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


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

UpperCamelCase__          =	torch.device('cpu')
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      Optional[int]     							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase__	:      int     							=					Image.open(requests.get(_UpperCamelCase    ,stream=_UpperCamelCase ).raw )
    return im
def 					lowerCamelCase   (     _snake_case ):



    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1] )

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1] )

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2] )

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2] )
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Optional[Any]     							=					dct.pop(_UpperCamelCase )
    UpperCAmelCase__	:      List[str]     							=					val
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      List[Any]     							=					[]
    for k in state_dict.keys():
        UpperCAmelCase__	:      Dict     							=					k
        if ".pwconv" in k:
            UpperCAmelCase__	:      List[str]     							=					k_new.replace('.pwconv'    ,'.point_wise_conv' )
        if ".dwconv" in k:
            UpperCAmelCase__	:      List[Any]     							=					k_new.replace('.dwconv'    ,'.depth_wise_conv' )
        if ".Proj." in k:
            UpperCAmelCase__	:      Any     							=					k_new.replace('.Proj.'    ,'.proj.' )
        if "patch_embed" in k_new:
            UpperCAmelCase__	:      Optional[Any]     							=					k_new.replace('patch_embed'    ,'swiftformer.patch_embed.patch_embedding' )
        if "network" in k_new:
            UpperCAmelCase__	:      Tuple     							=					k_new.split('.' )
            if ls[2].isdigit():
                UpperCAmelCase__	:      Tuple     							=					'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:] )
            else:
                UpperCAmelCase__	:      List[str]     							=					k_new.replace('network'    ,'swiftformer.encoder.network' )
        rename_keys.append((k, k_new) )
    return rename_keys
@torch.no_grad()
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Any     							=					SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    UpperCAmelCase__	:      Any     							=					1000
    UpperCAmelCase__	:      Any     							=					'''huggingface/label-files'''
    UpperCAmelCase__	:      Tuple     							=					'''imagenet-1k-id2label.json'''
    UpperCAmelCase__	:      List[Any]     							=					json.load(open(hf_hub_download(_UpperCamelCase    ,_UpperCamelCase    ,repo_type='dataset' )    ,'r' ) )
    UpperCAmelCase__	:      Any     							=					{int(_UpperCamelCase ): v for k, v in idalabel.items()}
    UpperCAmelCase__	:      Dict     							=					idalabel
    UpperCAmelCase__	:      Union[str, Any]     							=					{v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        UpperCAmelCase__	:      List[Any]     							=					[3, 3, 6, 4]
        UpperCAmelCase__	:      List[Any]     							=					[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        UpperCAmelCase__	:      Union[str, Any]     							=					[3, 3, 9, 6]
        UpperCAmelCase__	:      Union[str, Any]     							=					[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        UpperCAmelCase__	:      Tuple     							=					[4, 3, 10, 5]
        UpperCAmelCase__	:      Any     							=					[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        UpperCAmelCase__	:      Optional[int]     							=					[4, 4, 12, 6]
        UpperCAmelCase__	:      str     							=					[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('https' ):
            UpperCAmelCase__	:      Optional[int]     							=					torch.hub.load_state_dict_from_url(_UpperCamelCase    ,map_location='cpu'    ,check_hash=_UpperCamelCase )
        else:
            UpperCAmelCase__	:      Tuple     							=					torch.load(_UpperCamelCase    ,map_location='cpu' )
    UpperCAmelCase__	:      Dict     							=					checkpoint

    UpperCAmelCase__	:      Dict     							=					create_rename_keys(_UpperCamelCase )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase )

    # load HuggingFace model
    UpperCAmelCase__	:      Optional[Any]     							=					SwiftFormerForImageClassification(_UpperCamelCase ).eval()
    hf_model.load_state_dict(_UpperCamelCase )

    # prepare test inputs
    UpperCAmelCase__	:      str     							=					prepare_img()
    UpperCAmelCase__	:      Dict     							=					ViTImageProcessor.from_pretrained('preprocessor_config' )
    UpperCAmelCase__	:      Dict     							=					processor(images=_UpperCamelCase    ,return_tensors='pt' )

    # compare outputs from both models
    UpperCAmelCase__	:      List[str]     							=					get_expected_output(_UpperCamelCase )
    UpperCAmelCase__	:      Tuple     							=					hf_model(inputs['pixel_values'] ).logits

    assert hf_logits.shape == torch.Size([1, 1000] )
    assert torch.allclose(hf_logits[0, 0:5]    ,_UpperCamelCase    ,atol=1e-3 )

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


if __name__ == "__main__":
    UpperCamelCase__          =	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.')

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



"""simple docstring"""




import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


A__ :       int				= threading.Lock()
A__ :       Optional[logging.Handler]				= None

A__ :       str				= {
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

A__ :       str				= logging.WARNING

A__ :       Union[str, Any]				= True




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[Any]   			=  os.getenv('''TRANSFORMERS_VERBOSITY'''			,					_UpperCamelCase    )
  if env_level_str:
    if env_level_str in log_levels:
      return log_levels[env_level_str]
    else:
      logging.getLogger().warning(
          f'''Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '''
          f'''has to be one of: { ", ".join(log_levels.keys()    ) }'''    )
  return _default_log_level




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  return __name__.split('''.'''    )[0]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  return logging.getLogger(_get_library_name()    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  global _default_handler

  with _lock:
    if _default_handler:
      # This library has already configured the library root logger.
      return
    _lowercase:      int   			=  logging.StreamHandler()  # Set sys.stderr as stream.
    _lowercase:      Dict   			=  sys.stderr.flush

    # Apply our default configuration to the library root logger.
    _lowercase:      Dict   			=  _get_library_root_logger()
    library_root_logger.addHandler(_default_handler    )
    library_root_logger.setLevel(_get_default_logging_level()    )
    _lowercase:      Optional[Any]   			=  False




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  global _default_handler

  with _lock:
    if not _default_handler:
      return

    _lowercase:      Tuple   			=  _get_library_root_logger()
    library_root_logger.removeHandler(_default_handler    )
    library_root_logger.setLevel(logging.NOTSET    )
    _lowercase:      Dict   			=  None




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  return log_levels




def 			_lowerCAmelCase		(		_UpperCamelCase = None    ):






  """simple docstring"""



  if name is None:
    _lowercase:      Tuple   			=  _get_library_name()

  _configure_library_root_logger()
  return logging.getLogger(_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _configure_library_root_logger()
  return _get_library_root_logger().getEffectiveLevel()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _configure_library_root_logger()
  _get_library_root_logger().setLevel(_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  return set_verbosity(_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  return set_verbosity(_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  return set_verbosity(_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  return set_verbosity(_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _configure_library_root_logger()

  assert _default_handler is not None
  _get_library_root_logger().removeHandler(_default_handler    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _configure_library_root_logger()

  assert _default_handler is not None
  _get_library_root_logger().addHandler(_default_handler    )




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _configure_library_root_logger()

  assert handler is not None
  _get_library_root_logger().addHandler(_UpperCamelCase    )




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _configure_library_root_logger()

  assert handler is not None and handler not in _get_library_root_logger().handlers
  _get_library_root_logger().removeHandler(_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _configure_library_root_logger()
  _lowercase:      str   			=  False




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _configure_library_root_logger()
  _lowercase:      List[str]   			=  True




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Any   			=  _get_library_root_logger().handlers

  for handler in handlers:
    _lowercase:      int   			=  logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''    )
    handler.setFormatter(_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  _get_library_root_logger().handlers

  for handler in handlers:
    handler.setFormatter(_UpperCamelCase    )




def 			_lowerCAmelCase		(		self			,					*_UpperCamelCase			,					**_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  os.getenv('''TRANSFORMERS_NO_ADVISORY_WARNINGS'''			,					_UpperCamelCase    )
  if no_advisory_warnings:
    return
  self.warning(*_UpperCamelCase			,					**_UpperCamelCase    )


A__ :       Optional[int]				= warning_advice




@functools.lru_cache(_UpperCamelCase    )
def 			_lowerCAmelCase		(		self			,					*_UpperCamelCase			,					**_UpperCamelCase    ):






  """simple docstring"""



  self.warning(*_UpperCamelCase			,					**_UpperCamelCase    )


A__ :       List[Any]				= warning_once


class       __magic_name__							:
       def __init__(  self			,					*A_			,					**A_							)  ->						Any:  # pylint: disable=unused-argument





         """simple docstring"""




         _lowercase:      Tuple   			=  args[0] if args else None
       def __iter__(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         return iter(self._iterator							)
       def __getattr__(  self			,					A_							)  ->						List[Any]:





         """simple docstring"""




         def empty_fn(*A_			,					**A_							):  # pylint: disable=unused-argument
           return

         return empty_fn
       def __enter__(  self							)  ->						Tuple:





         """simple docstring"""




         return self



       def __exit__(  self			,					A_			,					A_			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         return


class       __magic_name__							:
       def __call__(  self			,					*A_			,					**A_							)  ->						Dict:





         """simple docstring"""




         if _tqdm_active:
           return tqdm_lib.tqdm(*A_			,					**A_							)
         else:
           return EmptyTqdm(*A_			,					**A_							)
       def 				lowercase_				(  self			,					*A_			,					**A_							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  None
         if _tqdm_active:
           return tqdm_lib.tqdm.set_lock(*A_			,					**A_							)



       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         if _tqdm_active:
           return tqdm_lib.tqdm.get_lock()

A__ :       str				= _tqdm_cls()




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  global _tqdm_active
  return bool(_tqdm_active    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  global _tqdm_active
  _lowercase:      str   			=  True
  hf_hub_utils.enable_progress_bars()







def 			_lowerCAmelCase		(		):






  """simple docstring"""



  global _tqdm_active
  _lowercase:      Union[str, Any]   			=  False
  hf_hub_utils.disable_progress_bars()
