






'''simple docstring'''

import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def    lowercase__			(				__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = SwinConfig()
      UpperCamelCase      = swin_name.split("""_"""   )

      UpperCamelCase      = name_split[1]
      UpperCamelCase      = int(name_split[4]   )
      UpperCamelCase      = int(name_split[3][-1]   )

      if model_size == "tiny":
            UpperCamelCase      = 96
            UpperCamelCase      = (2, 2, 6, 2)
            UpperCamelCase      = (3, 6, 12, 24)
      elif model_size == "small":
            UpperCamelCase      = 96
            UpperCamelCase      = (2, 2, 18, 2)
            UpperCamelCase      = (3, 6, 12, 24)
      elif model_size == "base":
            UpperCamelCase      = 128
            UpperCamelCase      = (2, 2, 18, 2)
            UpperCamelCase      = (4, 8, 16, 32)
      else:
            UpperCamelCase      = 192
            UpperCamelCase      = (2, 2, 18, 2)
            UpperCamelCase      = (6, 12, 24, 48)

      if "in22k" in swin_name:
            UpperCamelCase      = 21841
      else:
            UpperCamelCase      = 1000
            UpperCamelCase      = """huggingface/label-files"""
            UpperCamelCase      = """imagenet-1k-id2label.json"""
            UpperCamelCase      = json.load(open(hf_hub_download(__UpperCamelCase		,						__UpperCamelCase		,						repo_type="""dataset"""   )		,						"""r"""   )   )
            UpperCamelCase      = {int(__UpperCamelCase   ): v for k, v in idalabel.items()}
            UpperCamelCase      = idalabel
            UpperCamelCase      = {v: k for k, v in idalabel.items()}

      UpperCamelCase      = img_size
      UpperCamelCase      = num_classes
      UpperCamelCase      = embed_dim
      UpperCamelCase      = depths
      UpperCamelCase      = num_heads
      UpperCamelCase      = window_size

      return config







def    lowercase__			(				__UpperCamelCase   )->			Tuple:
      if "patch_embed.proj" in name:
            UpperCamelCase      = name.replace("""patch_embed.proj"""		,						"""embeddings.patch_embeddings.projection"""   )
      if "patch_embed.norm" in name:
            UpperCamelCase      = name.replace("""patch_embed.norm"""		,						"""embeddings.norm"""   )
      if "layers" in name:
            UpperCamelCase      = """encoder.""" + name
      if "attn.proj" in name:
            UpperCamelCase      = name.replace("""attn.proj"""		,						"""attention.output.dense"""   )
      if "attn" in name:
            UpperCamelCase      = name.replace("""attn"""		,						"""attention.self"""   )
      if "norm1" in name:
            UpperCamelCase      = name.replace("""norm1"""		,						"""layernorm_before"""   )
      if "norm2" in name:
            UpperCamelCase      = name.replace("""norm2"""		,						"""layernorm_after"""   )
      if "mlp.fc1" in name:
            UpperCamelCase      = name.replace("""mlp.fc1"""		,						"""intermediate.dense"""   )
      if "mlp.fc2" in name:
            UpperCamelCase      = name.replace("""mlp.fc2"""		,						"""output.dense"""   )

      if name == "norm.weight":
            UpperCamelCase      = """layernorm.weight"""
      if name == "norm.bias":
            UpperCamelCase      = """layernorm.bias"""

      if "head" in name:
            UpperCamelCase      = name.replace("""head"""		,						"""classifier"""   )
      else:
            UpperCamelCase      = """swin.""" + name

      return name







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      for key in orig_state_dict.copy().keys():
            UpperCamelCase      = orig_state_dict.pop(__UpperCamelCase   )

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  UpperCamelCase      = key.split("""."""   )
                  UpperCamelCase      = int(key_split[1]   )
                  UpperCamelCase      = int(key_split[3]   )
                  UpperCamelCase      = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        UpperCamelCase      = val[:dim, :]
                        UpperCamelCase      = val[
                            dim : dim * 2, :
                        ]
                        UpperCamelCase      = val[-dim:, :]
                  else:
                        UpperCamelCase      = val[
                            :dim
                        ]
                        UpperCamelCase      = val[
                            dim : dim * 2
                        ]
                        UpperCamelCase      = val[
                            -dim:
                        ]
            else:
                  UpperCamelCase      = val

      return orig_state_dict







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			int:
      UpperCamelCase      = timm.create_model(__UpperCamelCase		,						pretrained=__UpperCamelCase   )
      timm_model.eval()

      UpperCamelCase      = get_swin_config(__UpperCamelCase   )
      UpperCamelCase      = SwinForImageClassification(__UpperCamelCase   )
      model.eval()

      UpperCamelCase      = convert_state_dict(timm_model.state_dict()		,						__UpperCamelCase   )
      model.load_state_dict(__UpperCamelCase   )

      UpperCamelCase      = """http://images.cocodataset.org/val2017/000000039769.jpg"""

      UpperCamelCase      = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""		,						"""-"""   )   )   )
      UpperCamelCase      = Image.open(requests.get(__UpperCamelCase		,						stream=__UpperCamelCase   ).raw   )
      UpperCamelCase      = image_processor(images=__UpperCamelCase		,						return_tensors="""pt"""   )

      UpperCamelCase      = timm_model(inputs["""pixel_values"""]   )
      UpperCamelCase      = model(**__UpperCamelCase   ).logits

      assert torch.allclose(__UpperCamelCase		,						__UpperCamelCase		,						atol=1E-3   )

      print(F"Saving model {swin_name} to {pytorch_dump_folder_path}"   )
      model.save_pretrained(__UpperCamelCase   )

      print(F"Saving image processor to {pytorch_dump_folder_path}"   )
      image_processor.save_pretrained(__UpperCamelCase   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swin_name',
         default='swin_tiny_patch4_window7_224',
         type=str,
         help='Name of the Swin 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.'
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








'''simple docstring'''

from timeit import timeit







def    lowercase__			(				__UpperCamelCase   )->			int:
      if number < 0:
            raise ValueError("""the value of input must not be negative"""   )
      UpperCamelCase      = 0
      while number:
            number &= number - 1
            result += 1
      return result







def    lowercase__			(				__UpperCamelCase   )->			int:
      if number < 0:
            raise ValueError("""the value of input must not be negative"""   )
      UpperCamelCase      = 0
      while number:
            if number % 2 == 1:
                  result += 1
            number >>= 1
      return result







def    lowercase__			(				)->			None:

      def do_benchmark(__UpperCamelCase   ) -> None:
            UpperCamelCase      = """import __main__ as z"""
            print(F"Benchmark when {number = }:"   )
            print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase   ) = }"   )
            UpperCamelCase      = timeit("""z.get_set_bits_count_using_modulo_operator(25)"""		,						setup=__UpperCamelCase   )
            print(F"timeit() runs in {timing} seconds"   )
            print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase   ) = }"   )
            UpperCamelCase      = timeit(
                """z.get_set_bits_count_using_brian_kernighans_algorithm(25)"""		,						setup=__UpperCamelCase		,						)
            print(F"timeit() runs in {timing} seconds"   )

      for number in (25, 37, 58, 0):
            do_benchmark(__UpperCamelCase   )
            print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()
