



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()
SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Optional[Any]					=						torch.device("""cpu""")




def      __UpperCamelCase     (  )	->  Optional[int]:



    """simple docstring"""






    A			:	int										=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
    A			:	Optional[int]										=       Image.open(requests.get(_lowerCAmelCase				,					stream=_lowerCAmelCase		).raw		)
    return im




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    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     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	Optional[Any]										=       dct.pop(_lowerCAmelCase		)
    A			:	Optional[int]										=       val




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



    """simple docstring"""






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




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Tuple										=       SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    A			:	List[str]										=       1000
    A			:	Any										=       """huggingface/label-files"""
    A			:	Dict										=       """imagenet-1k-id2label.json"""
    A			:	int										=       json.load(open(hf_hub_download(_lowerCAmelCase				,					_lowerCAmelCase				,					repo_type="""dataset"""		)				,					"""r"""		)		)
    A			:	List[Any]										=       {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
    A			:	Union[str, Any]										=       idalabel
    A			:	List[Any]										=       {v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        A			:	str										=       [3, 3, 6, 4]
        A			:	Any										=       [48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        A			:	Optional[int]										=       [3, 3, 9, 6]
        A			:	Tuple										=       [48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        A			:	Union[str, Any]										=       [4, 3, 10, 5]
        A			:	Optional[int]										=       [48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        A			:	int										=       [4, 4, 12, 6]
        A			:	Union[str, Any]										=       [64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith("""https"""		):
            A			:	Optional[int]										=       torch.hub.load_state_dict_from_url(_lowerCAmelCase				,					map_location="""cpu"""				,					check_hash=_lowerCAmelCase		)
        else:
            A			:	Dict										=       torch.load(_lowerCAmelCase				,					map_location="""cpu"""		)
    A			:	Union[str, Any]										=       checkpoint

    A			:	int										=       create_rename_keys(_lowerCAmelCase		)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)

    # load HuggingFace model
    A			:	Optional[Any]										=       SwiftFormerForImageClassification(_lowerCAmelCase		).eval()
    hf_model.load_state_dict(_lowerCAmelCase		)

    # prepare test inputs
    A			:	List[Any]										=       prepare_img()
    A			:	int										=       ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
    A			:	List[Any]										=       processor(images=_lowerCAmelCase				,					return_tensors="""pt"""		)

    # compare outputs from both models
    A			:	Optional[Any]										=       get_expected_output(_lowerCAmelCase		)
    A			:	int										=       hf_model(inputs["""pixel_values"""]		).logits

    assert hf_logits.shape == torch.Size([1, 1000]		)
    assert torch.allclose(hf_logits[0, 0:5]				,					_lowerCAmelCase				,					atol=1e-3		)

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_: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.""")

     SCREAMING_SNAKE_CASE_:List[str]					=						parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:str					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


