


'''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()
A__						:   str				=    logging.get_logger(__name__)

A__						:   Tuple				=    torch.device("""cpu""")



def      UpperCAmelCase__   (      )	->						Any:
     __lowerCamelCase  :      List[str]														=						'http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowerCamelCase  :      int														=						Image.open(requests.get(UpperCAmelCase_ ,       stream=UpperCAmelCase_				).raw				)
     return im



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any]				)	->						Tuple:
     if swiftformer_name == "swiftformer_xs":
          return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]				)

     elif swiftformer_name == "swiftformer_s":
          return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]				)

     elif swiftformer_name == "swiftformer_l1":
          return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]				)

     elif swiftformer_name == "swiftformer_l3":
          return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							List[str]				)	->						int:
     __lowerCamelCase  :      Optional[Any]														=						dct.pop(UpperCAmelCase_				)
     __lowerCamelCase  :      List[str]														=						val



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple				)	->						Dict:
     __lowerCamelCase  :      str														=						[]
     for k in state_dict.keys():
          __lowerCamelCase  :      Optional[Any]														=						k
          if ".pwconv" in k:
               __lowerCamelCase  :      int														=						k_new.replace('.pwconv' ,       '.point_wise_conv'				)
          if ".dwconv" in k:
               __lowerCamelCase  :      Any														=						k_new.replace('.dwconv' ,       '.depth_wise_conv'				)
          if ".Proj." in k:
               __lowerCamelCase  :      List[Any]														=						k_new.replace('.Proj.' ,       '.proj.'				)
          if "patch_embed" in k_new:
               __lowerCamelCase  :      Union[str, Any]														=						k_new.replace('patch_embed' ,       'swiftformer.patch_embed.patch_embedding'				)
          if "network" in k_new:
               __lowerCamelCase  :      Optional[Any]														=						k_new.split('.'				)
               if ls[2].isdigit():
                    __lowerCamelCase  :      int														=						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]				)
               else:
                    __lowerCamelCase  :      List[Any]														=						k_new.replace('network' ,       'swiftformer.encoder.network'				)
          rename_keys.append((k, k_new)				)
     return rename_keys



@torch.no_grad()
def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							Any				)	->						Optional[int]:
     __lowerCamelCase  :      Any														=						SwiftFormerConfig()

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

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

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

     elif swiftformer_name == "swiftformer_l1":
          __lowerCamelCase  :      List[str]														=						[4, 3, 10, 5]
          __lowerCamelCase  :      Union[str, Any]														=						[48, 96, 1_92, 3_84]

     elif swiftformer_name == "swiftformer_l3":
          __lowerCamelCase  :      Union[str, Any]														=						[4, 4, 12, 6]
          __lowerCamelCase  :      Dict														=						[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'				):
               __lowerCamelCase  :      int														=						torch.hub.load_state_dict_from_url(UpperCAmelCase_ ,       map_location='cpu' ,       check_hash=UpperCAmelCase_				)
          else:
               __lowerCamelCase  :      Tuple														=						torch.load(UpperCAmelCase_ ,       map_location='cpu'				)
     __lowerCamelCase  :      Tuple														=						checkpoint

     __lowerCamelCase  :      Any														=						create_rename_keys(UpperCAmelCase_				)
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_				)

     # load HuggingFace model
     __lowerCamelCase  :      List[str]														=						SwiftFormerForImageClassification(UpperCAmelCase_				).eval()
     hf_model.load_state_dict(UpperCAmelCase_				)

     # prepare test inputs
     __lowerCamelCase  :      Dict														=						prepare_img()
     __lowerCamelCase  :      Tuple														=						ViTImageProcessor.from_pretrained('preprocessor_config'				)
     __lowerCamelCase  :      Any														=						processor(images=UpperCAmelCase_ ,       return_tensors='pt'				)

     # compare outputs from both models
     __lowerCamelCase  :      List[Any]														=						get_expected_output(UpperCAmelCase_				)
     __lowerCamelCase  :      Dict														=						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__":
      A__						:   List[Any]				=    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.""")

      A__						:   Union[str, Any]				=    parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}






class 			__a					(						__magic_name__       ,	__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						'focalnet'






       def __init__(   self			,					snake_case=224			,					snake_case=4			,					snake_case=3			,					snake_case=96			,					snake_case=False			,					snake_case=[192, 384, 768, 768]			,					snake_case=[2, 2, 6, 2]			,					snake_case=[2, 2, 2, 2]			,					snake_case=[3, 3, 3, 3]			,					snake_case="gelu"			,					snake_case=4.0			,					snake_case=0.0			,					snake_case=0.1			,					snake_case=False			,					snake_case=1e-4			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=0.02			,					snake_case=1e-5			,					snake_case=32			,					snake_case=None			,					snake_case=None			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(**snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				image_size
        lowerCAmelCase__   :				Union[str, Any] 			=				patch_size
        lowerCAmelCase__   :				List[str] 			=				num_channels
        lowerCAmelCase__   :				List[str] 			=				embed_dim
        lowerCAmelCase__   :				List[Any] 			=				use_conv_embed
        lowerCAmelCase__   :				List[str] 			=				hidden_sizes
        lowerCAmelCase__   :				List[Any] 			=				depths
        lowerCAmelCase__   :				Union[str, Any] 			=				focal_levels
        lowerCAmelCase__   :				Union[str, Any] 			=				focal_windows
        lowerCAmelCase__   :				int 			=				hidden_act
        lowerCAmelCase__   :				List[Any] 			=				mlp_ratio
        lowerCAmelCase__   :				str 			=				hidden_dropout_prob
        lowerCAmelCase__   :				Union[str, Any] 			=				drop_path_rate
        lowerCAmelCase__   :				Tuple 			=				use_layerscale
        lowerCAmelCase__   :				Tuple 			=				layerscale_value
        lowerCAmelCase__   :				str 			=				use_post_layernorm
        lowerCAmelCase__   :				str 			=				use_post_layernorm_in_modulation
        lowerCAmelCase__   :				Union[str, Any] 			=				normalize_modulator
        lowerCAmelCase__   :				Optional[int] 			=				initializer_range
        lowerCAmelCase__   :				List[Any] 			=				layer_norm_eps
        lowerCAmelCase__   :				str 			=				encoder_stride
        lowerCAmelCase__   :				Union[str, Any] 			=				["stem"] + [F"""stage{idx}""" for idx in range(1			,					len(self.depths   ) + 1   )]
        lowerCAmelCase__			,						lowerCAmelCase__   :				Union[str, Any] 			=				get_aligned_output_features_output_indices(
            out_features=snake_case			,					out_indices=snake_case			,					stage_names=self.stage_names   )






