


'''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()
__lowercase      :		Tuple			=      logging.get_logger(__name__)






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Any  ):
					__a     :			List[str]          =    DPTConfig()

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

					if "ade" in checkpoint_url:
										__a     :			int          =    True

										__a     :			Optional[int]          =    150
										__a     :			List[str]          =    'huggingface/label-files'
										__a     :			Optional[int]          =    'ade20k-id2label.json'
										__a     :			Any          =    json.load(open(cached_download(hf_hub_url(A__		, A__		, repo_type='dataset'  )  )		, 'r'  )  )
										__a     :			Dict          =    {int(A__  ): v for k, v in idalabel.items()}
										__a     :			Dict          =    idalabel
										__a     :			Any          =    {v: k for k, v in idalabel.items()}
										__a     :			List[str]          =    [1, 150, 480, 480]

					return config, expected_shape






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Dict  ):
					__a     :			List[Any]          =    ['pretrained.model.head.weight', 'pretrained.model.head.bias']
					for k in ignore_keys:
										state_dict.pop(A__		, A__  )






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

					return name






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






def     lowerCamelCase				():
					__a     :			Optional[Any]          =    'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a     :			int          =    Image.open(requests.get(A__		, stream=A__  ).raw  )
					return im






@torch.no_grad()
def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  str		, _SCREAMING_SNAKE_CASE :  Optional[int]		, _SCREAMING_SNAKE_CASE :  Optional[int]		, _SCREAMING_SNAKE_CASE :  str  ):
					__a       ,							__a     :			List[Any]          =    get_dpt_config(A__  )
					# load original state_dict from URL
					__a     :			List[Any]          =    torch.hub.load_state_dict_from_url(A__		, map_location='cpu'  )
					# remove certain keys
					remove_ignore_keys_(A__  )
					# rename keys
					for key in state_dict.copy().keys():
										__a     :			List[Any]          =    state_dict.pop(A__  )
										__a     :			Tuple          =    val
					# read in qkv matrices
					read_in_q_k_v(A__		, A__  )

					# load HuggingFace model
					__a     :			int          =    DPTForSemanticSegmentation(A__  ) if 'ade' in checkpoint_url else DPTForDepthEstimation(A__  )
					model.load_state_dict(A__  )
					model.eval()

					# Check outputs on an image
					__a     :			List[Any]          =    480 if 'ade' in checkpoint_url else 384
					__a     :			List[str]          =    DPTImageProcessor(size=A__  )

					__a     :			Tuple          =    prepare_img()
					__a     :			List[str]          =    image_processor(A__		, return_tensors='pt'  )

					# forward pass
					__a     :			Any          =    model(**A__  ).logits if 'ade' in checkpoint_url else model(**A__  ).predicted_depth

					# Assert logits
					__a     :			List[Any]          =    torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]  )
					if "ade" in checkpoint_url:
										__a     :			Union[str, Any]          =    torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]  )
					assert outputs.shape == torch.Size(A__  )
					assert (
					    torch.allclose(outputs[0, 0, :3, :3]		, A__		, atol=1e-4  )
					    if "ade" in checkpoint_url
					    else torch.allclose(outputs[0, :3, :3]		, A__  )
					)

					Path(A__  ).mkdir(exist_ok=A__  )
					print(F"""Saving model to {pytorch_dump_folder_path}"""  )
					model.save_pretrained(A__  )
					print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
					image_processor.save_pretrained(A__  )

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


if __name__ == "__main__":
	__lowercase      :		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.',
	)

	__lowercase      :		Tuple			=      parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





'''simple docstring'''
import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowercase__											=version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__=False  ,      ):
	output_path.parent.mkdir(parents=A__  ,      exist_ok=A__						)
	# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
	# so we check the torch version for backwards compatibility
	if is_torch_less_than_1_11:
		export(
		    A__  ,      A__  ,      f=output_path.as_posix()  ,      input_names=A__  ,      output_names=A__  ,      dynamic_axes=A__  ,      do_constant_folding=A__  ,      use_external_data_format=A__  ,      enable_onnx_checker=A__  ,      opset_version=A__  ,      )
	else:
		export(
		    A__  ,      A__  ,      f=output_path.as_posix()  ,      input_names=A__  ,      output_names=A__  ,      dynamic_axes=A__  ,      do_constant_folding=A__  ,      opset_version=A__  ,      )






@torch.no_grad()
def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__ = False						):
	a_      				=						torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		a_      				=						"""cuda"""
	elif fpaa and not torch.cuda.is_available():
		raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""						)
	else:
		a_      				=						"""cpu"""
	a_      				=						Path(A__						)

	# VAE DECODER
	a_      				=						AutoencoderKL.from_pretrained(model_path + """/vae"""						)
	a_      				=						vae_decoder.config.latent_channels
	# forward only through the decoder part
	a_      				=						vae_decoder.decode
	onnx_export(
	    A__  ,      model_args=(
	        torch.randn(1  ,      A__  ,      25  ,      25						).to(device=A__  ,      dtype=A__						),
	        False,
	    )  ,      output_path=output_path / """vae_decoder""" / """model.onnx"""  ,      ordered_input_names=["""latent_sample""", """return_dict"""]  ,      output_names=["""sample"""]  ,      dynamic_axes={
	        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
	    }  ,      opset=A__  ,      )
	del vae_decoder


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()

		parser.add_argument(
		    '--model_path',
		    type=str,
		    required=True,
		    help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
		)

		parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
		parser.add_argument(
		    '--opset',
		    default=14,
		    type=int,
		    help='The version of the ONNX operator set to use.',
		)
		parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')

		lowercase__											=parser.parse_args()
		print(args.output_path)
		convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
		print('SD: Done: ONNX')
