import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
snake_case__				= logging.get_logger(__name__)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      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'''),
                ]		)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            _lowerCamelCase			    =				[(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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=False		)						->    str:



      '''simple docstring'''

      for i in range(config.num_hidden_layers		):
            if base_model:
                  _lowerCamelCase			    =				''''''
            else:
                  _lowerCamelCase			    =				'''vit.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowerCamelCase			    =				state_dict.pop(F'blocks.{i}.attn.qkv.weight'		)
            _lowerCamelCase			    =				state_dict.pop(F'blocks.{i}.attn.qkv.bias'		)
            # next, add query, keys and values (in that order) to the state dict
            _lowerCamelCase			    =				in_proj_weight[
                : config.hidden_size, :
            ]
            _lowerCamelCase			    =				in_proj_bias[: config.hidden_size]
            _lowerCamelCase			    =				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCamelCase			    =				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCamelCase			    =				in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCamelCase			    =				in_proj_bias[-config.hidden_size :]
def       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				dct.pop(__UpperCAmelCase		)
      _lowerCamelCase			    =				val
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _lowerCamelCase			    =				Image.open(requests.get(__UpperCAmelCase ,						stream=__UpperCAmelCase		).raw		)
      return im
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=True		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            _lowerCamelCase			    =				8
      # set labels if required
      if not base_model:
            _lowerCamelCase			    =				1000
            _lowerCamelCase			    =				'''huggingface/label-files'''
            _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
            _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
            _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            _lowerCamelCase			    =				384
            _lowerCamelCase			    =				1536
            _lowerCamelCase			    =				12
            _lowerCamelCase			    =				6

      # load original model from torch hub
      _lowerCamelCase			    =				torch.hub.load('''facebookresearch/dino:main''' ,						__UpperCAmelCase		)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      _lowerCamelCase			    =				original_model.state_dict()
      if base_model:
            remove_classification_head_(__UpperCAmelCase		)
      _lowerCamelCase			    =				create_rename_keys(__UpperCAmelCase ,						base_model=__UpperCAmelCase		)
      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      read_in_q_k_v(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      # load HuggingFace model
      if base_model:
            _lowerCamelCase			    =				ViTModel(__UpperCAmelCase ,						add_pooling_layer=__UpperCAmelCase		).eval()
      else:
            _lowerCamelCase			    =				ViTForImageClassification(__UpperCAmelCase		).eval()
      model.load_state_dict(__UpperCAmelCase		)

      # Check outputs on an image, prepared by ViTImageProcessor
      _lowerCamelCase			    =				ViTImageProcessor()
      _lowerCamelCase			    =				image_processor(images=prepare_img() ,						return_tensors='''pt'''		)
      _lowerCamelCase			    =				encoding['''pixel_values''']
      _lowerCamelCase			    =				model(__UpperCAmelCase		)

      if base_model:
            _lowerCamelCase			    =				original_model(__UpperCAmelCase		)
            assert torch.allclose(__UpperCAmelCase ,						outputs.last_hidden_state[:, 0, :] ,						atol=1E-1		)
      else:
            _lowerCamelCase			    =				original_model(__UpperCAmelCase		)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(__UpperCAmelCase ,						outputs.logits ,						atol=1E-3		)

      Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
      print(F'Saving model {model_name} to {pytorch_dump_folder_path}'		)
      model.save_pretrained(__UpperCAmelCase		)
      print(F'Saving image processor to {pytorch_dump_folder_path}'		)
      image_processor.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='dino_vitb16',
         type=str,
         help='Name of the model trained with DINO you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--base_model',
         action='store_true',
         help='Whether to only convert the base model (no projection head weights).',
     )

     parser.set_defaults(base_model=True)
     snake_case__				= parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)



import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
snake_case__				= logging.get_logger(__name__)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      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'''),
                ]		)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            _lowerCamelCase			    =				[(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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=False		)						->    str:



      '''simple docstring'''

      for i in range(config.num_hidden_layers		):
            if base_model:
                  _lowerCamelCase			    =				''''''
            else:
                  _lowerCamelCase			    =				'''vit.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowerCamelCase			    =				state_dict.pop(F'blocks.{i}.attn.qkv.weight'		)
            _lowerCamelCase			    =				state_dict.pop(F'blocks.{i}.attn.qkv.bias'		)
            # next, add query, keys and values (in that order) to the state dict
            _lowerCamelCase			    =				in_proj_weight[
                : config.hidden_size, :
            ]
            _lowerCamelCase			    =				in_proj_bias[: config.hidden_size]
            _lowerCamelCase			    =				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCamelCase			    =				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCamelCase			    =				in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCamelCase			    =				in_proj_bias[-config.hidden_size :]
def       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				dct.pop(__UpperCAmelCase		)
      _lowerCamelCase			    =				val
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _lowerCamelCase			    =				Image.open(requests.get(__UpperCAmelCase ,						stream=__UpperCAmelCase		).raw		)
      return im
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=True		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            _lowerCamelCase			    =				8
      # set labels if required
      if not base_model:
            _lowerCamelCase			    =				1000
            _lowerCamelCase			    =				'''huggingface/label-files'''
            _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
            _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
            _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            _lowerCamelCase			    =				384
            _lowerCamelCase			    =				1536
            _lowerCamelCase			    =				12
            _lowerCamelCase			    =				6

      # load original model from torch hub
      _lowerCamelCase			    =				torch.hub.load('''facebookresearch/dino:main''' ,						__UpperCAmelCase		)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      _lowerCamelCase			    =				original_model.state_dict()
      if base_model:
            remove_classification_head_(__UpperCAmelCase		)
      _lowerCamelCase			    =				create_rename_keys(__UpperCAmelCase ,						base_model=__UpperCAmelCase		)
      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      read_in_q_k_v(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      # load HuggingFace model
      if base_model:
            _lowerCamelCase			    =				ViTModel(__UpperCAmelCase ,						add_pooling_layer=__UpperCAmelCase		).eval()
      else:
            _lowerCamelCase			    =				ViTForImageClassification(__UpperCAmelCase		).eval()
      model.load_state_dict(__UpperCAmelCase		)

      # Check outputs on an image, prepared by ViTImageProcessor
      _lowerCamelCase			    =				ViTImageProcessor()
      _lowerCamelCase			    =				image_processor(images=prepare_img() ,						return_tensors='''pt'''		)
      _lowerCamelCase			    =				encoding['''pixel_values''']
      _lowerCamelCase			    =				model(__UpperCAmelCase		)

      if base_model:
            _lowerCamelCase			    =				original_model(__UpperCAmelCase		)
            assert torch.allclose(__UpperCAmelCase ,						outputs.last_hidden_state[:, 0, :] ,						atol=1E-1		)
      else:
            _lowerCamelCase			    =				original_model(__UpperCAmelCase		)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(__UpperCAmelCase ,						outputs.logits ,						atol=1E-3		)

      Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
      print(F'Saving model {model_name} to {pytorch_dump_folder_path}'		)
      model.save_pretrained(__UpperCAmelCase		)
      print(F'Saving image processor to {pytorch_dump_folder_path}'		)
      image_processor.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='dino_vitb16',
         type=str,
         help='Name of the model trained with DINO you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--base_model',
         action='store_true',
         help='Whether to only convert the base model (no projection head weights).',
     )

     parser.set_defaults(base_model=True)
     snake_case__				= parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)