
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_						(				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       DPTConfig(embedding_type='hybrid'				)

	if "large" in checkpoint_url:
		lowercase	    =       1024
		lowercase	    =       4096
		lowercase	    =       24
		lowercase	    =       16
		lowercase	    =       [5, 11, 17, 23]
		lowercase	    =       [256, 512, 1024, 1024]
		lowercase	    =       (1, 384, 384)

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

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

	return config, expected_shape






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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	if (
	    "pretrained.model" in name
	    and "cls_token" not in name
	    and "pos_embed" not in name
	    and "patch_embed" not in name
	):
		lowercase	    =       name.replace('pretrained.model'					,				'dpt.encoder'				)
	if "pretrained.model" in name:
		lowercase	    =       name.replace('pretrained.model'					,				'dpt.embeddings'				)
	if "patch_embed" in name:
		lowercase	    =       name.replace('patch_embed'					,				''				)
	if "pos_embed" in name:
		lowercase	    =       name.replace('pos_embed'					,				'position_embeddings'				)
	if "attn.proj" in name:
		lowercase	    =       name.replace('attn.proj'					,				'attention.output.dense'				)
	if "proj" in name and "project" not in name:
		lowercase	    =       name.replace('proj'					,				'projection'				)
	if "blocks" in name:
		lowercase	    =       name.replace('blocks'					,				'layer'				)
	if "mlp.fc1" in name:
		lowercase	    =       name.replace('mlp.fc1'					,				'intermediate.dense'				)
	if "mlp.fc2" in name:
		lowercase	    =       name.replace('mlp.fc2'					,				'output.dense'				)
	if "norm1" in name and "backbone" not in name:
		lowercase	    =       name.replace('norm1'					,				'layernorm_before'				)
	if "norm2" in name and "backbone" not in name:
		lowercase	    =       name.replace('norm2'					,				'layernorm_after'				)
	if "scratch.output_conv" in name:
		lowercase	    =       name.replace('scratch.output_conv'					,				'head'				)
	if "scratch" in name:
		lowercase	    =       name.replace('scratch'					,				'neck'				)
	if "layer1_rn" in name:
		lowercase	    =       name.replace('layer1_rn'					,				'convs.0'				)
	if "layer2_rn" in name:
		lowercase	    =       name.replace('layer2_rn'					,				'convs.1'				)
	if "layer3_rn" in name:
		lowercase	    =       name.replace('layer3_rn'					,				'convs.2'				)
	if "layer4_rn" in name:
		lowercase	    =       name.replace('layer4_rn'					,				'convs.3'				)
	if "refinenet" in name:
		lowercase	    =       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
		lowercase	    =       name.replace(F'''refinenet{layer_idx}'''					,				F'''fusion_stage.layers.{abs(layer_idx-4				)}'''				)
	if "out_conv" in name:
		lowercase	    =       name.replace('out_conv'					,				'projection'				)
	if "resConfUnit1" in name:
		lowercase	    =       name.replace('resConfUnit1'					,				'residual_layer1'				)
	if "resConfUnit2" in name:
		lowercase	    =       name.replace('resConfUnit2'					,				'residual_layer2'				)
	if "conv1" in name:
		lowercase	    =       name.replace('conv1'					,				'convolution1'				)
	if "conv2" in name:
		lowercase	    =       name.replace('conv2'					,				'convolution2'				)
	# readout blocks
	if "pretrained.act_postprocess1.0.project.0" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess1.0.project.0'					,				'neck.reassemble_stage.readout_projects.0.0'				)
	if "pretrained.act_postprocess2.0.project.0" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess2.0.project.0'					,				'neck.reassemble_stage.readout_projects.1.0'				)
	if "pretrained.act_postprocess3.0.project.0" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess3.0.project.0'					,				'neck.reassemble_stage.readout_projects.2.0'				)
	if "pretrained.act_postprocess4.0.project.0" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess4.0.project.0'					,				'neck.reassemble_stage.readout_projects.3.0'				)

	# resize blocks
	if "pretrained.act_postprocess1.3" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess1.3'					,				'neck.reassemble_stage.layers.0.projection'				)
	if "pretrained.act_postprocess1.4" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess1.4'					,				'neck.reassemble_stage.layers.0.resize'				)
	if "pretrained.act_postprocess2.3" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess2.3'					,				'neck.reassemble_stage.layers.1.projection'				)
	if "pretrained.act_postprocess2.4" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess2.4'					,				'neck.reassemble_stage.layers.1.resize'				)
	if "pretrained.act_postprocess3.3" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess3.3'					,				'neck.reassemble_stage.layers.2.projection'				)
	if "pretrained.act_postprocess4.3" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess4.3'					,				'neck.reassemble_stage.layers.3.projection'				)
	if "pretrained.act_postprocess4.4" in name:
		lowercase	    =       name.replace('pretrained.act_postprocess4.4'					,				'neck.reassemble_stage.layers.3.resize'				)
	if "pretrained" in name:
		lowercase	    =       name.replace('pretrained'					,				'dpt'				)
	if "bn" in name:
		lowercase	    =       name.replace('bn'					,				'batch_norm'				)
	if "head" in name:
		lowercase	    =       name.replace('head'					,				'head.head'				)
	if "encoder.norm" in name:
		lowercase	    =       name.replace('encoder.norm'					,				'layernorm'				)
	if "auxlayer" in name:
		lowercase	    =       name.replace('auxlayer'					,				'auxiliary_head.head'				)
	if "backbone" in name:
		lowercase	    =       name.replace('backbone'					,				'backbone.bit.encoder'				)

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

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






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






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






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

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

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

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

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

	if show_prediction:
		lowercase	    =       (
		    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       =					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


_snake_case      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Optional[Any]:
     UpperCamelCase						    =					b.T
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=1			)
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=0			)
     UpperCamelCase						    =					np.matmul(_lowercase						,     _lowercase			)
     UpperCamelCase						    =					aa[:, None] - 2 * ab + ba[None, :]
     return d

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      List[Any]:
     UpperCamelCase						    =					x.reshape(-1						,     3			)
     UpperCamelCase						    =					squared_euclidean_distance(_lowercase						,     _lowercase			)
     return np.argmin(_lowercase						,     axis=1			)







class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["pixel_values"]

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






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					size if size is not None else {'height': 2_56, 'width': 2_56}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   ) if clusters is not None else None
      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					resample
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					do_color_quantize

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      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(
          SCREAMING_SNAKE_CASE__					, size=(size['height'], size['width'])					, resample=SCREAMING_SNAKE_CASE__					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, ):






      """simple docstring"""



      UpperCamelCase						    =					rescale(image=SCREAMING_SNAKE_CASE__					, scale=1 / 127.5					, data_format=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					image - 1
      return image






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






      """simple docstring"""



      UpperCamelCase						    =					do_resize if do_resize is not None else self.do_resize
      UpperCamelCase						    =					size if size is not None else self.size
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					resample if resample is not None else self.resample
      UpperCamelCase						    =					do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase						    =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCamelCase						    =					clusters if clusters is not None else self.clusters
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					make_list_of_images(SCREAMING_SNAKE_CASE__   )

      if not valid_images(SCREAMING_SNAKE_CASE__   ):
           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						    =					[to_numpy_array(SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_resize:
           UpperCamelCase						    =					[self.resize(image=SCREAMING_SNAKE_CASE__					, size=SCREAMING_SNAKE_CASE__					, resample=SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_normalize:
           UpperCamelCase						    =					[self.normalize(image=SCREAMING_SNAKE_CASE__   ) for image in images]

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

           # flatten to (batch_size, height*width)
           UpperCamelCase						    =					images.shape[0]
           UpperCamelCase						    =					images.reshape(SCREAMING_SNAKE_CASE__					, -1   )

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

      UpperCamelCase						    =					{'input_ids': images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE__					, tensor_type=SCREAMING_SNAKE_CASE__   )


