



"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			="""huggingface/label-files"""
   SCREAMING_SNAKE_CASE_: Optional[Any]     			="""imagenet-1k-id2label.json"""
   SCREAMING_SNAKE_CASE_: str     			=json.load(open(hf_hub_download(lowercase							,			lowercase							,			repo_type="""dataset""" )							,			"""r""" ) )
   SCREAMING_SNAKE_CASE_: Tuple     			={int(lowercase ): v for k, v in idalabel.items()}
   SCREAMING_SNAKE_CASE_: Optional[Any]     			={v: k for k, v in idalabel.items()}

   SCREAMING_SNAKE_CASE_: List[str]     			="""std_conv""" if """bit""" in model_name else False

   # note that when using BiT as backbone for ViT-hybrid checkpoints,
   # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
   # config.conv_layer = "std_conv_same"
   SCREAMING_SNAKE_CASE_: Optional[int]     			=BitConfig(
       conv_layer=lowercase							,			num_labels=1000							,			idalabel=lowercase							,			labelaid=lowercase							,			)

   return config





def      __magic_name__				(							lowercase ):
   if "stem.conv" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""stem.conv"""							,			"""bit.embedder.convolution""" )
   if "blocks" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""blocks"""							,			"""layers""" )
   if "head.fc" in name:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=name.replace("""head.fc"""							,			"""classifier.1""" )
   if name.startswith("""norm""" ):
      SCREAMING_SNAKE_CASE_: Optional[int]     			="""bit.""" + name
   if "bit" not in name and "classifier" not in name:
      SCREAMING_SNAKE_CASE_: Any     			="""bit.encoder.""" + name

   return name





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





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=get_config(lowercase )

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

   # load state_dict of original model
   SCREAMING_SNAKE_CASE_: int     			=timm_model.state_dict()
   for key in state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: Tuple     			=state_dict.pop(lowercase )
      SCREAMING_SNAKE_CASE_: Any     			=val.squeeze() if """head""" in key else val

   # load HuggingFace model
   SCREAMING_SNAKE_CASE_: int     			=BitForImageClassification(lowercase )
   model.eval()
   model.load_state_dict(lowercase )

   # create image processor
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=create_transform(**resolve_data_config({}							,			model=lowercase ) )
   SCREAMING_SNAKE_CASE_: Tuple     			=transform.transforms

   SCREAMING_SNAKE_CASE_: List[str]     			={
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   SCREAMING_SNAKE_CASE_: int     			=BitImageProcessor(
       do_resize=lowercase							,			size={"""shortest_edge""": timm_transforms[0].size}							,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]							,			do_center_crop=lowercase							,			crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}							,			do_normalize=lowercase							,			image_mean=timm_transforms[-1].mean.tolist()							,			image_std=timm_transforms[-1].std.tolist()							,			)

   SCREAMING_SNAKE_CASE_: Optional[int]     			=prepare_img()
   SCREAMING_SNAKE_CASE_: List[str]     			=transform(lowercase ).unsqueeze(0 )
   SCREAMING_SNAKE_CASE_: List[Any]     			=processor(lowercase							,			return_tensors="""pt""" ).pixel_values

   # verify pixel values
   assert torch.allclose(lowercase							,			lowercase )

   # verify logits
   with torch.no_grad():
      SCREAMING_SNAKE_CASE_: str     			=model(lowercase )
      SCREAMING_SNAKE_CASE_: List[str]     			=outputs.logits

   print("""Logits:"""							,			logits[0, :3] )
   print("""Predicted class:"""							,			model.config.idalabel[logits.argmax(-1 ).item()] )
   SCREAMING_SNAKE_CASE_: str     			=timm_model(lowercase )
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(lowercase							,			outputs.logits							,			atol=1e-3 )
   print("""Looks ok!""" )

   if pytorch_dump_folder_path is not None:
      Path(lowercase ).mkdir(exist_ok=lowercase )
      print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' )
      model.save_pretrained(lowercase )
      processor.save_pretrained(lowercase )

   if push_to_hub:
      print(f'''Pushing model {model_name} and processor to the hub''' )
      model.push_to_hub(f'''ybelkada/{model_name}''' )
      processor.push_to_hub(f'''ybelkada/{model_name}''' )


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""resnetv2_50x1_bitm""",
       type=str,
       help="""Name of the BiT 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."""
   )
   parser.add_argument(
       """--push_to_hub""",
       action="""store_true""",
       help="""Whether to push the model to the hub.""",
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""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



