

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_ :				Tuple						 =    logging.get_logger(__name__)

def 		UpperCamelCase      (lowercase_:				List[str]      )  ->   Any:
	A__							:					Tuple       =	DPTConfig()

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

	if "ade" in checkpoint_url:
		A__							:					int       =	True

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

	return config, expected_shape

def 		UpperCamelCase      (lowercase_:				str      )  ->   Any:
	A__							:					Optional[Any]       =	["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
	for k in ignore_keys:
		state_dict.pop(lowercase_					,   lowercase_      )

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

	return name

def 		UpperCamelCase      (lowercase_:				Tuple					,   lowercase_:				int      )  ->   List[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)
		A__							:					int       =	state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""      )
		A__							:					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
		A__							:					Any       =	in_proj_weight[: config.hidden_size, :]
		A__							:					Union[str, Any]       =	in_proj_bias[: config.hidden_size]
		A__							:					Union[str, Any]       =	in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A__							:					Optional[Any]       =	in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A__							:					Optional[int]       =	in_proj_weight[
		    -config.hidden_size :, :
		]
		A__							:					Optional[Any]       =	in_proj_bias[-config.hidden_size :]

def 		UpperCamelCase      ()  ->   Dict:
	A__							:					Tuple       =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__							:					Any       =	Image.open(requests.get(lowercase_					,   stream=lowercase_      ).raw      )
	return im

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

	# load HuggingFace model
	A__							:					str       =	DPTForSemanticSegmentation(lowercase_      ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowercase_      )
	model.load_state_dict(lowercase_      )
	model.eval()

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

	A__							:					int       =	prepare_img()
	A__							:					List[str]       =	image_processor(lowercase_					,   return_tensors="""pt"""      )

	# forward pass
	A__							:					Tuple       =	model(**lowercase_      ).logits if """ade""" in checkpoint_url else model(**lowercase_      ).predicted_depth

	# Assert logits
	A__							:					Optional[int]       =	torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]      )
	if "ade" in checkpoint_url:
		A__							:					Union[str, Any]       =	torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]      )
	assert outputs.shape == torch.Size(lowercase_      )
	assert (
	    torch.allclose(outputs[0, 0, :3, :3]					,   lowercase_					,   atol=1E-4      )
	    if "ade" in checkpoint_url
	    else torch.allclose(outputs[0, :3, :3]					,   lowercase_      )
	)

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

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


if __name__ == "__main__":
	A_ :				List[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_ :				Optional[int]						 =    parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

A_ :				Dict						 =    list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A_ :				Optional[Any]						 =    [file for file in filepaths if file != file.lower()]
if upper_files:
	print(f'''{len(upper_files)} files contain uppercase characters:''')
	print('\n'.join(upper_files) + '\n')

A_ :				Tuple						 =    [file for file in filepaths if ' ' in file]
if space_files:
	print(f'''{len(space_files)} files contain space characters:''')
	print('\n'.join(space_files) + '\n')

A_ :				Any						 =    [file for file in filepaths if '-' in file]
if hyphen_files:
	print(f'''{len(hyphen_files)} files contain hyphen characters:''')
	print('\n'.join(hyphen_files) + '\n')

A_ :				List[str]						 =    [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(f'''{len(nodir_files)} files are not in a directory:''')
	print('\n'.join(nodir_files) + '\n')

A_ :				Any						 =    len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)



