


'''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     :  Any											=    logging.get_logger(__name__)

lowerCAmelCase     :  Union[str, Any]											=    torch.device("""cpu""")






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							'http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCAmelCase     :				str       =							Image.open(requests.get(_A ,    stream=_A       ).raw       )
       return im






def       lowercase					(_A       ):







       """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       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Dict       =							dct.pop(_A       )
       _lowerCAmelCase     :				Optional[Any]       =							val






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							[]
       for k in state_dict.keys():
              _lowerCAmelCase     :				str       =							k
              if ".pwconv" in k:
                     _lowerCAmelCase     :				Optional[int]       =							k_new.replace('.pwconv' ,    '.point_wise_conv'       )
              if ".dwconv" in k:
                     _lowerCAmelCase     :				int       =							k_new.replace('.dwconv' ,    '.depth_wise_conv'       )
              if ".Proj." in k:
                     _lowerCAmelCase     :				Optional[int]       =							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     :				int       =							k_new.split('.'       )
                     if ls[2].isdigit():
                            _lowerCAmelCase     :				Union[str, Any]       =							'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       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							SwiftFormerConfig()

       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       _lowerCAmelCase     :				int       =							1_0_0_0
       _lowerCAmelCase     :				Optional[Any]       =							'huggingface/label-files'
       _lowerCAmelCase     :				List[Any]       =							'imagenet-1k-id2label.json'
       _lowerCAmelCase     :				Optional[int]       =							json.load(open(hf_hub_download(_A ,    _A ,    repo_type='dataset'       ) ,    'r'       )       )
       _lowerCAmelCase     :				List[Any]       =							{int(_A       ): v for k, v in idalabel.items()}
       _lowerCAmelCase     :				Any       =							idalabel
       _lowerCAmelCase     :				Optional[Any]       =							{v: k for k, v in idalabel.items()}

       # size of the architecture
       if swiftformer_name == "swiftformer_xs":
              _lowerCAmelCase     :				List[Any]       =							[3, 3, 6, 4]
              _lowerCAmelCase     :				List[str]       =							[4_8, 5_6, 1_1_2, 2_2_0]

       elif swiftformer_name == "swiftformer_s":
              _lowerCAmelCase     :				Dict       =							[3, 3, 9, 6]
              _lowerCAmelCase     :				Union[str, Any]       =							[4_8, 6_4, 1_6_8, 2_2_4]

       elif swiftformer_name == "swiftformer_l1":
              _lowerCAmelCase     :				Optional[int]       =							[4, 3, 1_0, 5]
              _lowerCAmelCase     :				Optional[int]       =							[4_8, 9_6, 1_9_2, 3_8_4]

       elif swiftformer_name == "swiftformer_l3":
              _lowerCAmelCase     :				Tuple       =							[4, 4, 1_2, 6]
              _lowerCAmelCase     :				Dict       =							[6_4, 1_2_8, 3_2_0, 5_1_2]

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

       _lowerCAmelCase     :				Any       =							create_rename_keys(_A       )
       for rename_key_src, rename_key_dest in rename_keys:
              rename_key(_A ,    _A ,    _A       )

       # load HuggingFace model
       _lowerCAmelCase     :				str       =							SwiftFormerForImageClassification(_A       ).eval()
       hf_model.load_state_dict(_A       )

       # prepare test inputs
       _lowerCAmelCase     :				Any       =							prepare_img()
       _lowerCAmelCase     :				int       =							ViTImageProcessor.from_pretrained('preprocessor_config'       )
       _lowerCAmelCase     :				Optional[Any]       =							processor(images=_A ,    return_tensors='pt'       )

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

       assert hf_logits.shape == torch.Size([1, 1_0_0_0]       )
       assert torch.allclose(hf_logits[0, 0:5] ,    _A ,    atol=1E-3       )

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


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

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





'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCAmelCase     :  Optional[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

