

'''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 					__UpperCamelCase				(						lowercase__    :	Optional[int]			):






     '''simple docstring'''





     __lowercase      		=SwinConfig()
     __lowercase      		=swin_name.split('_'			)

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

     if model_size == "tiny":
          __lowercase      		=96
          __lowercase      		=(2, 2, 6, 2)
          __lowercase      		=(3, 6, 12, 24)
     elif model_size == "small":
          __lowercase      		=96
          __lowercase      		=(2, 2, 18, 2)
          __lowercase      		=(3, 6, 12, 24)
     elif model_size == "base":
          __lowercase      		=1_28
          __lowercase      		=(2, 2, 18, 2)
          __lowercase      		=(4, 8, 16, 32)
     else:
          __lowercase      		=1_92
          __lowercase      		=(2, 2, 18, 2)
          __lowercase      		=(6, 12, 24, 48)

     if "in22k" in swin_name:
          __lowercase      		=2_18_41
     else:
          __lowercase      		=10_00
          __lowercase      		="huggingface/label-files"
          __lowercase      		="imagenet-1k-id2label.json"
          __lowercase      		=json.load(open(hf_hub_download(lowercase__,       lowercase__,       repo_type='dataset'			),       'r'			)			)
          __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
          __lowercase      		=idalabel
          __lowercase      		={v: k for k, v in idalabel.items()}

     __lowercase      		=img_size
     __lowercase      		=num_classes
     __lowercase      		=embed_dim
     __lowercase      		=depths
     __lowercase      		=num_heads
     __lowercase      		=window_size

     return config

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     if "patch_embed.proj" in name:
          __lowercase      		=name.replace('patch_embed.proj',       'embeddings.patch_embeddings.projection'			)
     if "patch_embed.norm" in name:
          __lowercase      		=name.replace('patch_embed.norm',       'embeddings.norm'			)
     if "layers" in name:
          __lowercase      		="encoder." + name
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "attn" in name:
          __lowercase      		=name.replace('attn',       'attention.self'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)

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

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

     return name

def 					__UpperCamelCase				(						lowercase__    :	List[Any],       lowercase__    :	List[Any]			):






     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __lowercase      		=orig_state_dict.pop(lowercase__			)

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

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

     return orig_state_dict

def 					__UpperCamelCase				(						lowercase__    :	Tuple,       lowercase__    :	Any			):






     '''simple docstring'''





     __lowercase      		=timm.create_model(lowercase__,       pretrained=lowercase__			)
     timm_model.eval()

     __lowercase      		=get_swin_config(lowercase__			)
     __lowercase      		=SwinForImageClassification(lowercase__			)
     model.eval()

     __lowercase      		=convert_state_dict(timm_model.state_dict(),       lowercase__			)
     model.load_state_dict(lowercase__			)

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

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

     __lowercase      		=timm_model(inputs['pixel_values']			)
     __lowercase      		=model(**lowercase__			).logits

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

     print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(lowercase__			)

     print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
     image_processor.save_pretrained(lowercase__			)


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

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






"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case       =       {
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       ["""PoolFormerFeatureExtractor"""]
  __snake_case       =       ["""PoolFormerImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PoolFormerForImageClassification""",
      """PoolFormerModel""",
      """PoolFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_poolformer import (
      POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      PoolFormerConfig,
      PoolFormerOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_poolformer import PoolFormerFeatureExtractor
    from .image_processing_poolformer import PoolFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_poolformer import (
        POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PoolFormerForImageClassification,
        PoolFormerModel,
        PoolFormerPreTrainedModel,
    )


else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)





