





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()
lowercase_          =		logging.get_logger(__name__)

lowercase_          =		torch.device("""cpu""")




def 					__UpperCamelCase  ()    ->       Union[str, Any]:
  lowercase__           =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase__           =  Image.open(requests.get(UpperCAmelCase__		,						stream=UpperCAmelCase__		).raw		)
  return im




def 					__UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  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  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__           =  dct.pop(UpperCAmelCase__		)
  lowercase__           =  val




def 					__UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  lowercase__           =  []
  for k in state_dict.keys():
    lowercase__           =  k
    if ".pwconv" in k:
      lowercase__           =  k_new.replace('.pwconv'		,						'.point_wise_conv'		)
    if ".dwconv" in k:
      lowercase__           =  k_new.replace('.dwconv'		,						'.depth_wise_conv'		)
    if ".Proj." in k:
      lowercase__           =  k_new.replace('.Proj.'		,						'.proj.'		)
    if "patch_embed" in k_new:
      lowercase__           =  k_new.replace('patch_embed'		,						'swiftformer.patch_embed.patch_embedding'		)
    if "network" in k_new:
      lowercase__           =  k_new.split('.'		)
      if ls[2].isdigit():
        lowercase__           =  """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]		)
      else:
        lowercase__           =  k_new.replace('network'		,						'swiftformer.encoder.network'		)
    rename_keys.append((k, k_new)		)
  return rename_keys




@torch.no_grad()
def 					__UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:
  lowercase__           =  SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  lowercase__           =  1000
  lowercase__           =  """huggingface/label-files"""
  lowercase__           =  """imagenet-1k-id2label.json"""
  lowercase__           =  json.load(open(hf_hub_download(UpperCAmelCase__		,						UpperCAmelCase__		,						repo_type='dataset'		)		,						'r'		)		)
  lowercase__           =  {int(UpperCAmelCase__		): v for k, v in idalabel.items()}
  lowercase__           =  idalabel
  lowercase__           =  {v: k for k, v in idalabel.items()}

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

  elif swiftformer_name == "swiftformer_s":
    lowercase__           =  [3, 3, 9, 6]
    lowercase__           =  [48, 64, 168, 224]

  elif swiftformer_name == "swiftformer_l1":
    lowercase__           =  [4, 3, 10, 5]
    lowercase__           =  [48, 96, 192, 384]

  elif swiftformer_name == "swiftformer_l3":
    lowercase__           =  [4, 4, 12, 6]
    lowercase__           =  [64, 128, 320, 512]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith('https'		):
      lowercase__           =  torch.hub.load_state_dict_from_url(UpperCAmelCase__		,						map_location='cpu'		,						check_hash=UpperCAmelCase__		)
    else:
      lowercase__           =  torch.load(UpperCAmelCase__		,						map_location='cpu'		)
  lowercase__           =  checkpoint

  lowercase__           =  create_rename_keys(UpperCAmelCase__		)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(UpperCAmelCase__		,						UpperCAmelCase__		,						UpperCAmelCase__		)

  # load HuggingFace model
  lowercase__           =  SwiftFormerForImageClassification(UpperCAmelCase__		).eval()
  hf_model.load_state_dict(UpperCAmelCase__		)

  # prepare test inputs
  lowercase__           =  prepare_img()
  lowercase__           =  ViTImageProcessor.from_pretrained('preprocessor_config'		)
  lowercase__           =  processor(images=UpperCAmelCase__		,						return_tensors='pt'		)

  # compare outputs from both models
  lowercase__           =  get_expected_output(UpperCAmelCase__		)
  lowercase__           =  hf_model(inputs['pixel_values']		).logits

  assert hf_logits.shape == torch.Size([1, 1000]		)
  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__":
      lowercase_          =		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.""")

      lowercase_          =		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,
    is_vision_available,
)


lowercase_          =		{
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		["""EfficientFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EfficientFormerForImageClassification""",
          """EfficientFormerForImageClassificationWithTeacher""",
          """EfficientFormerModel""",
          """EfficientFormerPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEfficientFormerForImageClassification""",
          """TFEfficientFormerForImageClassificationWithTeacher""",
          """TFEfficientFormerModel""",
          """TFEfficientFormerPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientformer import EfficientFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientformer import (
                EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientFormerForImageClassification,
                EfficientFormerForImageClassificationWithTeacher,
                EfficientFormerModel,
                EfficientFormerPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_efficientformer import (
                TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
                TFEfficientFormerModel,
                TFEfficientFormerPreTrainedModel,
            )

else:
      import sys

      lowercase_          =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


