


"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__						:   int			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	Union[str, Any]	):
      lowerCAmelCase    :		Dict				=      OrderedDict()
      for key, value in state_dict.items():
            if key.startswith('''module.encoder'''	):
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''module.encoder'''       ,      '''glpn.encoder'''	)
            if key.startswith('''module.decoder'''	):
                  lowerCAmelCase    :		str				=      key.replace('''module.decoder'''       ,      '''decoder.stages'''	)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  lowerCAmelCase    :		Union[str, Any]				=      key[key.find('''patch_embed'''	) + len('''patch_embed'''	)]
                  lowerCAmelCase    :		str				=      key.replace(f'''patch_embed{idx}'''       ,      f'''patch_embeddings.{int(_snake_case	)-1}'''	)
            if "norm" in key:
                  lowerCAmelCase    :		str				=      key.replace('''norm'''       ,      '''layer_norm'''	)
            if "glpn.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  lowerCAmelCase    :		Optional[int]				=      key[key.find('''glpn.encoder.layer_norm'''	) + len('''glpn.encoder.layer_norm'''	)]
                  lowerCAmelCase    :		List[str]				=      key.replace(f'''layer_norm{idx}'''       ,      f'''layer_norm.{int(_snake_case	)-1}'''	)
            if "layer_norm1" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''layer_norm1'''       ,      '''layer_norm_1'''	)
            if "layer_norm2" in key:
                  lowerCAmelCase    :		Any				=      key.replace('''layer_norm2'''       ,      '''layer_norm_2'''	)
            if "block" in key:
                  # replace for example block1 by block.0
                  lowerCAmelCase    :		Tuple				=      key[key.find('''block'''	) + len('''block'''	)]
                  lowerCAmelCase    :		Tuple				=      key.replace(f'''block{idx}'''       ,      f'''block.{int(_snake_case	)-1}'''	)
            if "attn.q" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''attn.q'''       ,      '''attention.self.query'''	)
            if "attn.proj" in key:
                  lowerCAmelCase    :		Dict				=      key.replace('''attn.proj'''       ,      '''attention.output.dense'''	)
            if "attn" in key:
                  lowerCAmelCase    :		List[str]				=      key.replace('''attn'''       ,      '''attention.self'''	)
            if "fc1" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''fc1'''       ,      '''dense1'''	)
            if "fc2" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''fc2'''       ,      '''dense2'''	)
            if "linear_pred" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''linear_pred'''       ,      '''classifier'''	)
            if "linear_fuse" in key:
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''linear_fuse.conv'''       ,      '''linear_fuse'''	)
                  lowerCAmelCase    :		int				=      key.replace('''linear_fuse.bn'''       ,      '''batch_norm'''	)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  lowerCAmelCase    :		Optional[Any]				=      key[key.find('''linear_c'''	) + len('''linear_c'''	)]
                  lowerCAmelCase    :		int				=      key.replace(f'''linear_c{idx}'''       ,      f'''linear_c.{int(_snake_case	)-1}'''	)
            if "bot_conv" in key:
                  lowerCAmelCase    :		str				=      key.replace('''bot_conv'''       ,      '''0.convolution'''	)
            if "skip_conv1" in key:
                  lowerCAmelCase    :		int				=      key.replace('''skip_conv1'''       ,      '''1.convolution'''	)
            if "skip_conv2" in key:
                  lowerCAmelCase    :		str				=      key.replace('''skip_conv2'''       ,      '''2.convolution'''	)
            if "fusion1" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''fusion1'''       ,      '''1.fusion'''	)
            if "fusion2" in key:
                  lowerCAmelCase    :		Any				=      key.replace('''fusion2'''       ,      '''2.fusion'''	)
            if "fusion3" in key:
                  lowerCAmelCase    :		List[Any]				=      key.replace('''fusion3'''       ,      '''3.fusion'''	)
            if "fusion" in key and "conv" in key:
                  lowerCAmelCase    :		Union[str, Any]				=      key.replace('''conv'''       ,      '''convolutional_layer'''	)
            if key.startswith('''module.last_layer_depth'''	):
                  lowerCAmelCase    :		Optional[Any]				=      key.replace('''module.last_layer_depth'''       ,      '''head.head'''	)
            lowerCAmelCase    :		Union[str, Any]				=      value

      return new_state_dict


def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	str	):
      # for each of the encoder blocks:
      for i in range(config.num_encoder_blocks	):
            for j in range(config.depths[i]	):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCAmelCase    :		int				=      state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''	)
                  lowerCAmelCase    :		Optional[int]				=      state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''	)
                  # next, add keys and values (in that order) to the state dict
                  lowerCAmelCase    :		str				=      kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase    :		Union[str, Any]				=      kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase    :		Dict				=      kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase    :		List[str]				=      kv_bias[config.hidden_sizes[i] :]


def    _snake_case  (  ):
      lowerCAmelCase    :		int				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		str				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)

      return image


@torch.no_grad()
def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	Dict       ,      _snake_case :	Union[str, Any]=False       ,      _snake_case :	List[str]=None	):
      lowerCAmelCase    :		Optional[int]				=      GLPNConfig(hidden_sizes=[64, 128, 320, 512]       ,      decoder_hidden_size=64       ,      depths=[3, 8, 27, 3]	)

      # load image processor (only resize + rescale)
      lowerCAmelCase    :		Union[str, Any]				=      GLPNImageProcessor()

      # prepare image
      lowerCAmelCase    :		Tuple				=      prepare_img()
      lowerCAmelCase    :		Dict				=      image_processor(images=_snake_case       ,      return_tensors='''pt'''	).pixel_values

      logger.info('''Converting model...'''	)

      # load original state dict
      lowerCAmelCase    :		List[str]				=      torch.load(_snake_case       ,      map_location=torch.device('''cpu'''	)	)

      # rename keys
      lowerCAmelCase    :		Tuple				=      rename_keys(_snake_case	)

      # key and value matrices need special treatment
      read_in_k_v(_snake_case       ,      _snake_case	)

      # create HuggingFace model and load state dict
      lowerCAmelCase    :		str				=      GLPNForDepthEstimation(_snake_case	)
      model.load_state_dict(_snake_case	)
      model.eval()

      # forward pass
      lowerCAmelCase    :		Union[str, Any]				=      model(_snake_case	)
      lowerCAmelCase    :		int				=      outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase    :		str				=      torch.tensor(
                      [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]	)
            elif "kitti" in model_name:
                  lowerCAmelCase    :		str				=      torch.tensor(
                      [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]	)
            else:
                  raise ValueError(f'''Unknown model name: {model_name}'''	)

            lowerCAmelCase    :		List[Any]				=      torch.Size([1, 480, 640]	)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]       ,      _snake_case       ,      atol=1E-4	)
            print('''Looks ok!'''	)

      # finally, push to hub if required
      if push_to_hub:
            logger.info('''Pushing model and image processor to the hub...'''	)
            model.push_to_hub(
                repo_path_or_name=Path(_snake_case       ,      _snake_case	)       ,      organization='''nielsr'''       ,      commit_message='''Add model'''       ,      use_temp_dir=_snake_case       ,      )
            image_processor.push_to_hub(
                repo_path_or_name=Path(_snake_case       ,      _snake_case	)       ,      organization='''nielsr'''       ,      commit_message='''Add image processor'''       ,      use_temp_dir=_snake_case       ,      )


if __name__ == "__main__":
 snake_case__						:   Tuple			 = argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''',
     default=None,
     type=str,
     help='''Path to the original PyTorch checkpoint (.pth file).''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
 )
 parser.add_argument(
     '''--model_name''',
     default='''glpn-kitti''',
     type=str,
     help='''Name of the model in case you\'re pushing to the hub.''',
 )
 snake_case__						:   List[str]			 = parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__						:   Any			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	Tuple=False	):
      lowerCAmelCase    :		List[str]				=      []
      for i in range(config.num_hidden_layers	):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
            rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
            rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
            rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
            rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')	)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('''cls_token''', '''vit.embeddings.cls_token'''),
              ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
              ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
              ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
          ]	)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('''norm.weight''', '''layernorm.weight'''),
                    ('''norm.bias''', '''layernorm.bias'''),
                    ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                    ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
                ]	)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            lowerCAmelCase    :		Union[str, Any]				=      [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('''norm.weight''', '''vit.layernorm.weight'''),
                    ('''norm.bias''', '''vit.layernorm.bias'''),
                    ('''head.weight''', '''classifier.weight'''),
                    ('''head.bias''', '''classifier.bias'''),
                ]	)

      return rename_keys


def    _snake_case  (  _snake_case :	Tuple       ,      _snake_case :	List[Any]       ,      _snake_case :	Tuple=False	):
      for i in range(config.num_hidden_layers	):
            if base_model:
                  lowerCAmelCase    :		Optional[int]				=      ''''''
            else:
                  lowerCAmelCase    :		Union[str, Any]				=      '''vit.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            lowerCAmelCase    :		List[Any]				=      state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''	)
            lowerCAmelCase    :		Tuple				=      state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''	)
            # next, add query, keys and values (in that order) to the state dict
            lowerCAmelCase    :		Optional[Any]				=      in_proj_weight[
                : config.hidden_size, :
            ]
            lowerCAmelCase    :		Tuple				=      in_proj_bias[: config.hidden_size]
            lowerCAmelCase    :		Tuple				=      in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowerCAmelCase    :		Tuple				=      in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowerCAmelCase    :		Union[str, Any]				=      in_proj_weight[
                -config.hidden_size :, :
            ]
            lowerCAmelCase    :		List[Any]				=      in_proj_bias[-config.hidden_size :]


def    _snake_case  (  _snake_case :	Tuple	):
      lowerCAmelCase    :		List[Any]				=      ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(_snake_case       ,      _snake_case	)


def    _snake_case  (  _snake_case :	Union[str, Any]       ,      _snake_case :	Any       ,      _snake_case :	List[Any]	):
      lowerCAmelCase    :		Optional[int]				=      dct.pop(_snake_case	)
      lowerCAmelCase    :		Union[str, Any]				=      val


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


@torch.no_grad()
def    _snake_case  (  _snake_case :	Optional[int]       ,      _snake_case :	Optional[Any]	):
      lowerCAmelCase    :		Any				=      ViTConfig()
      lowerCAmelCase    :		Any				=      False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            lowerCAmelCase    :		List[str]				=      True
            lowerCAmelCase    :		int				=      int(vit_name[-12:-10]	)
            lowerCAmelCase    :		List[Any]				=      int(vit_name[-9:-6]	)
      else:
            lowerCAmelCase    :		str				=      1000
            lowerCAmelCase    :		Optional[int]				=      '''huggingface/label-files'''
            lowerCAmelCase    :		Any				=      '''imagenet-1k-id2label.json'''
            lowerCAmelCase    :		Optional[Any]				=      json.load(open(hf_hub_download(_snake_case       ,      _snake_case       ,      repo_type='''dataset'''	)       ,      '''r'''	)	)
            lowerCAmelCase    :		Optional[Any]				=      {int(_snake_case	): v for k, v in idalabel.items()}
            lowerCAmelCase    :		Dict				=      idalabel
            lowerCAmelCase    :		List[Any]				=      {v: k for k, v in idalabel.items()}
            lowerCAmelCase    :		List[str]				=      int(vit_name[-6:-4]	)
            lowerCAmelCase    :		int				=      int(vit_name[-3:]	)
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith('''tiny'''	):
                  lowerCAmelCase    :		str				=      192
                  lowerCAmelCase    :		int				=      768
                  lowerCAmelCase    :		List[str]				=      12
                  lowerCAmelCase    :		str				=      3
            elif vit_name[9:].startswith('''small'''	):
                  lowerCAmelCase    :		List[str]				=      384
                  lowerCAmelCase    :		Optional[int]				=      1536
                  lowerCAmelCase    :		int				=      12
                  lowerCAmelCase    :		str				=      6
            else:
                  pass
      else:
            if vit_name[4:].startswith('''small'''	):
                  lowerCAmelCase    :		List[str]				=      768
                  lowerCAmelCase    :		Dict				=      2304
                  lowerCAmelCase    :		Dict				=      8
                  lowerCAmelCase    :		Tuple				=      8
            elif vit_name[4:].startswith('''base'''	):
                  pass
            elif vit_name[4:].startswith('''large'''	):
                  lowerCAmelCase    :		Union[str, Any]				=      1024
                  lowerCAmelCase    :		List[Any]				=      4096
                  lowerCAmelCase    :		Union[str, Any]				=      24
                  lowerCAmelCase    :		Any				=      16
            elif vit_name[4:].startswith('''huge'''	):
                  lowerCAmelCase    :		Any				=      1280
                  lowerCAmelCase    :		str				=      5120
                  lowerCAmelCase    :		Tuple				=      32
                  lowerCAmelCase    :		Tuple				=      16

    # load original model from timm
      lowerCAmelCase    :		Any				=      timm.create_model(_snake_case       ,      pretrained=_snake_case	)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowerCAmelCase    :		int				=      timm_model.state_dict()
      if base_model:
            remove_classification_head_(_snake_case	)
      lowerCAmelCase    :		Optional[Any]				=      create_rename_keys(_snake_case       ,      _snake_case	)
      for src, dest in rename_keys:
            rename_key(_snake_case       ,      _snake_case       ,      _snake_case	)
      read_in_q_k_v(_snake_case       ,      _snake_case       ,      _snake_case	)

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            lowerCAmelCase    :		Any				=      ViTModel(_snake_case	).eval()
      else:
            lowerCAmelCase    :		Any				=      ViTForImageClassification(_snake_case	).eval()
      model.load_state_dict(_snake_case	)

      # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
      if "deit" in vit_name:
            lowerCAmelCase    :		Dict				=      DeiTImageProcessor(size=config.image_size	)
      else:
            lowerCAmelCase    :		Union[str, Any]				=      ViTImageProcessor(size=config.image_size	)
      lowerCAmelCase    :		Union[str, Any]				=      image_processor(images=prepare_img()       ,      return_tensors='''pt'''	)
      lowerCAmelCase    :		Dict				=      encoding['''pixel_values''']
      lowerCAmelCase    :		List[Any]				=      model(_snake_case	)

      if base_model:
            lowerCAmelCase    :		Dict				=      timm_model.forward_features(_snake_case	)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(_snake_case       ,      outputs.pooler_output       ,      atol=1E-3	)
      else:
            lowerCAmelCase    :		Dict				=      timm_model(_snake_case	)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(_snake_case       ,      outputs.logits       ,      atol=1E-3	)

      Path(_snake_case	).mkdir(exist_ok=_snake_case	)
      print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''	)
      model.save_pretrained(_snake_case	)
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
      image_processor.save_pretrained(_snake_case	)


if __name__ == "__main__":
 snake_case__						:   Union[str, Any]			 = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--vit_name''',
     default='''vit_base_patch16_224''',
     type=str,
     help='''Name of the ViT timm model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 snake_case__						:   int			 = parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)


