
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							=logging.get_logger(__name__)





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     DPTConfig()

				if "large" in checkpoint_url:
								lowerCamelCase_         =     1_0_2_4
								lowerCamelCase_         =     4_0_9_6
								lowerCamelCase_         =     2_4
								lowerCamelCase_         =     1_6
								lowerCamelCase_         =     [5, 1_1, 1_7, 2_3]
								lowerCamelCase_         =     [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
								lowerCamelCase_         =     (1, 3_8_4, 3_8_4)

				if "ade" in checkpoint_url:
								lowerCamelCase_         =     True

								lowerCamelCase_         =     1_5_0
								lowerCamelCase_         =     "huggingface/label-files"
								lowerCamelCase_         =     "ade20k-id2label.json"
								lowerCamelCase_         =     json.load(open(cached_download(hf_hub_url(lowerCamelCase__      ,					lowerCamelCase__      ,					repo_type="dataset"       )       )      ,					"r"       )       )
								lowerCamelCase_         =     {int(lowerCamelCase__       ): v for k, v in idalabel.items()}
								lowerCamelCase_         =     idalabel
								lowerCamelCase_         =     {v: k for k, v in idalabel.items()}
								lowerCamelCase_         =     [1, 1_5_0, 4_8_0, 4_8_0]

				return config, expected_shape





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     ["pretrained.model.head.weight", "pretrained.model.head.bias"]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase__      ,					lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								lowerCamelCase_         =     name.replace("pretrained.model"      ,					"dpt.encoder"       )
				if "pretrained.model" in name:
								lowerCamelCase_         =     name.replace("pretrained.model"      ,					"dpt.embeddings"       )
				if "patch_embed" in name:
								lowerCamelCase_         =     name.replace("patch_embed"      ,					"patch_embeddings"       )
				if "pos_embed" in name:
								lowerCamelCase_         =     name.replace("pos_embed"      ,					"position_embeddings"       )
				if "attn.proj" in name:
								lowerCamelCase_         =     name.replace("attn.proj"      ,					"attention.output.dense"       )
				if "proj" in name and "project" not in name:
								lowerCamelCase_         =     name.replace("proj"      ,					"projection"       )
				if "blocks" in name:
								lowerCamelCase_         =     name.replace("blocks"      ,					"layer"       )
				if "mlp.fc1" in name:
								lowerCamelCase_         =     name.replace("mlp.fc1"      ,					"intermediate.dense"       )
				if "mlp.fc2" in name:
								lowerCamelCase_         =     name.replace("mlp.fc2"      ,					"output.dense"       )
				if "norm1" in name:
								lowerCamelCase_         =     name.replace("norm1"      ,					"layernorm_before"       )
				if "norm2" in name:
								lowerCamelCase_         =     name.replace("norm2"      ,					"layernorm_after"       )
				if "scratch.output_conv" in name:
								lowerCamelCase_         =     name.replace("scratch.output_conv"      ,					"head"       )
				if "scratch" in name:
								lowerCamelCase_         =     name.replace("scratch"      ,					"neck"       )
				if "layer1_rn" in name:
								lowerCamelCase_         =     name.replace("layer1_rn"      ,					"convs.0"       )
				if "layer2_rn" in name:
								lowerCamelCase_         =     name.replace("layer2_rn"      ,					"convs.1"       )
				if "layer3_rn" in name:
								lowerCamelCase_         =     name.replace("layer3_rn"      ,					"convs.2"       )
				if "layer4_rn" in name:
								lowerCamelCase_         =     name.replace("layer4_rn"      ,					"convs.3"       )
				if "refinenet" in name:
								lowerCamelCase_         =     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
								lowerCamelCase_         =     name.replace(F'refinenet{layer_idx}'      ,					F'fusion_stage.layers.{abs(layer_idx-4       )}'       )
				if "out_conv" in name:
								lowerCamelCase_         =     name.replace("out_conv"      ,					"projection"       )
				if "resConfUnit1" in name:
								lowerCamelCase_         =     name.replace("resConfUnit1"      ,					"residual_layer1"       )
				if "resConfUnit2" in name:
								lowerCamelCase_         =     name.replace("resConfUnit2"      ,					"residual_layer2"       )
				if "conv1" in name:
								lowerCamelCase_         =     name.replace("conv1"      ,					"convolution1"       )
				if "conv2" in name:
								lowerCamelCase_         =     name.replace("conv2"      ,					"convolution2"       )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess1.0.project.0"      ,					"neck.reassemble_stage.readout_projects.0.0"       )
				if "pretrained.act_postprocess2.0.project.0" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess2.0.project.0"      ,					"neck.reassemble_stage.readout_projects.1.0"       )
				if "pretrained.act_postprocess3.0.project.0" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess3.0.project.0"      ,					"neck.reassemble_stage.readout_projects.2.0"       )
				if "pretrained.act_postprocess4.0.project.0" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess4.0.project.0"      ,					"neck.reassemble_stage.readout_projects.3.0"       )
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess1.3"      ,					"neck.reassemble_stage.layers.0.projection"       )
				if "pretrained.act_postprocess1.4" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess1.4"      ,					"neck.reassemble_stage.layers.0.resize"       )
				if "pretrained.act_postprocess2.3" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess2.3"      ,					"neck.reassemble_stage.layers.1.projection"       )
				if "pretrained.act_postprocess2.4" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess2.4"      ,					"neck.reassemble_stage.layers.1.resize"       )
				if "pretrained.act_postprocess3.3" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess3.3"      ,					"neck.reassemble_stage.layers.2.projection"       )
				if "pretrained.act_postprocess4.3" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess4.3"      ,					"neck.reassemble_stage.layers.3.projection"       )
				if "pretrained.act_postprocess4.4" in name:
								lowerCamelCase_         =     name.replace("pretrained.act_postprocess4.4"      ,					"neck.reassemble_stage.layers.3.resize"       )
				if "pretrained" in name:
								lowerCamelCase_         =     name.replace("pretrained"      ,					"dpt"       )
				if "bn" in name:
								lowerCamelCase_         =     name.replace("bn"      ,					"batch_norm"       )
				if "head" in name:
								lowerCamelCase_         =     name.replace("head"      ,					"head.head"       )
				if "encoder.norm" in name:
								lowerCamelCase_         =     name.replace("encoder.norm"      ,					"layernorm"       )
				if "auxlayer" in name:
								lowerCamelCase_         =     name.replace("auxlayer"      ,					"auxiliary_head.head"       )

				return name





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				for i in range(config.num_hidden_layers       ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								lowerCamelCase_         =     state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'       )
								lowerCamelCase_         =     state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'       )
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase_         =     in_proj_weight[: config.hidden_size, :]
								lowerCamelCase_         =     in_proj_bias[: config.hidden_size]
								lowerCamelCase_         =     in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCamelCase_         =     in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowerCamelCase_         =     in_proj_weight[
								    -config.hidden_size :, :
								]
								lowerCamelCase_         =     in_proj_bias[-config.hidden_size :]





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     "http://images.cocodataset.org/val2017/000000039769.jpg"
				lowerCamelCase_         =     Image.open(requests.get(lowerCamelCase__      ,					stream=lowerCamelCase__       ).raw       )
				return im





@torch.no_grad()
def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_      ,       lowerCamelCase_         =     get_dpt_config(lowerCamelCase__       )
				# load original state_dict from URL
				lowerCamelCase_         =     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():
								lowerCamelCase_         =     state_dict.pop(lowerCamelCase__       )
								lowerCamelCase_         =     val
				# read in qkv matrices
				read_in_q_k_v(lowerCamelCase__      ,					lowerCamelCase__       )

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

				# Check outputs on an image
				lowerCamelCase_         =     4_8_0 if "ade" in checkpoint_url else 3_8_4
				lowerCamelCase_         =     DPTImageProcessor(size=lowerCamelCase__       )

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

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

				# Assert logits
				lowerCamelCase_         =     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:
								lowerCamelCase_         =     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(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__":
			__A							=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							=parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True





def      lowerCamelCase_					(							lowerCamelCase__ = 1_0_0_0_1       ):

				try:
								lowerCamelCase_         =     int(lowerCamelCase__       )
				except (TypeError, ValueError):
								raise TypeError("Parameter nth must be int or castable to int."       ) from None
				if nth <= 0:
								raise ValueError("Parameter nth must be greater than or equal to one."       )
				lowerCamelCase_         =     []
				lowerCamelCase_         =     2
				while len(lowerCamelCase__       ) < nth:
								if is_prime(lowerCamelCase__       ):
												primes.append(lowerCamelCase__       )
												num += 1
								else:
												num += 1
				return primes[len(lowerCamelCase__       ) - 1]


if __name__ == "__main__":
			print(F"""{solution() = }""")


