






'''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()
__A    	=   logging.get_logger(__name__)




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> str:
   __snake_case				:      List[Any]     							= DPTConfig(embedding_type='hybrid'  )

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

   if "nyu" or "midas" in checkpoint_url:
      __snake_case				:      int     							= 7_68
      __snake_case				:      Tuple     							= [1, 1, 1, 0.5]
      __snake_case				:      Union[str, Any]     							= [2_56, 5_12, 7_68, 7_68]
      __snake_case				:      Optional[int]     							= 1_50
      __snake_case				:      Any     							= 16
      __snake_case				:      Optional[Any]     							= (1, 3_84, 3_84)
      __snake_case				:      int     							= False
      __snake_case				:      List[str]     							= 'project'

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

   return config, expected_shape




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> Dict:
   __snake_case				:      Union[str, Any]     							= ['pretrained.model.head.weight', 'pretrained.model.head.bias']
   for k in ignore_keys:
      state_dict.pop(A  ,		A  )




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

   if ".." in name:
      __snake_case				:      List[str]     							= name.replace('..'  ,		'.'  )

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




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       Tuple  )    -> 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				:      Any     							= state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""  )
      __snake_case				:      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
      __snake_case				:      Any     							= in_proj_weight[: config.hidden_size, :]
      __snake_case				:      Dict     							= in_proj_bias[: config.hidden_size]
      __snake_case				:      Union[str, Any]     							= in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __snake_case				:      List[str]     							= in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __snake_case				:      Tuple     							= in_proj_weight[
          -config.hidden_size :, :
      ]
      __snake_case				:      str     							= in_proj_bias[-config.hidden_size :]




def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:
   __snake_case				:      Tuple     							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
   __snake_case				:      Tuple     							= Image.open(requests.get(A  ,		stream=A  ).raw  )
   return im




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

   # load HuggingFace model
   __snake_case				:      Optional[Any]     							= DPTForSemanticSegmentation(A  ) if 'ade' in checkpoint_url else DPTForDepthEstimation(A  )
   model.load_state_dict(A  )
   model.eval()

   # Check outputs on an image
   __snake_case				:      str     							= 4_80 if 'ade' in checkpoint_url else 3_84
   __snake_case				:      Dict     							= DPTImageProcessor(size=A  )

   __snake_case				:      int     							= prepare_img()
   __snake_case				:      Optional[int]     							= image_processor(A  ,		return_tensors='pt'  )

   # forward pass
   __snake_case				:      List[Any]     							= model(**A  ).logits if 'ade' in checkpoint_url else model(**A  ).predicted_depth

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

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

   if pytorch_dump_folder_path is not None:
      Path(A  ).mkdir(exist_ok=A  )
      print(F"""Saving model to {pytorch_dump_folder_path}"""  )
      model.save_pretrained(A  )
      print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
      image_processor.save_pretrained(A  )

   if push_to_hub:
      model.push_to_hub('ybelkada/dpt-hybrid-midas'  )
      image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'  )


if __name__ == "__main__":
      __A    	=   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''',
      )

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







'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A    	=   logging.get_logger()




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       str  ,		A  :       LevitConfig  ,		A  :       Path  ,		A  :       bool = True  )    -> Dict:







   """simple docstring"""
   print(F"""Converting {name}..."""  )

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            __snake_case				:      Optional[int]     							= timm.create_model('levit_128s'  ,		pretrained=A  )
         else:
            __snake_case				:      Tuple     							= timm.create_model('levit_128'  ,		pretrained=A  )
      if hidden_sizes == 1_92:
         __snake_case				:      int     							= timm.create_model('levit_192'  ,		pretrained=A  )
      if hidden_sizes == 2_56:
         __snake_case				:      List[Any]     							= timm.create_model('levit_256'  ,		pretrained=A  )
      if hidden_sizes == 3_84:
         __snake_case				:      int     							= timm.create_model('levit_384'  ,		pretrained=A  )

      from_model.eval()
      __snake_case				:      str     							= LevitForImageClassificationWithTeacher(A  ).eval()
      __snake_case				:      int     							= OrderedDict()

      __snake_case				:      Optional[Any]     							= from_model.state_dict()
      __snake_case				:      Tuple     							= list(from_model.state_dict().keys()  )
      __snake_case				:      List[str]     							= list(our_model.state_dict().keys()  )
      print(len(A  )  ,		len(A  )  )
      for i in range(len(A  )  ):
         __snake_case				:      Optional[int]     							= weights[og_keys[i]]
      our_model.load_state_dict(A  )

      __snake_case				:      Tuple     							= torch.randn((2, 3, 2_24, 2_24)  )
      __snake_case				:      Union[str, Any]     							= from_model(A  )
      __snake_case				:      List[str]     							= our_model(A  ).logits

   assert torch.allclose(A  ,		A  ), "The model logits don't match the original one."

   __snake_case				:      int     							= name
   print(A  )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name  )
      __snake_case				:      int     							= LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name  )

      print(F"""Pushed {checkpoint_name}"""  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Path  ,		A  :       str = None  ,		A  :       bool = True  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= 'imagenet-1k-id2label.json'
   __snake_case				:      Tuple     							= 10_00
   __snake_case				:      Dict     							= (1, num_labels)

   __snake_case				:      List[str]     							= 'huggingface/label-files'
   __snake_case				:      Any     							= num_labels
   __snake_case				:      str     							= json.load(open(hf_hub_download(A  ,		A  ,		repo_type='dataset'  )  ,		'r'  )  )
   __snake_case				:      Any     							= {int(A  ): v for k, v in idalabel.items()}

   __snake_case				:      int     							= idalabel
   __snake_case				:      Union[str, Any]     							= {v: k for k, v in idalabel.items()}

   __snake_case				:      Optional[int]     							= partial(A  ,		num_labels=A  ,		idalabel=A  ,		labelaid=A  )

   __snake_case				:      Dict     							= {
       'levit-128S': 1_28,
       'levit-128': 1_28,
       'levit-192': 1_92,
       'levit-256': 2_56,
       'levit-384': 3_84,
   }

   __snake_case				:      Union[str, Any]     							= {
       'levit-128S': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[2, 3, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-128': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 8, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-192': ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]  ,		num_attention_heads=[3, 5, 6]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-256': ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-384': ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]  ,		num_attention_heads=[6, 9, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0.1  ,		),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]  ,		A  ,		names_to_config[model_name]  ,		A  ,		A  )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]  ,		A  ,		A  ,		A  ,		A  )
   return config, expected_shape


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default=None,
          type=str,
          help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default='''levit-dump-folder/''',
          type=Path,
          required=False,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
      parser.add_argument(
          '''--no-push_to_hub''',
          dest='''push_to_hub''',
          action='''store_false''',
          help='''Do not push model and image processor to the hub''',
      )

      __A    	=   parser.parse_args()
      __A    	=   args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)