

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







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(lowercase__   ,	lowercase__						)







def 		_lowercase (     lowercase__						):
      # projection head is used in the self-supervised pre-training in MSN,
      # for downstream task it's not needed.
      __lowerCAmelCase					:						int					=					[
          '''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(lowercase__   ,	lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					dct.pop(lowercase__						)
      __lowerCAmelCase					:						int					=					val







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Optional[int]					=					ViTMSNConfig()
      __lowerCAmelCase					:						Optional[Any]					=					1_0_0_0

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

      if "s16" in checkpoint_url:
            __lowerCAmelCase					:						Union[str, Any]					=					3_8_4
            __lowerCAmelCase					:						Tuple					=					1_5_3_6
            __lowerCAmelCase					:						Union[str, Any]					=					6
      elif "l16" in checkpoint_url:
            __lowerCAmelCase					:						Optional[int]					=					1_0_2_4
            __lowerCAmelCase					:						Any					=					4_0_9_6
            __lowerCAmelCase					:						Optional[Any]					=					2_4
            __lowerCAmelCase					:						List[Any]					=					1_6
            __lowerCAmelCase					:						Optional[Any]					=					0.1
      elif "b4" in checkpoint_url:
            __lowerCAmelCase					:						List[str]					=					4
      elif "l7" in checkpoint_url:
            __lowerCAmelCase					:						Any					=					7
            __lowerCAmelCase					:						str					=					1_0_2_4
            __lowerCAmelCase					:						Union[str, Any]					=					4_0_9_6
            __lowerCAmelCase					:						int					=					2_4
            __lowerCAmelCase					:						Optional[int]					=					1_6
            __lowerCAmelCase					:						Dict					=					0.1

      __lowerCAmelCase					:						Any					=					ViTMSNModel(lowercase__						)

      __lowerCAmelCase					:						Tuple					=					torch.hub.load_state_dict_from_url(lowercase__   ,	map_location='''cpu'''						)['''target_encoder''']

      __lowerCAmelCase					:						Optional[Any]					=					ViTImageProcessor(size=config.image_size						)

      remove_projection_head(lowercase__						)
      __lowerCAmelCase					:						Any					=					create_rename_keys(lowercase__   ,	base_model=lowercase__						)

      for src, dest in rename_keys:
            rename_key(lowercase__   ,	lowercase__   ,	lowercase__						)
      read_in_q_k_v(lowercase__   ,	lowercase__   ,	base_model=lowercase__						)

      model.load_state_dict(lowercase__						)
      model.eval()

      __lowerCAmelCase					:						Dict					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''

      __lowerCAmelCase					:						Dict					=					Image.open(requests.get(lowercase__   ,	stream=lowercase__						).raw						)
      __lowerCAmelCase					:						List[str]					=					ViTImageProcessor(
          size=config.image_size   ,	image_mean=lowercase__   ,	image_std=lowercase__						)
      __lowerCAmelCase					:						Dict					=					image_processor(images=lowercase__   ,	return_tensors='''pt'''						)

      # forward pass
      torch.manual_seed(2						)
      __lowerCAmelCase					:						str					=					model(**lowercase__						)
      __lowerCAmelCase					:						Optional[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:
            __lowerCAmelCase					:						List[str]					=					torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]						)
      elif "b16" in checkpoint_url:
            __lowerCAmelCase					:						Any					=					torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]						)
      elif "l16" in checkpoint_url:
            __lowerCAmelCase					:						Tuple					=					torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]						)
      elif "b4" in checkpoint_url:
            __lowerCAmelCase					:						Optional[Any]					=					torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]						)
      else:
            __lowerCAmelCase					:						Tuple					=					torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]						)

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

      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 __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)





from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						str					=					[]
      __lowerCAmelCase					:						List[Any]					=					[]
      __lowerCAmelCase					:						str					=					[]

      for rt in rc.restypes:
            __lowerCAmelCase					:						List[Any]					=					rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
            restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]						)
            __lowerCAmelCase					:						List[str]					=					{name: i for i, name in enumerate(lowercase__						)}
            restype_atomaa_to_atomaa_list.append(
                [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]						)

            restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]						)

      # Add dummy mapping for restype 'UNK'
      restype_atomaa_to_atomaa_list.append([0] * 1_4						)
      restype_atomaa_to_atomaa_list.append([0] * 3_7						)
      restype_atomaa_mask_list.append([0.0] * 1_4						)

      __lowerCAmelCase					:						List[Any]					=					torch.tensor(
          lowercase__   ,	dtype=torch.intaa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						Optional[Any]					=					torch.tensor(
          lowercase__   ,	dtype=torch.intaa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						Tuple					=					torch.tensor(
          lowercase__   ,	dtype=torch.floataa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						List[Any]					=					protein['''aatype'''].to(torch.long						)

      # create the mapping for (residx, atom14) --> atom37, i.e. an array
      # with shape (num_res, 14) containing the atom37 indices for this protein
      __lowerCAmelCase					:						Any					=					restype_atomaa_to_atomaa[protein_aatype]
      __lowerCAmelCase					:						Union[str, Any]					=					restype_atomaa_mask[protein_aatype]

      __lowerCAmelCase					:						int					=					residx_atomaa_mask
      __lowerCAmelCase					:						List[str]					=					residx_atomaa_to_atomaa.long()

      # create the gather indices for mapping back
      __lowerCAmelCase					:						int					=					restype_atomaa_to_atomaa[protein_aatype]
      __lowerCAmelCase					:						Union[str, Any]					=					residx_atomaa_to_atomaa.long()

      # create the corresponding mask
      __lowerCAmelCase					:						str					=					torch.zeros([2_1, 3_7]   ,	dtype=torch.floataa   ,	device=protein['''aatype'''].device						)
      for restype, restype_letter in enumerate(rc.restypes						):
            __lowerCAmelCase					:						Optional[int]					=					rc.restype_atoa[restype_letter]
            __lowerCAmelCase					:						Optional[Any]					=					rc.residue_atoms[restype_name]
            for atom_name in atom_names:
                  __lowerCAmelCase					:						str					=					rc.atom_order[atom_name]
                  __lowerCAmelCase					:						List[Any]					=					1

      __lowerCAmelCase					:						Union[str, Any]					=					restype_atomaa_mask[protein_aatype]
      __lowerCAmelCase					:						Any					=					residx_atomaa_mask

      return protein







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Dict					=					tree_map(lambda lowercase__						: torch.tensor(lowercase__   ,	device=batch['''aatype'''].device						)   ,	lowercase__   ,	np.ndarray						)
      __lowerCAmelCase					:						Tuple					=					tensor_tree_map(lambda lowercase__						: np.array(lowercase__						)   ,	make_atomaa_masks(lowercase__						)						)
      return out


