"""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()
UpperCAmelCase__  		=						logging.get_logger(__name__)






def     __UpperCAmelCase					(			lowercase		):







		"""simple docstring"""
		_UpperCAmelCase						 =      DPTConfig()

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

		if "ade" in checkpoint_url:
				_UpperCAmelCase						 =      True

				_UpperCAmelCase						 =      1_50
				_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, 1_50, 4_80, 4_80]

		return config, expected_shape






def     __UpperCAmelCase					(			lowercase		):







		"""simple docstring"""
		_UpperCAmelCase						 =      ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
		for k in ignore_keys:
				state_dict.pop(lowercase   ,lowercase		)






def     __UpperCAmelCase					(			lowercase		):







		"""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"""   ,"""patch_embeddings"""		)
		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:
				_UpperCAmelCase						 =      name.replace("""norm1"""   ,"""layernorm_before"""		)
		if "norm2" 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"""		)

		return name






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







		"""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     __UpperCAmelCase					(			):







		"""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     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase		):







		"""simple docstring"""
		_UpperCAmelCase ,				_UpperCAmelCase						 =      get_dpt_config(lowercase		)
		# load original state_dict from URL
		_UpperCAmelCase						 =      torch.hub.load_state_dict_from_url(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						 =      4_80 if """ade""" in checkpoint_url else 3_84
		_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

		# Assert logits
		_UpperCAmelCase						 =      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:
				_UpperCAmelCase						 =      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(lowercase		)
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]   ,lowercase   ,atol=1E-4		)
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]   ,lowercase		)
		)

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


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=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.""",
				)

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

"""simple docstring"""


import random






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase = False		):







  """simple docstring"""
  _UpperCAmelCase						 =      {i: [] for i in range(lowercase		)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowercase		)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowercase		):
    for j in range(i + 1   ,lowercase		):
      if random.random() < probability:
        graph[i].append(lowercase		)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowercase		)
  return graph






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return {
      i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()
