


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							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							torch.device('cpu')




def  a   ():
   __a					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
   __a					=  Image.open(requests.get(lowerCAmelCase__		,				stream=lowerCAmelCase__   ).raw   )
   return im




def  a   (lowerCAmelCase__   ):
   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]   )

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]   )

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]   )

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  dct.pop(lowerCAmelCase__   )
   __a					=  val




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




@torch.no_grad()
def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   __a					=  1_000
   __a					=  """huggingface/label-files"""
   __a					=  """imagenet-1k-id2label.json"""
   __a					=  json.load(open(hf_hub_download(lowerCAmelCase__		,				lowerCAmelCase__		,				repo_type="""dataset"""   )		,				"""r"""   )   )
   __a					=  {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
   __a					=  idalabel
   __a					=  {v: k for k, v in idalabel.items()}

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

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

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

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

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

   __a					=  create_rename_keys(lowerCAmelCase__   )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )

   # load HuggingFace model
   __a					=  SwiftFormerForImageClassification(lowerCAmelCase__   ).eval()
   hf_model.load_state_dict(lowerCAmelCase__   )

   # prepare test inputs
   __a					=  prepare_img()
   __a					=  ViTImageProcessor.from_pretrained("""preprocessor_config"""   )
   __a					=  processor(images=lowerCAmelCase__		,				return_tensors="""pt"""   )

   # compare outputs from both models
   __a					=  get_expected_output(lowerCAmelCase__   )
   __a					=  hf_model(inputs["""pixel_values"""]   ).logits

   assert hf_logits.shape == torch.Size([1, 1_000]   )
   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							    =							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							    =							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_torch_available


SCREAMING_SNAKE_CASE							    =							{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'WavLMForAudioFrameClassification',
      'WavLMForCTC',
      'WavLMForSequenceClassification',
      'WavLMForXVector',
      'WavLMModel',
      'WavLMPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



