   atol=1e-4		)
           if "ade" in checkpoint_url
           else torch.allclose(outputs[0, :3, :3]       ,   lowerCAmelCase_		)
       )

       Path(lowerCAmelCase_		).mkdir(exist_ok=lowerCAmelCase_		)
       print(F'''Saving model to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(lowerCAmelCase_		)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
       image_processor.save_pretrained(lowerCAmelCase_		)

       if push_to_hub:
              print("""Pushing model to hub..."""		)
              model.push_to_hub(
                  repo_path_or_name=Path(lowerCAmelCase_       ,   lowerCAmelCase_		)       ,   organization="""nielsr"""       ,   commit_message="""Add model"""       ,   use_temp_dir=lowerCAmelCase_       ,   )
              image_processor.push_to_hub(
                  repo_path_or_name=Path(lowerCAmelCase_       ,   lowerCAmelCase_		)       ,   organization="""nielsr"""       ,   commit_message="""Add image processor"""       ,   use_temp_dir=lowerCAmelCase_       ,   )


if __name__ == "__main__":
   A_							: Any						 =	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=True,
       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.""",
   )

   A_							: List[Any]						 =	parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



