



"""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()
_UpperCAmelCase     =			logging.get_logger(__name__)





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





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(lowercase							,			lowercase )





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Tuple     			=dct.pop(lowercase )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=val





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			="""http://images.cocodataset.org/val2017/000000039769.jpg"""
   SCREAMING_SNAKE_CASE_: List[str]     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   return im





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

    # load original model from timm
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=timm.create_model(lowercase							,			pretrained=lowercase )
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   SCREAMING_SNAKE_CASE_: Dict     			=timm_model.state_dict()
   if base_model:
      remove_classification_head_(lowercase )
   SCREAMING_SNAKE_CASE_: str     			=create_rename_keys(lowercase							,			lowercase )
   for src, dest in rename_keys:
      rename_key(lowercase							,			lowercase							,			lowercase )
   read_in_q_k_v(lowercase							,			lowercase							,			lowercase )

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      SCREAMING_SNAKE_CASE_: Optional[int]     			=ViTModel(lowercase ).eval()
   else:
      SCREAMING_SNAKE_CASE_: str     			=ViTForImageClassification(lowercase ).eval()
   model.load_state_dict(lowercase )

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

   if base_model:
      SCREAMING_SNAKE_CASE_: List[str]     			=timm_model.forward_features(lowercase )
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(lowercase							,			outputs.pooler_output							,			atol=1e-3 )
   else:
      SCREAMING_SNAKE_CASE_: List[str]     			=timm_model(lowercase )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(lowercase							,			outputs.logits							,			atol=1e-3 )

   Path(lowercase ).mkdir(exist_ok=lowercase )
   print(f'''Saving model {vit_name} 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(
       """--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."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




"""simple docstring"""
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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=[]
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   SCREAMING_SNAKE_CASE_: Any     			=[]

   for rt in rc.restypes:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=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] )
      SCREAMING_SNAKE_CASE_: Any     			={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] * 14 )
   restype_atomaa_to_atomaa_list.append([0] * 37 )
   restype_atomaa_mask_list.append([0.0] * 14 )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.tensor(
       lowercase							,			dtype=torch.intaa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.tensor(
       lowercase							,			dtype=torch.intaa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: List[str]     			=torch.tensor(
       lowercase							,			dtype=torch.floataa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: Optional[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
   SCREAMING_SNAKE_CASE_: List[str]     			=restype_atomaa_to_atomaa[protein_aatype]
   SCREAMING_SNAKE_CASE_: Any     			=restype_atomaa_mask[protein_aatype]

   SCREAMING_SNAKE_CASE_: Tuple     			=residx_atomaa_mask
   SCREAMING_SNAKE_CASE_: Dict     			=residx_atomaa_to_atomaa.long()

   # create the gather indices for mapping back
   SCREAMING_SNAKE_CASE_: Dict     			=restype_atomaa_to_atomaa[protein_aatype]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=residx_atomaa_to_atomaa.long()

   # create the corresponding mask
   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.zeros([21, 37]							,			dtype=torch.floataa							,			device=protein["""aatype"""].device )
   for restype, restype_letter in enumerate(rc.restypes ):
      SCREAMING_SNAKE_CASE_: int     			=rc.restype_atoa[restype_letter]
      SCREAMING_SNAKE_CASE_: Any     			=rc.residue_atoms[restype_name]
      for atom_name in atom_names:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=rc.atom_order[atom_name]
         SCREAMING_SNAKE_CASE_: Dict     			=1

   SCREAMING_SNAKE_CASE_: List[str]     			=restype_atomaa_mask[protein_aatype]
   SCREAMING_SNAKE_CASE_: List[Any]     			=residx_atomaa_mask

   return protein





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=tree_map(lambda lowercase : torch.tensor(lowercase							,			device=batch["""aatype"""].device )							,			lowercase							,			np.ndarray )
   SCREAMING_SNAKE_CASE_: int     			=tensor_tree_map(lambda lowercase : np.array(lowercase )							,			make_atomaa_masks(lowercase ) )
   return out



