
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()
lowerCamelCase_  :	Optional[Any] 				=					logging.get_logger(__name__)







def     A__   (  lowerCamelCase  )					->		Any:
				UpperCamelCase_:							Optional[int]		=  DPTConfig()

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

				if "ade" in checkpoint_url:
								UpperCamelCase_:							Tuple		=  True

								UpperCamelCase_:							Any		=  1_50
								UpperCamelCase_:							str		=  """huggingface/label-files"""
								UpperCamelCase_:							List[Any]		=  """ade20k-id2label.json"""
								UpperCamelCase_:							List[Any]		=  json.load(open(cached_download(hf_hub_url(lowerCamelCase       ,      lowerCamelCase       ,      repo_type="""dataset"""  )  )       ,      """r"""  )  )
								UpperCamelCase_:							str		=  {int(lowerCamelCase  ): v for k, v in idalabel.items()}
								UpperCamelCase_:							str		=  idalabel
								UpperCamelCase_:							str		=  {v: k for k, v in idalabel.items()}
								UpperCamelCase_:							Tuple		=  [1, 1_50, 4_80, 4_80]

				return config, expected_shape







def     A__   (  lowerCamelCase  )					->		str:
				UpperCamelCase_:							int		=  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase       ,      lowerCamelCase  )







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

				return name







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[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)
								UpperCamelCase_:							Union[str, Any]		=  state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''  )
								UpperCamelCase_:							Any		=  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_:							Tuple		=  in_proj_weight[: config.hidden_size, :]
								UpperCamelCase_:							int		=  in_proj_bias[: config.hidden_size]
								UpperCamelCase_:							Tuple		=  in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								UpperCamelCase_:							int		=  in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								UpperCamelCase_:							Optional[Any]		=  in_proj_weight[
								    -config.hidden_size :, :
								]
								UpperCamelCase_:							Any		=  in_proj_bias[-config.hidden_size :]







def     A__   (  )					->		Dict:
				UpperCamelCase_:							Union[str, Any]		=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:							List[Any]		=  Image.open(requests.get(lowerCamelCase       ,      stream=lowerCamelCase  ).raw  )
				return im







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

				# load HuggingFace model
				UpperCamelCase_:							Optional[int]		=  DPTForSemanticSegmentation(lowerCamelCase  ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase  )
				model.load_state_dict(lowerCamelCase  )
				model.eval()

				# Check outputs on an image
				UpperCamelCase_:							str		=  4_80 if """ade""" in checkpoint_url else 3_84
				UpperCamelCase_:							Union[str, Any]		=  DPTImageProcessor(size=lowerCamelCase  )

				UpperCamelCase_:							List[Any]		=  prepare_img()
				UpperCamelCase_:							Optional[int]		=  image_processor(lowerCamelCase       ,      return_tensors="""pt"""  )

				# forward pass
				UpperCamelCase_:							Optional[int]		=  model(**lowerCamelCase  ).logits if """ade""" in checkpoint_url else model(**lowerCamelCase  ).predicted_depth

				# Assert logits
				UpperCamelCase_:							Optional[int]		=  torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]  )
				if "ade" in checkpoint_url:
								UpperCamelCase_:							int		=  torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]  )
				assert outputs.shape == torch.Size(lowerCamelCase  )
				assert (
				    torch.allclose(outputs[0, 0, :3, :3]       ,      lowerCamelCase       ,      atol=1E-4  )
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3]       ,      lowerCamelCase  )
				)

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

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


if __name__ == "__main__":
							lowerCamelCase_  :	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=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.""",
							)

							lowerCamelCase_  :	Any 				=					parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase_  :	Dict 				=					{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							lowerCamelCase_  :	Any 				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






