
"""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 (					__lowerCamelCase  :			Tuple						)	->   Optional[Any]:
  _snake_case   	=			DPTConfig(embedding_type='''hybrid'''						)

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

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

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

  return config, expected_shape
def    _UpperCAmelCase (					__lowerCamelCase  :			Dict						)	->   List[str]:
  _snake_case   	=			['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
  for k in ignore_keys:
    state_dict.pop(__lowerCamelCase					,   __lowerCamelCase						)
def    _UpperCAmelCase (					__lowerCamelCase  :			List[Any]						)	->   int:
  if (
      "pretrained.model" in name
      and "cls_token" not in name
      and "pos_embed" not in name
      and "patch_embed" not in name
  ):
    _snake_case   	=			name.replace('''pretrained.model'''					,   '''dpt.encoder'''						)
  if "pretrained.model" in name:
    _snake_case   	=			name.replace('''pretrained.model'''					,   '''dpt.embeddings'''						)
  if "patch_embed" in name:
    _snake_case   	=			name.replace('''patch_embed'''					,   ''''''						)
  if "pos_embed" in name:
    _snake_case   	=			name.replace('''pos_embed'''					,   '''position_embeddings'''						)
  if "attn.proj" in name:
    _snake_case   	=			name.replace('''attn.proj'''					,   '''attention.output.dense'''						)
  if "proj" in name and "project" not in name:
    _snake_case   	=			name.replace('''proj'''					,   '''projection'''						)
  if "blocks" in name:
    _snake_case   	=			name.replace('''blocks'''					,   '''layer'''						)
  if "mlp.fc1" in name:
    _snake_case   	=			name.replace('''mlp.fc1'''					,   '''intermediate.dense'''						)
  if "mlp.fc2" in name:
    _snake_case   	=			name.replace('''mlp.fc2'''					,   '''output.dense'''						)
  if "norm1" in name and "backbone" not in name:
    _snake_case   	=			name.replace('''norm1'''					,   '''layernorm_before'''						)
  if "norm2" in name and "backbone" not in name:
    _snake_case   	=			name.replace('''norm2'''					,   '''layernorm_after'''						)
  if "scratch.output_conv" in name:
    _snake_case   	=			name.replace('''scratch.output_conv'''					,   '''head'''						)
  if "scratch" in name:
    _snake_case   	=			name.replace('''scratch'''					,   '''neck'''						)
  if "layer1_rn" in name:
    _snake_case   	=			name.replace('''layer1_rn'''					,   '''convs.0'''						)
  if "layer2_rn" in name:
    _snake_case   	=			name.replace('''layer2_rn'''					,   '''convs.1'''						)
  if "layer3_rn" in name:
    _snake_case   	=			name.replace('''layer3_rn'''					,   '''convs.2'''						)
  if "layer4_rn" in name:
    _snake_case   	=			name.replace('''layer4_rn'''					,   '''convs.3'''						)
  if "refinenet" in name:
    _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
    _snake_case   	=			name.replace(f'''refinenet{layer_idx}'''					,   f'''fusion_stage.layers.{abs(layer_idx-4						)}'''						)
  if "out_conv" in name:
    _snake_case   	=			name.replace('''out_conv'''					,   '''projection'''						)
  if "resConfUnit1" in name:
    _snake_case   	=			name.replace('''resConfUnit1'''					,   '''residual_layer1'''						)
  if "resConfUnit2" in name:
    _snake_case   	=			name.replace('''resConfUnit2'''					,   '''residual_layer2'''						)
  if "conv1" in name:
    _snake_case   	=			name.replace('''conv1'''					,   '''convolution1'''						)
  if "conv2" in name:
    _snake_case   	=			name.replace('''conv2'''					,   '''convolution2'''						)
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    _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:
    _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:
    _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:
    _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:
    _snake_case   	=			name.replace('''pretrained.act_postprocess1.3'''					,   '''neck.reassemble_stage.layers.0.projection'''						)
  if "pretrained.act_postprocess1.4" in name:
    _snake_case   	=			name.replace('''pretrained.act_postprocess1.4'''					,   '''neck.reassemble_stage.layers.0.resize'''						)
  if "pretrained.act_postprocess2.3" in name:
    _snake_case   	=			name.replace('''pretrained.act_postprocess2.3'''					,   '''neck.reassemble_stage.layers.1.projection'''						)
  if "pretrained.act_postprocess2.4" in name:
    _snake_case   	=			name.replace('''pretrained.act_postprocess2.4'''					,   '''neck.reassemble_stage.layers.1.resize'''						)
  if "pretrained.act_postprocess3.3" in name:
    _snake_case   	=			name.replace('''pretrained.act_postprocess3.3'''					,   '''neck.reassemble_stage.layers.2.projection'''						)
  if "pretrained.act_postprocess4.3" in name:
    _snake_case   	=			name.replace('''pretrained.act_postprocess4.3'''					,   '''neck.reassemble_stage.layers.3.projection'''						)
  if "pretrained.act_postprocess4.4" in name:
    _snake_case   	=			name.replace('''pretrained.act_postprocess4.4'''					,   '''neck.reassemble_stage.layers.3.resize'''						)
  if "pretrained" in name:
    _snake_case   	=			name.replace('''pretrained'''					,   '''dpt'''						)
  if "bn" in name:
    _snake_case   	=			name.replace('''bn'''					,   '''batch_norm'''						)
  if "head" in name:
    _snake_case   	=			name.replace('''head'''					,   '''head.head'''						)
  if "encoder.norm" in name:
    _snake_case   	=			name.replace('''encoder.norm'''					,   '''layernorm'''						)
  if "auxlayer" in name:
    _snake_case   	=			name.replace('''auxlayer'''					,   '''auxiliary_head.head'''						)
  if "backbone" in name:
    _snake_case   	=			name.replace('''backbone'''					,   '''backbone.bit.encoder'''						)

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

  if "stem.conv" in name:
    _snake_case   	=			name.replace('''stem.conv'''					,   '''bit.embedder.convolution'''						)
  if "blocks" in name:
    _snake_case   	=			name.replace('''blocks'''					,   '''layers'''						)
  if "convolution" in name and "backbone" in name:
    _snake_case   	=			name.replace('''convolution'''					,   '''conv'''						)
  if "layer" in name and "backbone" in name:
    _snake_case   	=			name.replace('''layer'''					,   '''layers'''						)
  if "backbone.bit.encoder.bit" in name:
    _snake_case   	=			name.replace('''backbone.bit.encoder.bit'''					,   '''backbone.bit'''						)
  if "embedder.conv" in name:
    _snake_case   	=			name.replace('''embedder.conv'''					,   '''embedder.convolution'''						)
  if "backbone.bit.encoder.stem.norm" in name:
    _snake_case   	=			name.replace('''backbone.bit.encoder.stem.norm'''					,   '''backbone.bit.embedder.norm'''						)
  return name
def    _UpperCAmelCase (					__lowerCamelCase  :			Optional[int]					,   __lowerCamelCase  :			Dict						)	->   Union[str, Any]:
  for i in range(config.num_hidden_layers						):
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case   	=			state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''						)
    _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
    _snake_case   	=			in_proj_weight[: config.hidden_size, :]
    _snake_case   	=			in_proj_bias[: config.hidden_size]
    _snake_case   	=			in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case   	=			in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case   	=			in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case   	=			in_proj_bias[-config.hidden_size :]
def    _UpperCAmelCase (					)	->   Optional[int]:
  _snake_case   	=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case   	=			Image.open(requests.get(__lowerCamelCase					,   stream=__lowerCamelCase						).raw						)
  return im
@torch.no_grad()
def    _UpperCAmelCase (					__lowerCamelCase  :			str					,   __lowerCamelCase  :			Optional[int]					,   __lowerCamelCase  :			List[Any]					,   __lowerCamelCase  :			Any					,   __lowerCamelCase  :			str						)	->   List[Any]:
  _snake_case   	=			get_dpt_config(__lowerCamelCase						)
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  _snake_case   	=			torch.load(__lowerCamelCase					,   map_location='''cpu'''						)
  # remove certain keys
  remove_ignore_keys_(__lowerCamelCase						)
  # rename keys
  for key in state_dict.copy().keys():
    _snake_case   	=			state_dict.pop(__lowerCamelCase						)
    _snake_case   	=			val
  # read in qkv matrices
  read_in_q_k_v(__lowerCamelCase					,   __lowerCamelCase						)

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

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

  _snake_case   	=			prepare_img()
  _snake_case   	=			image_processor(__lowerCamelCase					,   return_tensors='''pt'''						)

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

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

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

  if pytorch_dump_folder_path is not None:
    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:
    model.push_to_hub('''ybelkada/dpt-hybrid-midas'''						)
    image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''						)


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=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__        =					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 typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_           =				logging.get_logger(__name__)




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				Union[str, Any]		)     ->					Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			b.T
 _UpperCamelCase:  Optional[Any]    						=			np.sum(np.square(lowercase		)	,       axis=1		)
 _UpperCamelCase:  List[Any]    						=			np.sum(np.square(lowercase		)	,       axis=0		)
 _UpperCamelCase:  Tuple    						=			np.matmul(lowercase	,       lowercase		)
 _UpperCamelCase:  Tuple    						=			aa[:, None] - 2 * ab + ba[None, :]
 return d




def    lowerCAmelCase_  (		lowercase:				List[str]	,       lowercase:				List[Any]		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			x.reshape(-1	,       3		)
 _UpperCamelCase:  Dict    						=			squared_euclidean_distance(lowercase	,       lowercase		)
 return np.argmin(lowercase	,       axis=1		)





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = ['''pixel_values''']



   def __init__(						self       :	List[str]		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	bool = True		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	bool = True		,	_lowercase       :	bool = True		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  Tuple    						=			size if size is not None else {'''height''': 256, '''width''': 256}
    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Dict    						=			np.array(_lowercase				) if clusters is not None else None
    _UpperCamelCase:  Optional[Any]    						=			do_resize
    _UpperCamelCase:  Union[str, Any]    						=			size
    _UpperCamelCase:  Optional[int]    						=			resample
    _UpperCamelCase:  Any    						=			do_normalize
    _UpperCamelCase:  int    						=			do_color_quantize



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Dict[str, int]		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""				)
    return resize(
        _lowercase		,	size=(size['''height'''], size['''width'''])		,	resample=_lowercase		,	data_format=_lowercase		,	**_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			rescale(image=_lowercase		,	scale=1 / 127.5		,	data_format=_lowercase				)
    _UpperCamelCase:  List[Any]    						=			image - 1
    return image



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	ImageInput		,	_lowercase       :	bool = None		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = None		,	_lowercase       :	bool = None		,	_lowercase       :	Optional[bool] = None		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	Optional[Union[str, TensorType]] = None		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST		,	**_lowercase       :	List[str]		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			do_resize if do_resize is not None else self.do_resize
    _UpperCamelCase:  Optional[Any]    						=			size if size is not None else self.size
    _UpperCamelCase:  Optional[Any]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			resample if resample is not None else self.resample
    _UpperCamelCase:  Union[str, Any]    						=			do_normalize if do_normalize is not None else self.do_normalize
    _UpperCamelCase:  Any    						=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    _UpperCamelCase:  Dict    						=			clusters if clusters is not None else self.clusters
    _UpperCamelCase:  Optional[Any]    						=			np.array(_lowercase				)

    _UpperCamelCase:  List[str]    						=			make_list_of_images(_lowercase				)

    if not valid_images(_lowercase				):
     raise ValueError(
         '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
         '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

    if do_resize and size is None or resample is None:
     raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

    if do_color_quantize and clusters is None:
     raise ValueError('''Clusters must be specified if do_color_quantize is True.'''				)

    # All transformations expect numpy arrays.
    _UpperCamelCase:  Optional[int]    						=			[to_numpy_array(_lowercase				) for image in images]

    if do_resize:
     _UpperCamelCase:  int    						=			[self.resize(image=_lowercase		,	size=_lowercase		,	resample=_lowercase				) for image in images]

    if do_normalize:
     _UpperCamelCase:  Any    						=			[self.normalize(image=_lowercase				) for image in images]

    if do_color_quantize:
     _UpperCamelCase:  Tuple    						=			[to_channel_dimension_format(_lowercase		,	ChannelDimension.LAST				) for image in images]
     # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
     _UpperCamelCase:  Tuple    						=			np.array(_lowercase				)
     _UpperCamelCase:  List[Any]    						=			color_quantize(_lowercase		,	_lowercase				).reshape(images.shape[:-1]				)

     # flatten to (batch_size, height*width)
     _UpperCamelCase:  Any    						=			images.shape[0]
     _UpperCamelCase:  Optional[int]    						=			images.reshape(_lowercase		,	-1				)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     _UpperCamelCase:  Tuple    						=			list(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			[to_channel_dimension_format(_lowercase		,	_lowercase				) for image in images]

    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': images}
    return BatchFeature(data=_lowercase		,	tensor_type=_lowercase				)