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    lowerCAmelCase_  (		lowercase:				Tuple		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  Union[str, Any]    						=			DPTConfig(embedding_type='''hybrid'''		)

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

 if "nyu" or "midas" in checkpoint_url:
  _UpperCamelCase:  int    						=			768
  _UpperCamelCase:  Tuple    						=			[1, 1, 1, 0.5]
  _UpperCamelCase:  Optional[int]    						=			[256, 512, 768, 768]
  _UpperCamelCase:  Optional[int]    						=			150
  _UpperCamelCase:  Any    						=			16
  _UpperCamelCase:  List[str]    						=			(1, 384, 384)
  _UpperCamelCase:  Optional[Any]    						=			False
  _UpperCamelCase:  List[str]    						=			'''project'''

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

 return config, expected_shape




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







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




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







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

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

 if "stem.conv" in name:
  _UpperCamelCase:  int    						=			name.replace('''stem.conv'''	,       '''bit.embedder.convolution'''		)
 if "blocks" in name:
  _UpperCamelCase:  List[str]    						=			name.replace('''blocks'''	,       '''layers'''		)
 if "convolution" in name and "backbone" in name:
  _UpperCamelCase:  List[str]    						=			name.replace('''convolution'''	,       '''conv'''		)
 if "layer" in name and "backbone" in name:
  _UpperCamelCase:  List[Any]    						=			name.replace('''layer'''	,       '''layers'''		)
 if "backbone.bit.encoder.bit" in name:
  _UpperCamelCase:  Optional[int]    						=			name.replace('''backbone.bit.encoder.bit'''	,       '''backbone.bit'''		)
 if "embedder.conv" in name:
  _UpperCamelCase:  Optional[Any]    						=			name.replace('''embedder.conv'''	,       '''embedder.convolution'''		)
 if "backbone.bit.encoder.stem.norm" in name:
  _UpperCamelCase:  Optional[int]    						=			name.replace('''backbone.bit.encoder.stem.norm'''	,       '''backbone.bit.embedder.norm'''		)
 return name




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







 '''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:  Any    						=			state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""		)
  _UpperCamelCase:  Optional[int]    						=			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:  Optional[int]    						=			in_proj_weight[: config.hidden_size, :]
  _UpperCamelCase:  List[str]    						=			in_proj_bias[: config.hidden_size]
  _UpperCamelCase:  str    						=			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  _UpperCamelCase:  Union[str, Any]    						=			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  _UpperCamelCase:  str    						=			in_proj_weight[
      -config.hidden_size :, :
  ]
  _UpperCamelCase:  Any    						=			in_proj_bias[-config.hidden_size :]




def    lowerCAmelCase_  (		)     ->					Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _UpperCamelCase:  List[Any]    						=			Image.open(requests.get(lowercase	,       stream=lowercase		).raw		)
 return im




@torch.no_grad()
def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				Optional[int]	,       lowercase:				List[Any]	,       lowercase:				Any	,       lowercase:				str		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase						,				_UpperCamelCase:  Any    						=			get_dpt_config(lowercase		)
 # load original state_dict from URL
 # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
 _UpperCamelCase:  Union[str, Any]    						=			torch.load(lowercase	,       map_location='''cpu'''		)
 # remove certain keys
 remove_ignore_keys_(lowercase		)
 # rename keys
 for key in state_dict.copy().keys():
  _UpperCamelCase:  Dict    						=			state_dict.pop(lowercase		)
  _UpperCamelCase:  Any    						=			val
 # read in qkv matrices
 read_in_q_k_v(lowercase	,       lowercase		)

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

 # Check outputs on an image
 _UpperCamelCase:  Optional[Any]    						=			480 if '''ade''' in checkpoint_url else 384
 _UpperCamelCase:  Tuple    						=			DPTImageProcessor(size=lowercase		)

 _UpperCamelCase:  int    						=			prepare_img()
 _UpperCamelCase:  Any    						=			image_processor(lowercase	,       return_tensors='''pt'''		)

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

 if show_prediction:
  _UpperCamelCase:  Union[str, Any]    						=			(
      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
  )
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = VQModel
   lowerCAmelCase   :     List[str]	      = '''sample'''



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Tuple=(32, 32)				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			4
    _UpperCamelCase:  Tuple    						=			3

    _UpperCamelCase:  List[str]    						=			floats_tensor((batch_size, num_channels) + sizes				).to(_lowercase				)

    return {"sample": image}



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return (3, 32, 32)



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    return (3, 32, 32)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			{
        '''block_out_channels''': [32, 64],
        '''in_channels''': 3,
        '''out_channels''': 3,
        '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
        '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
        '''latent_channels''': 3,
    }
    _UpperCamelCase:  Optional[Any]    						=			self.dummy_input
    return init_dict, inputs_dict



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  int    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''		,	output_loading_info=_lowercase				)
    self.assertIsNotNone(_lowercase				)
    self.assertEqual(len(loading_info['''missing_keys''']				)		,	0				)

    model.to(_lowercase				)
    _UpperCamelCase:  Tuple    						=			model(**self.dummy_input				)

    assert image is not None, "Make sure output is not None"



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
    model.to(_lowercase				).eval()

    torch.manual_seed(0				)
    if torch.cuda.is_available():
     torch.cuda.manual_seed_all(0				)

    _UpperCamelCase:  int    						=			torch.randn(1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size				)
    _UpperCamelCase:  Tuple    						=			image.to(_lowercase				)
    with torch.no_grad():
     _UpperCamelCase:  Optional[int]    						=			model(_lowercase				).sample

    _UpperCamelCase:  List[Any]    						=			output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _UpperCamelCase:  Any    						=			torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]				)
    # fmt: on
    self.assertTrue(torch.allclose(_lowercase		,	_lowercase		,	atol=1E-3				)				)