





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()
UpperCamelCase_									=logging.get_logger(__name__)





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : int         =   DPTConfig()

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

						if "ade" in checkpoint_url:
												_UpperCamelCase      : List[str]         =   True

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

						return config, expected_shape





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
						for k in ignore_keys:
												state_dict.pop(lowercase_	,						lowercase_       )





def 	a_    (							_lowercase       ):





						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												_UpperCamelCase      : Optional[int]         =   name.replace('''pretrained.model'''	,						'''dpt.encoder'''       )
						if "pretrained.model" in name:
												_UpperCamelCase      : List[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      : List[str]         =   name.replace('''pos_embed'''	,						'''position_embeddings'''       )
						if "attn.proj" in name:
												_UpperCamelCase      : Optional[Any]         =   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      : Tuple         =   name.replace('''blocks'''	,						'''layer'''       )
						if "mlp.fc1" in name:
												_UpperCamelCase      : Any         =   name.replace('''mlp.fc1'''	,						'''intermediate.dense'''       )
						if "mlp.fc2" in name:
												_UpperCamelCase      : Tuple         =   name.replace('''mlp.fc2'''	,						'''output.dense'''       )
						if "norm1" in name:
												_UpperCamelCase      : List[Any]         =   name.replace('''norm1'''	,						'''layernorm_before'''       )
						if "norm2" in name:
												_UpperCamelCase      : List[str]         =   name.replace('''norm2'''	,						'''layernorm_after'''       )
						if "scratch.output_conv" in name:
												_UpperCamelCase      : List[Any]         =   name.replace('''scratch.output_conv'''	,						'''head'''       )
						if "scratch" in name:
												_UpperCamelCase      : str         =   name.replace('''scratch'''	,						'''neck'''       )
						if "layer1_rn" in name:
												_UpperCamelCase      : int         =   name.replace('''layer1_rn'''	,						'''convs.0'''       )
						if "layer2_rn" in name:
												_UpperCamelCase      : Tuple         =   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      : Optional[int]         =   name.replace('''layer4_rn'''	,						'''convs.3'''       )
						if "refinenet" in name:
												_UpperCamelCase      : Dict         =   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      : List[Any]         =   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      : List[str]         =   name.replace('''resConfUnit2'''	,						'''residual_layer2'''       )
						if "conv1" in name:
												_UpperCamelCase      : Tuple         =   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      : 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:
												_UpperCamelCase      : 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:
												_UpperCamelCase      : 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      : 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:
												_UpperCamelCase      : int         =   name.replace('''pretrained.act_postprocess1.3'''	,						'''neck.reassemble_stage.layers.0.projection'''       )
						if "pretrained.act_postprocess1.4" in name:
												_UpperCamelCase      : Optional[Any]         =   name.replace('''pretrained.act_postprocess1.4'''	,						'''neck.reassemble_stage.layers.0.resize'''       )
						if "pretrained.act_postprocess2.3" in name:
												_UpperCamelCase      : Dict         =   name.replace('''pretrained.act_postprocess2.3'''	,						'''neck.reassemble_stage.layers.1.projection'''       )
						if "pretrained.act_postprocess2.4" in name:
												_UpperCamelCase      : Union[str, Any]         =   name.replace('''pretrained.act_postprocess2.4'''	,						'''neck.reassemble_stage.layers.1.resize'''       )
						if "pretrained.act_postprocess3.3" in name:
												_UpperCamelCase      : int         =   name.replace('''pretrained.act_postprocess3.3'''	,						'''neck.reassemble_stage.layers.2.projection'''       )
						if "pretrained.act_postprocess4.3" in name:
												_UpperCamelCase      : List[str]         =   name.replace('''pretrained.act_postprocess4.3'''	,						'''neck.reassemble_stage.layers.3.projection'''       )
						if "pretrained.act_postprocess4.4" in name:
												_UpperCamelCase      : Tuple         =   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      : List[Any]         =   name.replace('''bn'''	,						'''batch_norm'''       )
						if "head" in name:
												_UpperCamelCase      : Optional[Any]         =   name.replace('''head'''	,						'''head.head'''       )
						if "encoder.norm" in name:
												_UpperCamelCase      : Any         =   name.replace('''encoder.norm'''	,						'''layernorm'''       )
						if "auxlayer" in name:
												_UpperCamelCase      : str         =   name.replace('''auxlayer'''	,						'''auxiliary_head.head'''       )

						return name





def 	a_    (							_lowercase	,						_lowercase       ):





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





def 	a_    (							):
						_UpperCamelCase      : Tuple         =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						_UpperCamelCase      : Optional[Any]         =   Image.open(requests.get(lowercase_	,						stream=lowercase_       ).raw       )
						return im





@torch.no_grad()
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase       ,							_UpperCamelCase      : Any         =   get_dpt_config(lowercase_       )
						# load original state_dict from URL
						_UpperCamelCase      : Dict         =   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():
												_UpperCamelCase      : Any         =   state_dict.pop(lowercase_       )
												_UpperCamelCase      : Dict         =   val
						# read in qkv matrices
						read_in_q_k_v(lowercase_	,						lowercase_       )

						# load HuggingFace model
						_UpperCamelCase      : Union[str, Any]         =   DPTForSemanticSegmentation(lowercase_       ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(lowercase_       )
						model.load_state_dict(lowercase_       )
						model.eval()

						# Check outputs on an image
						_UpperCamelCase      : Tuple         =   480 if '''ade''' in checkpoint_url else 384
						_UpperCamelCase      : Dict         =   DPTImageProcessor(size=lowercase_       )

						_UpperCamelCase      : Optional[int]         =   prepare_img()
						_UpperCamelCase      : Tuple         =   image_processor(lowercase_	,						return_tensors='''pt'''       )

						# forward pass
						_UpperCamelCase      : Optional[Any]         =   model(**lowercase_       ).logits if '''ade''' in checkpoint_url else model(**lowercase_       ).predicted_depth

						# Assert logits
						_UpperCamelCase      : Optional[Any]         =   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:
												_UpperCamelCase      : Dict         =   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__":
						UpperCamelCase_									=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.""",
						)

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










"""simple docstring"""




def 	a_    (							_lowercase	,						_lowercase       ):
						if discount_rate < 0:
												raise ValueError('''Discount rate cannot be negative'''       )
						if not cash_flows:
												raise ValueError('''Cash flows list cannot be empty'''       )
						_UpperCamelCase      : Optional[int]         =   sum(
						    cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowercase       )       )
						return round(_lowercase	,						ndigits=2       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



