


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 							UpperCamelCase_(					_A :Tuple	)->		List[str]:
			UpperCamelCase__       =							DPTConfig()

			if "large" in checkpoint_url:
						UpperCamelCase__       =							10_24
						UpperCamelCase__       =							40_96
						UpperCamelCase__       =							24
						UpperCamelCase__       =							16
						UpperCamelCase__       =							[5, 11, 17, 23]
						UpperCamelCase__       =							[2_56, 5_12, 10_24, 10_24]
						UpperCamelCase__       =							(1, 3_84, 3_84)

			if "ade" in checkpoint_url:
						UpperCamelCase__       =							True

						UpperCamelCase__       =							1_50
						UpperCamelCase__       =							"huggingface/label-files"
						UpperCamelCase__       =							"ade20k-id2label.json"
						UpperCamelCase__       =							json.load(open(cached_download(hf_hub_url(lowerCamelCase_					,  lowerCamelCase_					,  repo_type="dataset"	)	)					,  "r"	)	)
						UpperCamelCase__       =							{int(lowerCamelCase_	): v for k, v in idalabel.items()}
						UpperCamelCase__       =							idalabel
						UpperCamelCase__       =							{v: k for k, v in idalabel.items()}
						UpperCamelCase__       =							[1, 1_50, 4_80, 4_80]

			return config, expected_shape
def 							UpperCamelCase_(					_A :int	)->		List[str]:
			UpperCamelCase__       =							["pretrained.model.head.weight", "pretrained.model.head.bias"]
			for k in ignore_keys:
						state_dict.pop(lowerCamelCase_					,  lowerCamelCase_	)
def 							UpperCamelCase_(					_A :Any	)->		Tuple:






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

			return name
def 							UpperCamelCase_(					_A :int					,  _A :Tuple	)->		Union[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)
						UpperCamelCase__       =							state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''	)
						UpperCamelCase__       =							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__       =							in_proj_weight[: config.hidden_size, :]
						UpperCamelCase__       =							in_proj_bias[: config.hidden_size]
						UpperCamelCase__       =							in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase__       =							in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase__       =							in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase__       =							in_proj_bias[-config.hidden_size :]
def 							UpperCamelCase_(					)->		Tuple:
			UpperCamelCase__       =							"http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase__       =							Image.open(requests.get(lowerCamelCase_					,  stream=lowerCamelCase_	).raw	)
			return im
@torch.no_grad()
def 							UpperCamelCase_(					_A :str					,  _A :Optional[Any]					,  _A :Optional[Any]					,  _A :List[Any]	)->		Tuple:
			UpperCamelCase__       =							get_dpt_config(lowerCamelCase_	)
			# load original state_dict from URL
			UpperCamelCase__       =							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__       =							state_dict.pop(lowerCamelCase_	)
						UpperCamelCase__       =							val
			# read in qkv matrices
			read_in_q_k_v(lowerCamelCase_					,  lowerCamelCase_	)

			# load HuggingFace model
			UpperCamelCase__       =							DPTForSemanticSegmentation(lowerCamelCase_	) if "ade" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase_	)
			model.load_state_dict(lowerCamelCase_	)
			model.eval()

			# Check outputs on an image
			UpperCamelCase__       =							4_80 if "ade" in checkpoint_url else 3_84
			UpperCamelCase__       =							DPTImageProcessor(size=lowerCamelCase_	)

			UpperCamelCase__       =							prepare_img()
			UpperCamelCase__       =							image_processor(lowerCamelCase_					,  return_tensors="pt"	)

			# forward pass
			UpperCamelCase__       =							model(**lowerCamelCase_	).logits if "ade" in checkpoint_url else model(**lowerCamelCase_	).predicted_depth

			# Assert logits
			UpperCamelCase__       =							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__       =							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__":
						__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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						return int((input_a, input_a).count(0 ) != 0 )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						assert nand_gate(0     ,		0 ) == 1
						assert nand_gate(0     ,		1 ) == 1
						assert nand_gate(1     ,		0 ) == 1
						assert nand_gate(1     ,		1 ) == 0


if __name__ == "__main__":
			print(nand_gate(0, 0))
			print(nand_gate(0, 1))
			print(nand_gate(1, 0))
			print(nand_gate(1, 1))



