





import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			Tuple=False      )      ->	Dict:
    UpperCamelCase__							:				str          =      []
    for i in range(config.num_hidden_layers      ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")      )
        rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")      )
        rename_keys.append(
            (f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")      )
        rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")      )
        rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")      )
        rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")      )
        rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")      )
        rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")      )
        rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")      )
        rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")      )

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

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

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        UpperCamelCase__							:				Dict          =      [(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 		lowerCAmelCase_    (  __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			List[str]=False      )      ->	str:
    for i in range(config.num_hidden_layers      ):
        if base_model:
            UpperCamelCase__							:				Dict          =      ''''''
        else:
            UpperCamelCase__							:				Tuple          =      '''vit.'''
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        UpperCamelCase__							:				int          =      state_dict.pop(f"module.blocks.{i}.attn.qkv.weight"      )
        UpperCamelCase__							:				Dict          =      state_dict.pop(f"module.blocks.{i}.attn.qkv.bias"      )
        # next, add query, keys and values (in that order) to the state dict
        UpperCamelCase__							:				List[Any]          =      in_proj_weight[
            : config.hidden_size, :
        ]
        UpperCamelCase__							:				Optional[Any]          =      in_proj_bias[: config.hidden_size]
        UpperCamelCase__							:				str          =      in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCamelCase__							:				str          =      in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCamelCase__							:				Optional[int]          =      in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCamelCase__							:				int          =      in_proj_bias[-config.hidden_size :]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Dict      )      ->	Optional[int]:
    UpperCamelCase__							:				List[Any]          =      ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(__UpperCAmelCase		,   __UpperCAmelCase      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Dict      )      ->	Tuple:
    # projection head is used in the self-supervised pre-training in MSN,
    # for downstream task it's not needed.
    UpperCamelCase__							:				Tuple          =      [
        '''module.fc.fc1.weight''',
        '''module.fc.fc1.bias''',
        '''module.fc.bn1.weight''',
        '''module.fc.bn1.bias''',
        '''module.fc.bn1.running_mean''',
        '''module.fc.bn1.running_var''',
        '''module.fc.bn1.num_batches_tracked''',
        '''module.fc.fc2.weight''',
        '''module.fc.fc2.bias''',
        '''module.fc.bn2.weight''',
        '''module.fc.bn2.bias''',
        '''module.fc.bn2.running_mean''',
        '''module.fc.bn2.running_var''',
        '''module.fc.bn2.num_batches_tracked''',
        '''module.fc.fc3.weight''',
        '''module.fc.fc3.bias''',
    ]
    for k in ignore_keys:
        state_dict.pop(__UpperCAmelCase		,   __UpperCAmelCase      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			str      )      ->	str:
    UpperCamelCase__							:				str          =      dct.pop(__UpperCAmelCase      )
    UpperCamelCase__							:				Optional[Any]          =      val




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Optional[int]      )      ->	List[str]:
    UpperCamelCase__							:				Tuple          =      ViTMSNConfig()
    UpperCamelCase__							:				List[str]          =      1000

    UpperCamelCase__							:				Dict          =      '''datasets/huggingface/label-files'''
    UpperCamelCase__							:				Tuple          =      '''imagenet-1k-id2label.json'''
    UpperCamelCase__							:				Optional[Any]          =      json.load(open(hf_hub_download(__UpperCAmelCase		,   __UpperCAmelCase      )		,   '''r'''      )      )
    UpperCamelCase__							:				List[str]          =      {int(__UpperCAmelCase      ): v for k, v in idalabel.items()}
    UpperCamelCase__							:				Union[str, Any]          =      idalabel
    UpperCamelCase__							:				Optional[Any]          =      {v: k for k, v in idalabel.items()}

    if "s16" in checkpoint_url:
        UpperCamelCase__							:				int          =      384
        UpperCamelCase__							:				List[str]          =      1536
        UpperCamelCase__							:				Optional[Any]          =      6
    elif "l16" in checkpoint_url:
        UpperCamelCase__							:				List[Any]          =      1024
        UpperCamelCase__							:				Any          =      4096
        UpperCamelCase__							:				Optional[Any]          =      24
        UpperCamelCase__							:				Tuple          =      16
        UpperCamelCase__							:				List[Any]          =      0.1
    elif "b4" in checkpoint_url:
        UpperCamelCase__							:				Any          =      4
    elif "l7" in checkpoint_url:
        UpperCamelCase__							:				str          =      7
        UpperCamelCase__							:				List[str]          =      1024
        UpperCamelCase__							:				List[Any]          =      4096
        UpperCamelCase__							:				List[Any]          =      24
        UpperCamelCase__							:				List[str]          =      16
        UpperCamelCase__							:				Optional[Any]          =      0.1

    UpperCamelCase__							:				int          =      ViTMSNModel(__UpperCAmelCase      )

    UpperCamelCase__							:				Dict          =      torch.hub.load_state_dict_from_url(__UpperCAmelCase		,   map_location='''cpu'''      )['''target_encoder''']

    UpperCamelCase__							:				Optional[int]          =      ViTImageProcessor(size=config.image_size      )

    remove_projection_head(__UpperCAmelCase      )
    UpperCamelCase__							:				List[str]          =      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		,   base_model=__UpperCAmelCase      )

    model.load_state_dict(__UpperCAmelCase      )
    model.eval()

    UpperCamelCase__							:				Optional[int]          =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

    UpperCamelCase__							:				str          =      Image.open(requests.get(__UpperCAmelCase		,   stream=__UpperCAmelCase      ).raw      )
    UpperCamelCase__							:				int          =      ViTImageProcessor(
        size=config.image_size		,   image_mean=__UpperCAmelCase		,   image_std=__UpperCAmelCase      )
    UpperCamelCase__							:				Tuple          =      image_processor(images=__UpperCAmelCase		,   return_tensors='''pt'''      )

    # forward pass
    torch.manual_seed(2      )
    UpperCamelCase__							:				List[str]          =      model(**__UpperCAmelCase      )
    UpperCamelCase__							:				int          =      outputs.last_hidden_state

    # The following Colab Notebook was used to generate these outputs:
    # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
    if "s16" in checkpoint_url:
        UpperCamelCase__							:				Dict          =      torch.tensor([[-1.0915, -1.4876, -1.1809]]      )
    elif "b16" in checkpoint_url:
        UpperCamelCase__							:				str          =      torch.tensor([[14.2889, -18.9045, 11.7281]]      )
    elif "l16" in checkpoint_url:
        UpperCamelCase__							:				Dict          =      torch.tensor([[41.5028, -22.8681, 45.6475]]      )
    elif "b4" in checkpoint_url:
        UpperCamelCase__							:				List[Any]          =      torch.tensor([[-4.3868, 5.2932, -0.4137]]      )
    else:
        UpperCamelCase__							:				Union[str, Any]          =      torch.tensor([[-0.1792, -0.6465, 2.4263]]      )

    # verify logits
    assert torch.allclose(last_hidden_state[:, 0, :3]		,   __UpperCAmelCase		,   atol=1e-4      )

    print(f"Saving model 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__":
  UpperCAmelCase_     							= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
      type=str,
      help='URL of the checkpoint you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )

  UpperCAmelCase_     							= parser.parse_args()
  convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








class     lowercase__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__ = "",		__magic_name__ = False							) ->      None:






       """simple docstring"""
       # Mapping from the first character of the prefix of the node
       UpperCamelCase__							:				dict[str, RadixNode]          =      {}

       # A node will be a leaf if the tree contains its word
       UpperCamelCase__							:				Optional[Any]          =      is_leaf

       UpperCamelCase__							:				List[str]          =      prefix




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      tuple[str, str, str]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      0
       for q, w in zip(self.prefix,		__magic_name__							):
           if q != w:
               break

           x += 1

       return self.prefix[:x], self.prefix[x:], word[x:]




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       for word in words:
           self.insert(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       # Case 1: If the word is the prefix of the node
       # Solution: We set the current node as leaf
       if self.prefix == word:
           UpperCamelCase__							:				Union[str, Any]          =      True

       # Case 2: The node has no edges that have a prefix to the word
       # Solution: We create an edge from the current node to a new one
       # containing the word
       elif word[0] not in self.nodes:
           UpperCamelCase__							:				Tuple          =      RadixNode(prefix=__magic_name__,		is_leaf=__magic_name__							)

       else:
           UpperCamelCase__							:				Any          =      self.nodes[word[0]]
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      incoming_node.match(
               __magic_name__							)

           # Case 3: The node prefix is equal to the matching
           # Solution: We insert remaining word on the next node
           if remaining_prefix == "":
               self.nodes[matching_string[0]].insert(__magic_name__							)

           # Case 4: The word is greater equal to the matching
           # Solution: Create a node in between both nodes, change
           # prefixes and add the new node for the remaining word
           else:
               UpperCamelCase__							:				Tuple          =      remaining_prefix

               UpperCamelCase__							:				Tuple          =      self.nodes[matching_string[0]]
               UpperCamelCase__							:				List[Any]          =      RadixNode(__magic_name__,		__magic_name__							)
               UpperCamelCase__							:				str          =      aux_node

               if remaining_word == "":
                   UpperCamelCase__							:				Any          =      True
               else:
                   self.nodes[matching_string[0]].insert(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      bool:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.nodes.get(word[0],		__magic_name__							)
       if not incoming_node:
           return False
       else:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Dict          =      incoming_node.match(
               __magic_name__							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
               return False
           # This applies when the word and the prefix are equal
           elif remaining_word == "":
               return incoming_node.is_leaf
           # We have word remaining so we check the next node
           else:
               return incoming_node.find(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      bool:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.nodes.get(word[0],		__magic_name__							)
       if not incoming_node:
           return False
       else:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      incoming_node.match(
               __magic_name__							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
               return False
           # We have word remaining so we check the next node
           elif remaining_word != "":
               return incoming_node.delete(__magic_name__							)
           else:
               # If it is not a leaf, we don't have to delete
               if not incoming_node.is_leaf:
                   return False
               else:
                   # We delete the nodes if no edges go from it
                   if len(incoming_node.nodes							) == 0:
                       del self.nodes[word[0]]
                       # We merge the current node with its only child
                       if len(self.nodes							) == 1 and not self.is_leaf:
                           UpperCamelCase__							:				Optional[Any]          =      list(self.nodes.values()							)[0]
                           UpperCamelCase__							:				Union[str, Any]          =      merging_node.is_leaf
                           self.prefix += merging_node.prefix
                           UpperCamelCase__							:				int          =      merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                   elif len(incoming_node.nodes							) > 1:
                       UpperCamelCase__							:				Any          =      False
                   # If there is 1 edge, we merge it with its child
                   else:
                       UpperCamelCase__							:				Union[str, Any]          =      list(incoming_node.nodes.values()							)[0]
                       UpperCamelCase__							:				List[str]          =      merging_node.is_leaf
                       incoming_node.prefix += merging_node.prefix
                       UpperCamelCase__							:				int          =      merging_node.nodes

                   return True




   def       UpperCamelCase__  (    self,		__magic_name__ = 0							) ->      None:






       """simple docstring"""
       if self.prefix != "":
           print('''-''' * height,		self.prefix,		'''  (leaf)''' if self.is_leaf else ''''''							)

       for value in self.nodes.values():
           value.print_tree(height + 1							)




def 		lowerCAmelCase_    (  )      ->	bool:
    UpperCamelCase__							:				Optional[int]          =      '''banana bananas bandana band apple all beast'''.split()
    UpperCamelCase__							:				Optional[int]          =      RadixNode()
    root.insert_many(__UpperCAmelCase      )

    assert all(root.find(__UpperCAmelCase      ) for word in words      )
    assert not root.find('''bandanas'''      )
    assert not root.find('''apps'''      )
    root.delete('''all'''      )
    assert not root.find('''all'''      )
    root.delete('''banana'''      )
    assert not root.find('''banana'''      )
    assert root.find('''bananas'''      )

    return True




def 		lowerCAmelCase_    (  )      ->	None:
    assert test_trie()




def 		lowerCAmelCase_    (  )      ->	None:
    UpperCamelCase__							:				int          =      RadixNode()
    UpperCamelCase__							:				Any          =      '''banana bananas bandanas bandana band apple all beast'''.split()
    root.insert_many(__UpperCAmelCase      )

    print('''Words:'''		,   __UpperCAmelCase      )
    print('''Tree:'''      )
    root.print_tree()


if __name__ == "__main__":
  main()

