


'''simple docstring'''
import argparse
import json
from pathlib import Path

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

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


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


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					DPTConfig()

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

					if "ade" in checkpoint_url:
										UpperCamelCase__  :Union[str, Any]				   =					True

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

					return config, expected_shape


def      a (     __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
					for k in ignore_keys:
										state_dict.pop(__a					,   __a  )


def      a (     __a  )    ->      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__  :Any				   =					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__  :Any				   =					name.replace('''patch_embed'''					,   '''patch_embeddings'''  )
					if "pos_embed" in name:
										UpperCamelCase__  :Optional[int]				   =					name.replace('''pos_embed'''					,   '''position_embeddings'''  )
					if "attn.proj" in name:
										UpperCamelCase__  :List[str]				   =					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__  :List[Any]				   =					name.replace('''blocks'''					,   '''layer'''  )
					if "mlp.fc1" in name:
										UpperCamelCase__  :List[str]				   =					name.replace('''mlp.fc1'''					,   '''intermediate.dense'''  )
					if "mlp.fc2" in name:
										UpperCamelCase__  :int				   =					name.replace('''mlp.fc2'''					,   '''output.dense'''  )
					if "norm1" in name:
										UpperCamelCase__  :Dict				   =					name.replace('''norm1'''					,   '''layernorm_before'''  )
					if "norm2" in name:
										UpperCamelCase__  :Tuple				   =					name.replace('''norm2'''					,   '''layernorm_after'''  )
					if "scratch.output_conv" in name:
										UpperCamelCase__  :Any				   =					name.replace('''scratch.output_conv'''					,   '''head'''  )
					if "scratch" in name:
										UpperCamelCase__  :List[Any]				   =					name.replace('''scratch'''					,   '''neck'''  )
					if "layer1_rn" in name:
										UpperCamelCase__  :Dict				   =					name.replace('''layer1_rn'''					,   '''convs.0'''  )
					if "layer2_rn" in name:
										UpperCamelCase__  :List[str]				   =					name.replace('''layer2_rn'''					,   '''convs.1'''  )
					if "layer3_rn" in name:
										UpperCamelCase__  :List[Any]				   =					name.replace('''layer3_rn'''					,   '''convs.2'''  )
					if "layer4_rn" in name:
										UpperCamelCase__  :Dict				   =					name.replace('''layer4_rn'''					,   '''convs.3'''  )
					if "refinenet" in name:
										UpperCamelCase__  :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
										UpperCamelCase__  :str				   =					name.replace(f'''refinenet{layer_idx}'''					,   f'''fusion_stage.layers.{abs(layer_idx-4  )}'''  )
					if "out_conv" in name:
										UpperCamelCase__  :Dict				   =					name.replace('''out_conv'''					,   '''projection'''  )
					if "resConfUnit1" in name:
										UpperCamelCase__  :Union[str, Any]				   =					name.replace('''resConfUnit1'''					,   '''residual_layer1'''  )
					if "resConfUnit2" in name:
										UpperCamelCase__  :Any				   =					name.replace('''resConfUnit2'''					,   '''residual_layer2'''  )
					if "conv1" in name:
										UpperCamelCase__  :int				   =					name.replace('''conv1'''					,   '''convolution1'''  )
					if "conv2" in name:
										UpperCamelCase__  :int				   =					name.replace('''conv2'''					,   '''convolution2'''  )
					# readout blocks
					if "pretrained.act_postprocess1.0.project.0" in name:
										UpperCamelCase__  :Union[str, 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__  :Union[str, 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:
										UpperCamelCase__  :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:
										UpperCamelCase__  :Optional[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__  :Union[str, Any]				   =					name.replace('''pretrained.act_postprocess1.4'''					,   '''neck.reassemble_stage.layers.0.resize'''  )
					if "pretrained.act_postprocess2.3" in name:
										UpperCamelCase__  :str				   =					name.replace('''pretrained.act_postprocess2.3'''					,   '''neck.reassemble_stage.layers.1.projection'''  )
					if "pretrained.act_postprocess2.4" in name:
										UpperCamelCase__  :Dict				   =					name.replace('''pretrained.act_postprocess2.4'''					,   '''neck.reassemble_stage.layers.1.resize'''  )
					if "pretrained.act_postprocess3.3" in name:
										UpperCamelCase__  :Dict				   =					name.replace('''pretrained.act_postprocess3.3'''					,   '''neck.reassemble_stage.layers.2.projection'''  )
					if "pretrained.act_postprocess4.3" in name:
										UpperCamelCase__  :Union[str, Any]				   =					name.replace('''pretrained.act_postprocess4.3'''					,   '''neck.reassemble_stage.layers.3.projection'''  )
					if "pretrained.act_postprocess4.4" in name:
										UpperCamelCase__  :List[str]				   =					name.replace('''pretrained.act_postprocess4.4'''					,   '''neck.reassemble_stage.layers.3.resize'''  )
					if "pretrained" in name:
										UpperCamelCase__  :Union[str, Any]				   =					name.replace('''pretrained'''					,   '''dpt'''  )
					if "bn" in name:
										UpperCamelCase__  :Tuple				   =					name.replace('''bn'''					,   '''batch_norm'''  )
					if "head" in name:
										UpperCamelCase__  :List[Any]				   =					name.replace('''head'''					,   '''head.head'''  )
					if "encoder.norm" in name:
										UpperCamelCase__  :Tuple				   =					name.replace('''encoder.norm'''					,   '''layernorm'''  )
					if "auxlayer" in name:
										UpperCamelCase__  :Union[str, Any]				   =					name.replace('''auxlayer'''					,   '''auxiliary_head.head'''  )

					return name


def      a (     __a					,   __a  )    ->      Dict:



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


def      a (     )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					UpperCamelCase__  :Optional[int]				   =					Image.open(requests.get(__a					,   stream=__a  ).raw  )
					return im


@torch.no_grad()
def      a (     __a					,   __a					,   __a					,   __a  )    ->      Dict:



					'''simple docstring'''






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

					# load HuggingFace model
					UpperCamelCase__  :int				   =					DPTForSemanticSegmentation(__a  ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__a  )
					model.load_state_dict(__a  )
					model.eval()

					# Check outputs on an image
					UpperCamelCase__  :Union[str, Any]				   =					480 if '''ade''' in checkpoint_url else 384
					UpperCamelCase__  :Dict				   =					DPTImageProcessor(size=__a  )

					UpperCamelCase__  :List[Any]				   =					prepare_img()
					UpperCamelCase__  :Any				   =					image_processor(__a					,   return_tensors='''pt'''  )

					# forward pass
					UpperCamelCase__  :List[Any]				   =					model(**__a  ).logits if '''ade''' in checkpoint_url else model(**__a  ).predicted_depth

					# Assert logits
					UpperCamelCase__  :Optional[Any]				   =					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__  :Any				   =					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(__a  )
					assert (
					    torch.allclose(outputs[0, 0, :3, :3]					,   __a					,   atol=1e-4  )
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3]					,   __a  )
					)

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


if __name__ == "__main__":
		__snake_case				 =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
		    type=str,
		    help='''URL of the original DPT checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=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.''',
		)

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



'''simple docstring'''
import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					test_file.split(os.path.sep  )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
										    f'''{test_file} instead.'''  )
					UpperCamelCase__  :List[Any]				   =					components[-1]
					if not test_fn.endswith('''py'''  ):
										raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''  )
					if not test_fn.startswith('''test_modeling_'''  ):
										raise ValueError(
										    f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''  )

					UpperCamelCase__  :Optional[Any]				   =					components[:-1] + [test_fn.replace('''.py'''					,   ''''''  )]
					UpperCamelCase__  :str				   =					'''.'''.join(__a  )

					return test_module_path


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :Dict				   =					get_module_path(__a  )
					UpperCamelCase__  :List[str]				   =					importlib.import_module(__a  )

					return test_module


def      a (     __a  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :Dict				   =					[]
					UpperCamelCase__  :Optional[Any]				   =					get_test_module(__a  )
					for attr in dir(__a  ):
										if attr.endswith('''ModelTester'''  ):
															tester_classes.append(getattr(__a					,   __a  )  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					[]
					UpperCamelCase__  :Dict				   =					get_test_module(__a  )
					for attr in dir(__a  ):
										UpperCamelCase__  :Dict				   =					getattr(__a					,   __a  )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										UpperCamelCase__  :Optional[int]				   =					getattr(__a					,   '''all_model_classes'''					,   []  )
										if len(__a  ) > 0:
															test_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					get_test_classes(__a  )
					UpperCamelCase__  :Any				   =					set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes  )

					# sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :Optional[Any]				   =					test_class()
					if hasattr(__a					,   '''setUp'''  ):
										test.setUp()

					UpperCamelCase__  :Union[str, Any]				   =					None
					if hasattr(__a					,   '''model_tester'''  ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															UpperCamelCase__  :List[str]				   =					test.model_tester.__class__

					return model_tester


def      a (     __a					,   __a  )    ->      Any:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					get_test_classes(__a  )

					UpperCamelCase__  :List[str]				   =					[]
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a					,   __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					get_test_classes_for_model(__a					,   __a  )

					UpperCamelCase__  :List[str]				   =					[]
					for test_class in test_classes:
										UpperCamelCase__  :int				   =					get_model_tester_from_test_class(__a  )
										if tester_class is not None:
															tester_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					get_test_classes(__a  )
					UpperCamelCase__  :Dict				   =					{test_class: get_model_tester_from_test_class(__a  ) for test_class in test_classes}
					return test_tester_mapping


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					get_model_classes(__a  )
					UpperCamelCase__  :Tuple				   =					{
					    model_class: get_test_classes_for_model(__a					,   __a  ) for model_class in model_classes
					}
					return model_test_mapping


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					get_model_classes(__a  )
					UpperCamelCase__  :Union[str, Any]				   =					{
					    model_class: get_tester_classes_for_model(__a					,   __a  ) for model_class in model_classes
					}
					return model_to_tester_mapping







def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					if isinstance(__a					,   __a  ):
										return o
					elif isinstance(__a					,   __a  ):
										return o.__name__
					elif isinstance(__a					,   (list, tuple)  ):
										return [to_json(__a  ) for x in o]
					elif isinstance(__a					,   __a  ):
										return {to_json(__a  ): to_json(__a  ) for k, v in o.items()}
					else:
										return o