



"""simple docstring"""
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__a      =				logging.get_logger(__name__)

enable_full_determinism()
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Tuple:
											snake_case_ :List[str]						=			4
											snake_case_ :Tuple						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :str						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Union[str, Any]						=			torch.tensor([10]						).to(snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Dict:
											snake_case_ :Any						=			{
											    """block_out_channels""": (32, 64),
											    """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
											    """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
											    """attention_head_dim""": 3,
											    """out_channels""": 3,
											    """in_channels""": 3,
											    """layers_per_block""": 2,
											    """sample_size""": 32,
											}
											snake_case_ :Tuple						=			self.dummy_input
											return init_dict, inputs_dict

class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	str:
											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			4
											snake_case_ :int						=			(32, 32)

											snake_case_ :Any						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10]						).to(snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (4, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	int:
											return (4, 32, 32)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[Any]:
											snake_case_ :Dict						=			{
											    """sample_size""": 32,
											    """in_channels""": 4,
											    """out_channels""": 4,
											    """layers_per_block""": 2,
											    """block_out_channels""": (32, 64),
											    """attention_head_dim""": 32,
											    """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
											    """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
											}
											snake_case_ :List[str]						=			self.dummy_input
											return init_dict, inputs_dict






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[Any]:
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)

											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :List[str]						=			model(**self.dummy_input						).sample

											assert image is not None, "Make sure output is not None"






					@unittest.skipIf(torch_device != """cuda"""						,     """This test is supposed to run on GPU"""						)
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_,       snake_case_ :Union[str, Any]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model.to(snake_case						)
											snake_case_ :Union[str, Any]						=			model(**self.dummy_input						).sample

											assert image is not None, "Make sure output is not None"






					@unittest.skipIf(torch_device != """cuda"""						,     """This test is supposed to run on GPU"""						)
					def    lowerCAmelCase_				(       self:							str						)   ->	Any:
											# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model_accelerate.to(snake_case						)
											model_accelerate.eval()

											snake_case_ :List[Any]						=			torch.randn(
											    1						,     model_accelerate.config.in_channels						,     model_accelerate.config.sample_size						,     model_accelerate.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :str						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											snake_case_ :Optional[int]						=			model_accelerate(snake_case						,     snake_case						)["""sample"""]

											# two models don't need to stay in the device at the same time
											del model_accelerate
											torch.cuda.empty_cache()
											gc.collect()

											snake_case_,       snake_case_ :str						=			UNetaDModel.from_pretrained(
											    """fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						,     low_cpu_mem_usage=snake_case						)
											model_normal_load.to(snake_case						)
											model_normal_load.eval()
											snake_case_ :int						=			model_normal_load(snake_case						,     snake_case						)["""sample"""]

											assert torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_ :Tuple						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						)
											model.eval()
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			torch.randn(
											    1						,     model.config.in_channels						,     model.config.sample_size						,     model.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Union[str, Any]						=			model(snake_case						,     snake_case						).sample

											snake_case_ :Optional[int]						=			output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case_ :Dict						=			torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)						)

class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			List[Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							int=(32, 32)						)   ->	Tuple:
											snake_case_ :Union[str, Any]						=			4
											snake_case_ :Any						=			3

											snake_case_ :int						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [10]						).to(dtype=torch.intaa						,     device=snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Any:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							int						)   ->	Tuple:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Tuple:
											snake_case_ :List[Any]						=			{
											    """block_out_channels""": [32, 64, 64, 64],
											    """in_channels""": 3,
											    """layers_per_block""": 1,
											    """out_channels""": 3,
											    """time_embedding_type""": """fourier""",
											    """norm_eps""": 1E-6,
											    """mid_block_scale_factor""": math.sqrt(2.0						),
											    """norm_num_groups""": None,
											    """down_block_types""": [
											        """SkipDownBlock2D""",
											        """AttnSkipDownBlock2D""",
											        """SkipDownBlock2D""",
											        """SkipDownBlock2D""",
											    ],
											    """up_block_types""": [
											        """SkipUpBlock2D""",
											        """SkipUpBlock2D""",
											        """AttnSkipUpBlock2D""",
											        """SkipUpBlock2D""",
											    ],
											}
											snake_case_ :int						=			self.dummy_input
											return init_dict, inputs_dict






					@slow
					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	List[Any]:
											snake_case_,       snake_case_ :List[Any]						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						,     output_loading_info=snake_case						)
											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :Any						=			self.dummy_input
											snake_case_ :int						=			floats_tensor((4, 3) + (256, 256)						).to(snake_case						)
											snake_case_ :int						=			noise
											snake_case_ :int						=			model(**snake_case						)

											assert image is not None, "Make sure output is not None"






					@slow
					def    lowerCAmelCase_				(       self:							str						)   ->	Dict:
											snake_case_ :Dict						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						)
											model.to(snake_case						)

											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			3
											snake_case_ :List[str]						=			(256, 256)

											snake_case_ :Tuple						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :str						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Dict						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :Optional[Any]						=			torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[Any]:
											snake_case_ :Optional[Any]						=			UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""						)
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			4
											snake_case_ :Optional[Any]						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :Dict						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :str						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :int						=			torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[Any]:
											# not required for this model
											pass


"""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			:       Dict   		=       logging.get_logger(__name__)


def   __magic_name__	(      __snake_case				:       Any  ) ->				Any:
       lowercase    :				Optional[Any]       		=							DPTConfig()

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

       if "ade" in checkpoint_url:
              lowercase    :				List[Any]       		=							True

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

       return config, expected_shape


def   __magic_name__	(      __snake_case				:       Union[str, Any]  ) ->				Optional[int]:
       lowercase    :				Optional[Any]       		=							["pretrained.model.head.weight", "pretrained.model.head.bias"]
       for k in ignore_keys:
              state_dict.pop(__snake_case     , __snake_case  )


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

       return name


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


def   __magic_name__	(      ) ->				int:
       lowercase    :				str       		=							"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase    :				str       		=							Image.open(requests.get(__snake_case     , stream=__snake_case  ).raw  )
       return im


@torch.no_grad()
def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       str     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				Tuple:
       lowercase		,    lowercase    :				Tuple       		=							get_dpt_config(__snake_case  )
       # load original state_dict from URL
       lowercase    :				List[Any]       		=							torch.hub.load_state_dict_from_url(__snake_case     , map_location="cpu"  )
       # remove certain keys
       remove_ignore_keys_(__snake_case  )
       # rename keys
       for key in state_dict.copy().keys():
              lowercase    :				Any       		=							state_dict.pop(__snake_case  )
              lowercase    :				Optional[int]       		=							val
       # read in qkv matrices
       read_in_q_k_v(__snake_case     , __snake_case  )

       # load HuggingFace model
       lowercase    :				List[str]       		=							DPTForSemanticSegmentation(__snake_case  ) if "ade" in checkpoint_url else DPTForDepthEstimation(__snake_case  )
       model.load_state_dict(__snake_case  )
       model.eval()

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

       lowercase    :				Any       		=							prepare_img()
       lowercase    :				Union[str, Any]       		=							image_processor(__snake_case     , return_tensors="pt"  )

       # forward pass
       lowercase    :				Optional[int]       		=							model(**__snake_case  ).logits if "ade" in checkpoint_url else model(**__snake_case  ).predicted_depth

       # Assert logits
       lowercase    :				Optional[int]       		=							torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]  )
       if "ade" in checkpoint_url:
              lowercase    :				List[str]       		=							torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]  )
       assert outputs.shape == torch.Size(__snake_case  )
       assert (
           torch.allclose(outputs[0, 0, :3, :3]     , __snake_case     , atol=1E-4  )
           if "ade" in checkpoint_url
           else torch.allclose(outputs[0, :3, :3]     , __snake_case  )
       )

       Path(__snake_case  ).mkdir(exist_ok=__snake_case  )
       print(f"""Saving model to {pytorch_dump_folder_path}"""  )
       model.save_pretrained(__snake_case  )
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
       image_processor.save_pretrained(__snake_case  )

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


if __name__ == "__main__":
   _A			:       Dict   		=       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			:       Dict   		=       parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


