
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 :			str              =	logging.get_logger(__name__)


def 				A	( lowercase			)       ->  int:
	'''simple docstring'''

	UpperCamelCase          =					DPTConfig(embedding_type='hybrid'			)

	if "large" in checkpoint_url:
		UpperCamelCase          =					1_024
		UpperCamelCase          =					4_096
		UpperCamelCase          =					24
		UpperCamelCase          =					16
		UpperCamelCase          =					[5, 11, 17, 23]
		UpperCamelCase          =					[256, 512, 1_024, 1_024]
		UpperCamelCase          =					(1, 384, 384)

	if "nyu" or "midas" in checkpoint_url:
		UpperCamelCase          =					768
		UpperCamelCase          =					[1, 1, 1, 0.5]
		UpperCamelCase          =					[256, 512, 768, 768]
		UpperCamelCase          =					150
		UpperCamelCase          =					16
		UpperCamelCase          =					(1, 384, 384)
		UpperCamelCase          =					False
		UpperCamelCase          =					'project'

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

	return config, expected_shape


def 				A	( lowercase			)       ->  Dict:
	'''simple docstring'''

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


def 				A	( lowercase			)       ->  Dict:
	'''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          =					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'   ,	''			)
	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 and "backbone" not in name:
		UpperCamelCase          =					name.replace('norm1'   ,	'layernorm_before'			)
	if "norm2" in name and "backbone" not 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'			)
	if "backbone" in name:
		UpperCamelCase          =					name.replace('backbone'   ,	'backbone.bit.encoder'			)

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

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


def 				A	( lowercase   ,	lowercase			)       ->  int:
	'''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          =					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 				A	( )       ->  Tuple:
	'''simple docstring'''

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


@torch.no_grad()
def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  str:
	'''simple docstring'''

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

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

	# Check outputs on an image
	UpperCamelCase          =					480 if 'ade' in checkpoint_url else 384
	UpperCamelCase          =					DPTImageProcessor(size=lowercase			)

	UpperCamelCase          =					prepare_img()
	UpperCamelCase          =					image_processor(lowercase   ,	return_tensors='pt'			)

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

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

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

	if pytorch_dump_folder_path is not None:
		Path(lowercase			).mkdir(exist_ok=lowercase			)
		print(f'''Saving model to {pytorch_dump_folder_path}'''			)
		model.save_pretrained(lowercase			)
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
		image_processor.save_pretrained(lowercase			)

	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 :			List[Any]              =	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 :			int              =	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 pprint

import requests

_UpperCAmelCase :			Union[str, Any]              =	"https://zenquotes.io/api"


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/today'			).json()


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/random'			).json()


if __name__ == "__main__":
					_UpperCAmelCase :			str              =	random_quotes()
					pprint.pprint(response)
