


"""simple docstring"""



import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


__snake_case   			=    Mapping[str, np.ndarray]
__snake_case   			=    Mapping[str, Any]  # Is a nested dict.
__snake_case   			=    0.01







@dataclasses.dataclass(frozen=lowerCamelCase__	)
class _lowerCAmelCase				:
   __UpperCAmelCase :       np.ndarray  # [num_res, num_atom_type, 3]

   # Amino-acid type for each residue represented as an integer between 0 and
   # 20, where 20 is 'X'.
   __UpperCAmelCase :       np.ndarray  # [num_res]

   # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
   # is present and 0.0 if not. This should be used for loss masking.
   __UpperCAmelCase :       np.ndarray  # [num_res, num_atom_type]

   # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
   __UpperCAmelCase :       np.ndarray  # [num_res]

   # B-factors, or temperature factors, of each residue (in sq. angstroms units),
   # representing the displacement of the residue from its ground truth mean
   # value.
   __UpperCAmelCase :       np.ndarray  # [num_res, num_atom_type]

   # Chain indices for multi-chain predictions
   __UpperCAmelCase :       Optional[np.ndarray]							  =  None

   # Optional remark about the protein. Included as a comment in output PDB
   # files
   __UpperCAmelCase :       Optional[str]							  =  None

   # Templates used to generate this protein (prediction-only)
   __UpperCAmelCase :       Optional[Sequence[str]]							  =  None

   # Chain corresponding to each parent
   __UpperCAmelCase :       Optional[Sequence[int]]							  =  None







def   __lowerCAmelCase						(						lowercase  :  str )	->						Dict:


    """simple docstring"""





    snake_case    :      Optional[Any]      					=    R"(\[[A-Z]+\]\n)"
    snake_case    :      Tuple      					=    [tag.strip() for tag in re.split(_A						,							_A ) if len(_A ) > 0]
    snake_case    :      Dict      					=    zip(tags[0::2]						,							[l.split("\n" ) for l in tags[1::2]] )

    snake_case    :      List[str]      					=    ["N", "CA", "C"]
    snake_case    :      Optional[int]      					=    None
    snake_case    :      Optional[int]      					=    None
    snake_case    :      Optional[Any]      					=    None
    for g in groups:
        if "[PRIMARY]" == g[0]:
            snake_case    :      Tuple      					=    g[1][0].strip()
            for i in range(len(_A ) ):
                if seq[i] not in residue_constants.restypes:
                    snake_case    :      int      					=    "X"  # FIXME: strings are immutable
            snake_case    :      str      					=    np.array(
                [residue_constants.restype_order.get(_A						,							residue_constants.restype_num ) for res_symbol in seq] )
        elif "[TERTIARY]" == g[0]:
            snake_case    :      Optional[int]      					=    []
            for axis in range(3 ):
                tertiary.append(list(map(_A						,							g[1][axis].split() ) ) )
            snake_case    :      Optional[Any]      					=    np.array(_A )
            snake_case    :      Any      					=    np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
            for i, atom in enumerate(_A ):
                snake_case    :      List[str]      					=    np.transpose(tertiary_np[:, i::3] )
            atom_positions *= PICO_TO_ANGSTROM
        elif "[MASK]" == g[0]:
            snake_case    :      Optional[int]      					=    np.array(list(map({"-": 0, "+": 1}.get						,							g[1][0].strip() ) ) )
            snake_case    :      Optional[Any]      					=    np.zeros(
                (
                    len(_A ),
                    residue_constants.atom_type_num,
                ) ).astype(np.floataa )
            for i, atom in enumerate(_A ):
                snake_case    :      List[Any]      					=    1
            atom_mask *= mask[..., None]

    assert aatype is not None

    return Protein(
        atom_positions=_A						,							atom_mask=_A						,							aatype=_A						,							residue_index=np.arange(len(_A ) )						,							b_factors=_A						,							)







def   __lowerCAmelCase						(						lowercase  :  Protein						,							lowercase  :  int = 0 )	->						List[str]:


    """simple docstring"""





    snake_case    :      Dict      					=    []

    snake_case    :      Optional[int]      					=    prot.remark
    if remark is not None:
        pdb_headers.append(F'REMARK {remark}' )

    snake_case    :      Union[str, Any]      					=    prot.parents
    snake_case    :      Dict      					=    prot.parents_chain_index
    if parents is not None and parents_chain_index is not None:
        snake_case    :      Tuple      					=    [p for i, p in zip(_A						,							_A ) if i == chain_id]

    if parents is None or len(_A ) == 0:
        snake_case    :      List[Any]      					=    ["N/A"]

    pdb_headers.append(F'PARENT {" ".join(_A )}' )

    return pdb_headers







def   __lowerCAmelCase						(						lowercase  :  Protein						,							lowercase  :  str )	->						Dict:


    """simple docstring"""





    snake_case    :      Optional[Any]      					=    []
    snake_case    :      str      					=    pdb_str.split("\n" )

    snake_case    :      str      					=    prot.remark
    if remark is not None:
        out_pdb_lines.append(F'REMARK {remark}' )

    snake_case    :      Tuple      					=    42
    if prot.parents is not None and len(prot.parents ) > 0:
        snake_case    :      List[Any]      					=    []
        if prot.parents_chain_index is not None:
            snake_case    :      Tuple      					=    {}
            for p, i in zip(prot.parents						,							prot.parents_chain_index ):
                parent_dict.setdefault(str(_A )						,							[] )
                parent_dict[str(_A )].append(_A )

            snake_case    :      Optional[Any]      					=    max([int(_A ) for chain_idx in parent_dict] )
            for i in range(max_idx + 1 ):
                snake_case    :      int      					=    parent_dict.get(str(_A )						,							["N/A"] )
                parents_per_chain.append(_A )
        else:
            parents_per_chain.append(list(prot.parents ) )
    else:
        snake_case    :      Dict      					=    [["N/A"]]

    def make_parent_line(lowercase  :  Sequence[str] ) -> str:
        return F'PARENT {" ".join(_A )}'

    out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )

    snake_case    :      Union[str, Any]      					=    0
    for i, l in enumerate(_A ):
        if "PARENT" not in l and "REMARK" not in l:
            out_pdb_lines.append(_A )
        if "TER" in l and "END" not in lines[i + 1]:
            chain_counter += 1
            if not chain_counter >= len(_A ):
                snake_case    :      Tuple      					=    parents_per_chain[chain_counter]
            else:
                snake_case    :      int      					=    ["N/A"]

            out_pdb_lines.append(make_parent_line(_A ) )

    return "\n".join(_A )







def   __lowerCAmelCase						(						lowercase  :  Protein )	->						str:


    """simple docstring"""





    snake_case    :      List[str]      					=    residue_constants.restypes + ["X"]

    def res_atoa(lowercase  :  int ) -> str:
        return residue_constants.restype_atoa.get(restypes[r]						,							"UNK" )

    snake_case    :      Any      					=    residue_constants.atom_types

    snake_case    :      str      					=    []

    snake_case    :      Any      					=    prot.atom_mask
    snake_case    :      str      					=    prot.aatype
    snake_case    :      List[str]      					=    prot.atom_positions
    snake_case    :      List[Any]      					=    prot.residue_index.astype(np.intaa )
    snake_case    :      Union[str, Any]      					=    prot.b_factors
    snake_case    :      Any      					=    prot.chain_index

    if np.any(aatype > residue_constants.restype_num ):
        raise ValueError("Invalid aatypes." )

    snake_case    :      List[Any]      					=    get_pdb_headers(_A )
    if len(_A ) > 0:
        pdb_lines.extend(_A )

    snake_case    :      List[Any]      					=    aatype.shape[0]
    snake_case    :      str      					=    1
    snake_case    :      Optional[Any]      					=    0
    snake_case    :      Optional[Any]      					=    string.ascii_uppercase
    snake_case    :      Any      					=    None
    # Add all atom sites.
    for i in range(_A ):
        snake_case    :      Tuple      					=    res_atoa(aatype[i] )
        for atom_name, pos, mask, b_factor in zip(_A						,							atom_positions[i]						,							atom_mask[i]						,							b_factors[i] ):
            if mask < 0.5:
                continue

            snake_case    :      List[Any]      					=    "ATOM"
            snake_case    :      List[Any]      					=    atom_name if len(_A ) == 4 else F' {atom_name}'
            snake_case    :      Any      					=    ""
            snake_case    :      Tuple      					=    ""
            snake_case    :      Union[str, Any]      					=    1.00
            snake_case    :      int      					=    atom_name[0]  # Protein supports only C, N, O, S, this works.
            snake_case    :      Any      					=    ""

            snake_case    :      str      					=    "A"
            if chain_index is not None:
                snake_case    :      Union[str, Any]      					=    chain_tags[chain_index[i]]

            # PDB is a columnar format, every space matters here!
            snake_case    :      int      					=    (
                F'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
                F'{res_name_a:>3} {chain_tag:>1}'
                F'{residue_index[i]:>4}{insertion_code:>1}   '
                F'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
                F'{occupancy:>6.2f}{b_factor:>6.2f}          '
                F'{element:>2}{charge:>2}'
            )
            pdb_lines.append(_A )
            atom_index += 1

        snake_case    :      List[str]      					=    i == n - 1
        if chain_index is not None:
            if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                snake_case    :      str      					=    True
                snake_case    :      Optional[int]      					=    chain_index[i + 1]

        if should_terminate:
            # Close the chain.
            snake_case    :      int      					=    "TER"
            snake_case    :      int      					=    (
                F'{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}'
            )
            pdb_lines.append(_A )
            atom_index += 1

            if i != n - 1:
                # "prev" is a misnomer here. This happens at the beginning of
                # each new chain.
                pdb_lines.extend(get_pdb_headers(_A						,							_A ) )

    pdb_lines.append("END" )
    pdb_lines.append("" )
    return "\n".join(_A )







def   __lowerCAmelCase						(						lowercase  :  Protein )	->						Tuple:


    """simple docstring"""





    return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







def   __lowerCAmelCase						(						lowercase  :  FeatureDict						,							lowercase  :  ModelOutput						,							lowercase  :  Optional[np.ndarray] = None						,							lowercase  :  Optional[np.ndarray] = None						,							lowercase  :  Optional[str] = None						,							lowercase  :  Optional[Sequence[str]] = None						,							lowercase  :  Optional[Sequence[int]] = None						,							)	->						Union[str, Any]:


    """simple docstring"""





    return Protein(
        aatype=features["aatype"]						,							atom_positions=result["final_atom_positions"]						,							atom_mask=result["final_atom_mask"]						,							residue_index=features["residue_index"] + 1						,							b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"] )						,							chain_index=_A						,							remark=_A						,							parents=_A						,							parents_chain_index=_A						,							)








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 (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

lowerCamelCase           =					torch.device('''cpu''')




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  a__  =Image.open(requests.get(_A	,      stream=_A							).raw							)
  return im




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]							)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]							)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]							)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]							)




def 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : Union[str, Any]	,      _A      : str							):





  '''simple docstring'''







  a__  =dct.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Any							):





  '''simple docstring'''







  a__  =[]
  for k in state_dict.keys():
    a__  =k
    if ".pwconv" in k:
      a__  =k_new.replace('''.pwconv'''	,      '''.point_wise_conv'''							)
    if ".dwconv" in k:
      a__  =k_new.replace('''.dwconv'''	,      '''.depth_wise_conv'''							)
    if ".Proj." in k:
      a__  =k_new.replace('''.Proj.'''	,      '''.proj.'''							)
    if "patch_embed" in k_new:
      a__  =k_new.replace('''patch_embed'''	,      '''swiftformer.patch_embed.patch_embedding'''							)
    if "network" in k_new:
      a__  =k_new.split('''.'''							)
      if ls[2].isdigit():
        a__  ='''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]							)
      else:
        a__  =k_new.replace('''network'''	,      '''swiftformer.encoder.network'''							)
    rename_keys.append((k, k_new)							)
  return rename_keys




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : int	,      _A      : Union[str, Any]	,      _A      : int							):





  '''simple docstring'''







  a__  =SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  a__  =10_00
  a__  ='''huggingface/label-files'''
  a__  ='''imagenet-1k-id2label.json'''
  a__  =json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''							)	,      '''r'''							)							)
  a__  ={int(_A							): v for k, v in idalabel.items()}
  a__  =idalabel
  a__  ={v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    a__  =[3, 3, 6, 4]
    a__  =[48, 56, 1_12, 2_20]

  elif swiftformer_name == "swiftformer_s":
    a__  =[3, 3, 9, 6]
    a__  =[48, 64, 1_68, 2_24]

  elif swiftformer_name == "swiftformer_l1":
    a__  =[4, 3, 10, 5]
    a__  =[48, 96, 1_92, 3_84]

  elif swiftformer_name == "swiftformer_l3":
    a__  =[4, 4, 12, 6]
    a__  =[64, 1_28, 3_20, 5_12]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith('''https'''							):
      a__  =torch.hub.load_state_dict_from_url(_A	,      map_location='''cpu'''	,      check_hash=_A							)
    else:
      a__  =torch.load(_A	,      map_location='''cpu'''							)
  a__  =checkpoint

  a__  =create_rename_keys(_A							)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_A	,      _A	,      _A							)

  # load HuggingFace model
  a__  =SwiftFormerForImageClassification(_A							).eval()
  hf_model.load_state_dict(_A							)

  # prepare test inputs
  a__  =prepare_img()
  a__  =ViTImageProcessor.from_pretrained('''preprocessor_config'''							)
  a__  =processor(images=_A	,      return_tensors='''pt'''							)

  # compare outputs from both models
  a__  =get_expected_output(_A							)
  a__  =hf_model(inputs['''pixel_values''']							).logits

  assert hf_logits.shape == torch.Size([1, 10_00]							)
  assert torch.allclose(hf_logits[0, 0:5]	,      _A	,      atol=1E-3							)

  Path(_A							).mkdir(exist_ok=_A							)
  print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""							)
  hf_model.save_pretrained(_A							)


if __name__ == "__main__":
       lowerCamelCase           =					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--swiftformer_name''',
           default='''swiftformer_xs''',
           choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
           type=str,
           help='''Name of the SwiftFormer model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default='''./converted_outputs/''',
           type=str,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

       lowerCamelCase           =					parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
