

'''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										=			logging.get_logger(__name__)

__lowerCamelCase										=			torch.device('''cpu''')
def 					UpperCAmelCase__			(					)	->	str:
       A_		    =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_		    =					Image.open(requests.get(_UpperCamelCase,				stream=_UpperCamelCase ).raw )
       return im
def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	Union[str, Any]:



       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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ )	->	Union[str, Any]:
       A_		    =					dct.pop(_UpperCamelCase )
       A_		    =					val
def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	List[Any]:
       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__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ )	->	Union[str, Any]:
       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(_UpperCamelCase,				_UpperCamelCase,				repo_type="""dataset""" ),				"""r""" ) )
       A_		    =					{int(_UpperCamelCase ): 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(_UpperCamelCase,				map_location="""cpu""",				check_hash=_UpperCamelCase )
              else:
                     A_		    =					torch.load(_UpperCamelCase,				map_location="""cpu""" )
       A_		    =					checkpoint

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

       # load HuggingFace model
       A_		    =					SwiftFormerForImageClassification(_UpperCamelCase ).eval()
       hf_model.load_state_dict(_UpperCamelCase )

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

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

       assert hf_logits.shape == torch.Size([1, 10_00] )
       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__":
      __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)




"""simple docstring"""






from ..utils import DummyObject, requires_backends





class      lowerCamelCase__   (     metaclass=A				):







					"""simple docstring"""


					__a	       =							["""note_seq"""]






					def __init__(       self    : List[Any] ,       *UpperCamelCase    : List[Any] ,       **UpperCamelCase    : Tuple  ):
												'''simple docstring'''






												requires_backends(self ,       ["""note_seq"""]  )






					@classmethod
					def lowerCamelCase__							(       cls    : Union[str, Any] ,       *UpperCamelCase    : Union[str, Any] ,       **UpperCamelCase    : str  ):
												'''simple docstring'''






												requires_backends(cls ,       ["""note_seq"""]  )






					@classmethod
					def lowerCamelCase__							(       cls    : List[str] ,       *UpperCamelCase    : Optional[Any] ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												requires_backends(cls ,       ["""note_seq"""]  )
