





'''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()
_a   :					Dict           =       logging.get_logger(__name__)





def   _lowerCAmelCase		(		lowercase      )   ->   Tuple:
			__lowerCAmelCase   		=							DPTConfig()

			if "large" in checkpoint_url:
						__lowerCAmelCase   		=							1024
						__lowerCAmelCase   		=							4096
						__lowerCAmelCase   		=							24
						__lowerCAmelCase   		=							16
						__lowerCAmelCase   		=							[5, 11, 17, 23]
						__lowerCAmelCase   		=							[256, 512, 1024, 1024]
						__lowerCAmelCase   		=							(1, 384, 384)

			if "ade" in checkpoint_url:
						__lowerCAmelCase   		=							True

						__lowerCAmelCase   		=							150
						__lowerCAmelCase   		=							"""huggingface/label-files"""
						__lowerCAmelCase   		=							"""ade20k-id2label.json"""
						__lowerCAmelCase   		=							json.load(open(cached_download(hf_hub_url(_snake_case   ,      _snake_case   ,      repo_type="""dataset"""      )      )   ,      """r"""      )      )
						__lowerCAmelCase   		=							{int(_snake_case      ): v for k, v in idalabel.items()}
						__lowerCAmelCase   		=							idalabel
						__lowerCAmelCase   		=							{v: k for k, v in idalabel.items()}
						__lowerCAmelCase   		=							[1, 150, 480, 480]

			return config, expected_shape





def   _lowerCAmelCase		(		lowercase      )   ->   List[Any]:
			__lowerCAmelCase   		=							["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(_snake_case   ,      _snake_case      )





def   _lowerCAmelCase		(		lowercase      )   ->   Dict:


			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		(		lowercase   ,      lowercase      )   ->   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)
						__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		(		)   ->   List[Any]:
			__lowerCAmelCase   		=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			__lowerCAmelCase   		=							Image.open(requests.get(_snake_case   ,      stream=_snake_case      ).raw      )
			return im





@torch.no_grad()
def   _lowerCAmelCase		(		lowercase   ,      lowercase   ,      lowercase   ,      lowercase      )   ->   int:
			__lowerCAmelCase		,			__lowerCAmelCase   		=							get_dpt_config(_snake_case      )
			# load original state_dict from URL
			__lowerCAmelCase   		=							torch.hub.load_state_dict_from_url(_snake_case   ,      map_location="""cpu"""      )
			# remove certain keys
			remove_ignore_keys_(_snake_case      )
			# rename keys
			for key in state_dict.copy().keys():
						__lowerCAmelCase   		=							state_dict.pop(_snake_case      )
						__lowerCAmelCase   		=							val
			# read in qkv matrices
			read_in_q_k_v(_snake_case   ,      _snake_case      )

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

			# Check outputs on an image
			__lowerCAmelCase   		=							480 if """ade""" in checkpoint_url else 384
			__lowerCAmelCase   		=							DPTImageProcessor(size=_snake_case      )

			__lowerCAmelCase   		=							prepare_img()
			__lowerCAmelCase   		=							image_processor(_snake_case   ,      return_tensors="""pt"""      )

			# forward pass
			__lowerCAmelCase   		=							model(**_snake_case      ).logits if """ade""" in checkpoint_url else model(**_snake_case      ).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(_snake_case      )
			assert (
			    torch.allclose(outputs[0, 0, :3, :3]   ,      _snake_case   ,      atol=1e-4      )
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3]   ,      _snake_case      )
			)

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

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


if __name__ == "__main__":
	_a   :					Tuple           =       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   :					Dict           =       parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    return "\n".join(
        f'''{number} * {i} = {number * i}''' for i in range(1  ,number_of_terms + 1							)							)


if __name__ == "__main__":
 print(multiplication_table(number=5, number_of_terms=10))




