




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()
SCREAMING_SNAKE_CASE__     :     str		    =       logging.get_logger(__name__)

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						DPTConfig(embedding_type='hybrid' )

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

			if "nyu" or "midas" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						7_68
						SCREAMING_SNAKE_CASE_								=						[1, 1, 1, 0.5]
						SCREAMING_SNAKE_CASE_								=						[2_56, 5_12, 7_68, 7_68]
						SCREAMING_SNAKE_CASE_								=						1_50
						SCREAMING_SNAKE_CASE_								=						16
						SCREAMING_SNAKE_CASE_								=						(1, 3_84, 3_84)
						SCREAMING_SNAKE_CASE_								=						False
						SCREAMING_SNAKE_CASE_								=						'project'

			if "ade" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						True
						SCREAMING_SNAKE_CASE_								=						7_68
						SCREAMING_SNAKE_CASE_								=						[1, 1, 1, 0.5]
						SCREAMING_SNAKE_CASE_								=						1_50
						SCREAMING_SNAKE_CASE_								=						16
						SCREAMING_SNAKE_CASE_								=						'huggingface/label-files'
						SCREAMING_SNAKE_CASE_								=						'ade20k-id2label.json'
						SCREAMING_SNAKE_CASE_								=						json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    repo_type='dataset' ) )       ,    'r' ) )
						SCREAMING_SNAKE_CASE_								=						{int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_								=						idalabel
						SCREAMING_SNAKE_CASE_								=						{v: k for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_								=						[1, 1_50, 4_80, 4_80]

			return config, expected_shape

def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						['pretrained.model.head.weight', 'pretrained.model.head.bias']
			for k in ignore_keys:
						state_dict.pop(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


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

			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('pretrained.act_postprocess1.3'       ,    'neck.reassemble_stage.layers.0.projection' )
			if "pretrained.act_postprocess1.4" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('pretrained.act_postprocess1.4'       ,    'neck.reassemble_stage.layers.0.resize' )
			if "pretrained.act_postprocess2.3" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('pretrained.act_postprocess2.3'       ,    'neck.reassemble_stage.layers.1.projection' )
			if "pretrained.act_postprocess2.4" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('pretrained.act_postprocess2.4'       ,    'neck.reassemble_stage.layers.1.resize' )
			if "pretrained.act_postprocess3.3" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('pretrained.act_postprocess3.3'       ,    'neck.reassemble_stage.layers.2.projection' )
			if "pretrained.act_postprocess4.3" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('pretrained.act_postprocess4.3'       ,    'neck.reassemble_stage.layers.3.projection' )
			if "pretrained.act_postprocess4.4" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('pretrained.act_postprocess4.4'       ,    'neck.reassemble_stage.layers.3.resize' )
			if "pretrained" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('pretrained'       ,    'dpt' )
			if "bn" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('bn'       ,    'batch_norm' )
			if "head" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('head'       ,    'head.head' )
			if "encoder.norm" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('encoder.norm'       ,    'layernorm' )
			if "auxlayer" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('auxlayer'       ,    'auxiliary_head.head' )
			if "backbone" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('backbone'       ,    'backbone.bit.encoder' )

			if ".." in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('..'       ,    '.' )

			if "stem.conv" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('stem.conv'       ,    'bit.embedder.convolution' )
			if "blocks" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('blocks'       ,    'layers' )
			if "convolution" in name and "backbone" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('convolution'       ,    'conv' )
			if "layer" in name and "backbone" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('layer'       ,    'layers' )
			if "backbone.bit.encoder.bit" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('backbone.bit.encoder.bit'       ,    'backbone.bit' )
			if "embedder.conv" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('embedder.conv'       ,    'embedder.convolution' )
			if "backbone.bit.encoder.stem.norm" in name:
						SCREAMING_SNAKE_CASE_								=						name.replace('backbone.bit.encoder.stem.norm'       ,    'backbone.bit.embedder.norm' )
			return name

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''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)
						SCREAMING_SNAKE_CASE_								=						state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight' )
						SCREAMING_SNAKE_CASE_								=						state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias' )
						# next, add query, keys and values (in that order) to the state dict
						SCREAMING_SNAKE_CASE_								=						in_proj_weight[: config.hidden_size, :]
						SCREAMING_SNAKE_CASE_								=						in_proj_bias[: config.hidden_size]
						SCREAMING_SNAKE_CASE_								=						in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						SCREAMING_SNAKE_CASE_								=						in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						SCREAMING_SNAKE_CASE_								=						in_proj_weight[
						    -config.hidden_size :, :
						]
						SCREAMING_SNAKE_CASE_								=						in_proj_bias[-config.hidden_size :]

def lowercase    (  )			->							Optional[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_								=						Image.open(requests.get(SCREAMING_SNAKE_CASE       ,    stream=SCREAMING_SNAKE_CASE ).raw )
			return im

@torch.no_grad()
def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						get_dpt_config(SCREAMING_SNAKE_CASE )
			# load original state_dict from URL
			# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
			SCREAMING_SNAKE_CASE_								=						torch.load(SCREAMING_SNAKE_CASE       ,    map_location='cpu' )
			# remove certain keys
			remove_ignore_keys_(SCREAMING_SNAKE_CASE )
			# rename keys
			for key in state_dict.copy().keys():
						SCREAMING_SNAKE_CASE_								=						state_dict.pop(SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE_								=						val
			# read in qkv matrices
			read_in_q_k_v(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# load HuggingFace model
			SCREAMING_SNAKE_CASE_								=						DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE ) if 'ade' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE )
			model.load_state_dict(SCREAMING_SNAKE_CASE )
			model.eval()

			# Check outputs on an image
			SCREAMING_SNAKE_CASE_								=						4_80 if 'ade' in checkpoint_url else 3_84
			SCREAMING_SNAKE_CASE_								=						DPTImageProcessor(size=SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						prepare_img()
			SCREAMING_SNAKE_CASE_								=						image_processor(SCREAMING_SNAKE_CASE       ,    return_tensors='pt' )

			# forward pass
			SCREAMING_SNAKE_CASE_								=						model(**SCREAMING_SNAKE_CASE ).logits if 'ade' in checkpoint_url else model(**SCREAMING_SNAKE_CASE ).predicted_depth

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

						Image.fromarray((prediction / prediction.max()) * 2_55 ).show()

			if pytorch_dump_folder_path is not None:
						Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
						print(F'Saving model to {pytorch_dump_folder_path}' )
						model.save_pretrained(SCREAMING_SNAKE_CASE )
						print(F'Saving image processor to {pytorch_dump_folder_path}' )
						image_processor.save_pretrained(SCREAMING_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__":
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       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",
				)

				SCREAMING_SNAKE_CASE__     :     str		    =       parser.parse_args()
				convert_dpt_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
				)






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				if depth_multiplier <= 0:
							raise ValueError('depth_multiplier must be greater than zero.'      )

				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						depth_multiplier
				SCREAMING_SNAKE_CASE_								=						depth_divisible_by
				SCREAMING_SNAKE_CASE_								=						min_depth
				SCREAMING_SNAKE_CASE_								=						expand_ratio
				SCREAMING_SNAKE_CASE_								=						output_stride
				SCREAMING_SNAKE_CASE_								=						first_layer_is_expansion
				SCREAMING_SNAKE_CASE_								=						finegrained_output
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						tf_padding
				SCREAMING_SNAKE_CASE_								=						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						semantic_loss_ignore_index




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				return OrderedDict([('pixel_values', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})]      )
				else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
