'''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()
__lowerCamelCase    :				List[str]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Any							     =	torch.device('''cpu''')



def 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  SCREAMING_SNAKE_CASE__    							=   Image.open(requests.get(__SCREAMING_SNAKE_CASE				,  stream=__SCREAMING_SNAKE_CASE			).raw			)
  return im



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]			)							->      Tuple:







  """simple docstring"""





  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]			)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]			)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]			)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: str			)							->      Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   dct.pop(__SCREAMING_SNAKE_CASE			)
  SCREAMING_SNAKE_CASE__    							=   val



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]			)							->      Tuple:







  """simple docstring"""





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



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: int				,  __UpperCamelCase					: Union[str, Any]			)							->      List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   SwiftFormerConfig()

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

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

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

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

  elif swiftformer_name == "swiftformer_l3":
    SCREAMING_SNAKE_CASE__    							=   [4, 4, 12, 6]
    SCREAMING_SNAKE_CASE__    							=   [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"""			):
      SCREAMING_SNAKE_CASE__    							=   torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE				,  map_location="""cpu"""				,  check_hash=__SCREAMING_SNAKE_CASE			)
    else:
      SCREAMING_SNAKE_CASE__    							=   torch.load(__SCREAMING_SNAKE_CASE				,  map_location="""cpu"""			)
  SCREAMING_SNAKE_CASE__    							=   checkpoint

  SCREAMING_SNAKE_CASE__    							=   create_rename_keys(__SCREAMING_SNAKE_CASE			)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE			)

  # load HuggingFace model
  SCREAMING_SNAKE_CASE__    							=   SwiftFormerForImageClassification(__SCREAMING_SNAKE_CASE			).eval()
  hf_model.load_state_dict(__SCREAMING_SNAKE_CASE			)

  # prepare test inputs
  SCREAMING_SNAKE_CASE__    							=   prepare_img()
  SCREAMING_SNAKE_CASE__    							=   ViTImageProcessor.from_pretrained("""preprocessor_config"""			)
  SCREAMING_SNAKE_CASE__    							=   processor(images=__SCREAMING_SNAKE_CASE				,  return_tensors="""pt"""			)

  # compare outputs from both models
  SCREAMING_SNAKE_CASE__    							=   get_expected_output(__SCREAMING_SNAKE_CASE			)
  SCREAMING_SNAKE_CASE__    							=   hf_model(inputs["""pixel_values"""]			).logits

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

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


if __name__ == "__main__":
   __lowerCamelCase    :				str							     =	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    :				int							     =	parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
