


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 					lowercase__				(					__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						DPTConfig(embedding_type='hybrid'  )

					if "large" in checkpoint_url:
										UpperCAmelCase_   :				List[str]						=						1_024
										UpperCAmelCase_   :				Dict						=						4_096
										UpperCAmelCase_   :				List[Any]						=						24
										UpperCAmelCase_   :				int						=						16
										UpperCAmelCase_   :				str						=						[5, 11, 17, 23]
										UpperCAmelCase_   :				Optional[int]						=						[256, 512, 1_024, 1_024]
										UpperCAmelCase_   :				Any						=						(1, 384, 384)

					if "nyu" or "midas" in checkpoint_url:
										UpperCAmelCase_   :				Tuple						=						768
										UpperCAmelCase_   :				List[Any]						=						[1, 1, 1, 0.5]
										UpperCAmelCase_   :				Dict						=						[256, 512, 768, 768]
										UpperCAmelCase_   :				Union[str, Any]						=						150
										UpperCAmelCase_   :				Dict						=						16
										UpperCAmelCase_   :				str						=						(1, 384, 384)
										UpperCAmelCase_   :				int						=						False
										UpperCAmelCase_   :				Tuple						=						'project'

					if "ade" in checkpoint_url:
										UpperCAmelCase_   :				List[Any]						=						True
										UpperCAmelCase_   :				Optional[Any]						=						768
										UpperCAmelCase_   :				Optional[int]						=						[1, 1, 1, 0.5]
										UpperCAmelCase_   :				List[Any]						=						150
										UpperCAmelCase_   :				str						=						16
										UpperCAmelCase_   :				Union[str, Any]						=						'huggingface/label-files'
										UpperCAmelCase_   :				List[str]						=						'ade20k-id2label.json'
										UpperCAmelCase_   :				Tuple						=						json.load(open(cached_download(hf_hub_url(__snake_case					,	__snake_case					,	repo_type='dataset'  )  )					,	'r'  )  )
										UpperCAmelCase_   :				Union[str, Any]						=						{int(__snake_case  ): v for k, v in idalabel.items()}
										UpperCAmelCase_   :				Optional[int]						=						idalabel
										UpperCAmelCase_   :				Any						=						{v: k for k, v in idalabel.items()}
										UpperCAmelCase_   :				List[str]						=						[1, 150, 480, 480]

					return config, expected_shape




def 					lowercase__				(					__snake_case   :							List[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						['pretrained.model.head.weight', 'pretrained.model.head.bias']
					for k in ignore_keys:
										state_dict.pop(__snake_case					,	__snake_case  )




def 					lowercase__				(					__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


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

					if ".." in name:
										UpperCAmelCase_   :				Optional[Any]						=						name.replace('..'					,	'.'  )

					if "stem.conv" in name:
										UpperCAmelCase_   :				List[str]						=						name.replace('stem.conv'					,	'bit.embedder.convolution'  )
					if "blocks" in name:
										UpperCAmelCase_   :				Tuple						=						name.replace('blocks'					,	'layers'  )
					if "convolution" in name and "backbone" in name:
										UpperCAmelCase_   :				int						=						name.replace('convolution'					,	'conv'  )
					if "layer" in name and "backbone" in name:
										UpperCAmelCase_   :				List[str]						=						name.replace('layer'					,	'layers'  )
					if "backbone.bit.encoder.bit" in name:
										UpperCAmelCase_   :				List[str]						=						name.replace('backbone.bit.encoder.bit'					,	'backbone.bit'  )
					if "embedder.conv" in name:
										UpperCAmelCase_   :				List[Any]						=						name.replace('embedder.conv'					,	'embedder.convolution'  )
					if "backbone.bit.encoder.stem.norm" in name:
										UpperCAmelCase_   :				str						=						name.replace('backbone.bit.encoder.stem.norm'					,	'backbone.bit.embedder.norm'  )
					return name




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Tuple  ):


					'''simple docstring'''


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




def 					lowercase__				(					):


					'''simple docstring'''


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




@torch.no_grad()
def 					lowercase__				(					__snake_case   :							int					,	__snake_case   :							Optional[int]					,	__snake_case   :							Optional[int]					,	__snake_case   :							List[str]					,	__snake_case   :							Any  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						get_dpt_config(__snake_case  )
					# load original state_dict from URL
					# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
					UpperCAmelCase_   :				Any						=						torch.load(__snake_case					,	map_location='cpu'  )
					# remove certain keys
					remove_ignore_keys_(__snake_case  )
					# rename keys
					for key in state_dict.copy().keys():
										UpperCAmelCase_   :				List[str]						=						state_dict.pop(__snake_case  )
										UpperCAmelCase_   :				List[Any]						=						val
					# read in qkv matrices
					read_in_q_k_v(__snake_case					,	__snake_case  )

					# load HuggingFace model
					UpperCAmelCase_   :				List[str]						=						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
					UpperCAmelCase_   :				Union[str, Any]						=						480 if 'ade' in checkpoint_url else 384
					UpperCAmelCase_   :				List[Any]						=						DPTImageProcessor(size=__snake_case  )

					UpperCAmelCase_   :				Tuple						=						prepare_img()
					UpperCAmelCase_   :				str						=						image_processor(__snake_case					,	return_tensors='pt'  )

					# forward pass
					UpperCAmelCase_   :				List[str]						=						model(**__snake_case  ).logits if 'ade' in checkpoint_url else model(**__snake_case  ).predicted_depth

					if show_prediction:
										UpperCAmelCase_   :				Dict						=						(
										    torch.nn.functional.interpolate(
										        outputs.unsqueeze(1  )					,	size=(image.size[1], image.size[0])					,	mode='bicubic'					,	align_corners=__snake_case					,	)
										    .squeeze()
										    .cpu()
										    .numpy()
										)

										Image.fromarray((prediction / prediction.max()) * 255  ).show()

					if pytorch_dump_folder_path is not None:
										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:
										model.push_to_hub('ybelkada/dpt-hybrid-midas'  )
										image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'  )


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=False,
		    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.',
		)
		parser.add_argument(
		    '--show_prediction',
		    action='store_true',
		)

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














import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def 					lowercase__				(					__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						RemBertConfig.from_json_file(__snake_case  )
					print('Building PyTorch model from configuration: {}'.format(str(__snake_case  )  )  )
					UpperCAmelCase_   :				Dict						=						RemBertModel(__snake_case  )

					# Load weights from tf checkpoint
					load_tf_weights_in_rembert(__snake_case					,	__snake_case					,	__snake_case  )

					# Save pytorch-model
					print('Save PyTorch model to {}'.format(__snake_case  )  )
					torch.save(model.state_dict()					,	__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--rembert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained RemBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__UpperCAmelCase     							=						parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






