





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    List[Any]:
  __snake_case:						Tuple   =						SwinvaConfig()
  __snake_case:						List[Any]   =						swinva_name.split("""_""")

  __snake_case:						List[Any]   =						name_split[1]
  if "to" in name_split[3]:
    __snake_case:						Dict   =						int(name_split[3][-3:])
  else:
    __snake_case:						Dict   =						int(name_split[3])
  if "to" in name_split[2]:
    __snake_case:						Optional[int]   =						int(name_split[2][-2:])
  else:
    __snake_case:						Optional[int]   =						int(name_split[2][6:])

  if model_size == "tiny":
    __snake_case:						Any   =						96
    __snake_case:						str   =						(2, 2, 6, 2)
    __snake_case:						List[Any]   =						(3, 6, 12, 24)
  elif model_size == "small":
    __snake_case:						Tuple   =						96
    __snake_case:						int   =						(2, 2, 18, 2)
    __snake_case:						int   =						(3, 6, 12, 24)
  elif model_size == "base":
    __snake_case:						str   =						128
    __snake_case:						Optional[Any]   =						(2, 2, 18, 2)
    __snake_case:						str   =						(4, 8, 16, 32)
  else:
    __snake_case:						Any   =						192
    __snake_case:						Any   =						(2, 2, 18, 2)
    __snake_case:						str   =						(6, 12, 24, 48)

  if "to" in swinva_name:
    __snake_case:						str   =						(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    __snake_case:						Any   =						2_1841
    __snake_case:						List[str]   =						"""huggingface/label-files"""
    __snake_case:						Optional[int]   =						"""imagenet-22k-id2label.json"""
    __snake_case:						List[str]   =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					repo_type="""dataset""")					,					"""r"""))
    __snake_case:						Union[str, Any]   =						{int(SCREAMING_SNAKE_CASE__): v for k, v in idalabel.items()}
    __snake_case:						Dict   =						idalabel
    __snake_case:						Tuple   =						{v: k for k, v in idalabel.items()}

  else:
    __snake_case:						Optional[int]   =						1000
    __snake_case:						Optional[Any]   =						"""huggingface/label-files"""
    __snake_case:						Dict   =						"""imagenet-1k-id2label.json"""
    __snake_case:						Tuple   =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					repo_type="""dataset""")					,					"""r"""))
    __snake_case:						Dict   =						{int(SCREAMING_SNAKE_CASE__): v for k, v in idalabel.items()}
    __snake_case:						Dict   =						idalabel
    __snake_case:						Optional[Any]   =						{v: k for k, v in idalabel.items()}

  __snake_case:						Optional[Any]   =						img_size
  __snake_case:						List[str]   =						num_classes
  __snake_case:						Optional[Any]   =						embed_dim
  __snake_case:						Any   =						depths
  __snake_case:						Any   =						num_heads
  __snake_case:						int   =						window_size

  return config







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    List[Any]:
  if "patch_embed.proj" in name:
    __snake_case:						str   =						name.replace("""patch_embed.proj"""					,					"""embeddings.patch_embeddings.projection""")
  if "patch_embed.norm" in name:
    __snake_case:						Union[str, Any]   =						name.replace("""patch_embed.norm"""					,					"""embeddings.norm""")
  if "layers" in name:
    __snake_case:						int   =						"""encoder.""" + name
  if "attn.proj" in name:
    __snake_case:						Union[str, Any]   =						name.replace("""attn.proj"""					,					"""attention.output.dense""")
  if "attn" in name:
    __snake_case:						int   =						name.replace("""attn"""					,					"""attention.self""")
  if "norm1" in name:
    __snake_case:						Optional[Any]   =						name.replace("""norm1"""					,					"""layernorm_before""")
  if "norm2" in name:
    __snake_case:						Optional[Any]   =						name.replace("""norm2"""					,					"""layernorm_after""")
  if "mlp.fc1" in name:
    __snake_case:						Any   =						name.replace("""mlp.fc1"""					,					"""intermediate.dense""")
  if "mlp.fc2" in name:
    __snake_case:						Optional[Any]   =						name.replace("""mlp.fc2"""					,					"""output.dense""")
  if "q_bias" in name:
    __snake_case:						int   =						name.replace("""q_bias"""					,					"""query.bias""")
  if "k_bias" in name:
    __snake_case:						List[Any]   =						name.replace("""k_bias"""					,					"""key.bias""")
  if "v_bias" in name:
    __snake_case:						Optional[Any]   =						name.replace("""v_bias"""					,					"""value.bias""")
  if "cpb_mlp" in name:
    __snake_case:						Optional[Any]   =						name.replace("""cpb_mlp"""					,					"""continuous_position_bias_mlp""")
  if name == "norm.weight":
    __snake_case:						Optional[int]   =						"""layernorm.weight"""
  if name == "norm.bias":
    __snake_case:						Union[str, Any]   =						"""layernorm.bias"""

  if "head" in name:
    __snake_case:						Optional[int]   =						name.replace("""head"""					,					"""classifier""")
  else:
    __snake_case:						List[str]   =						"""swinv2.""" + name

  return name







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Tuple:
  for key in orig_state_dict.copy().keys():
    __snake_case:						List[Any]   =						orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

    if "mask" in key:
      continue
    elif "qkv" in key:
      __snake_case:						Tuple   =						key.split(""".""")
      __snake_case:						Union[str, Any]   =						int(key_split[1])
      __snake_case:						List[Any]   =						int(key_split[3])
      __snake_case:						Union[str, Any]   =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __snake_case:						Any   =						val[:dim, :]
        __snake_case:						Tuple   =						val[dim : dim * 2, :]
        __snake_case:						List[Any]   =						val[-dim:, :]
      else:
        __snake_case:						int   =						val[:dim]
        __snake_case:						List[str]   =						val[
            dim : dim * 2
        ]
        __snake_case:						Optional[int]   =						val[-dim:]
    else:
      __snake_case:						List[Any]   =						val

  return orig_state_dict







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[Any]:
  __snake_case:						Optional[Any]   =						timm.create_model(SCREAMING_SNAKE_CASE__					,					pretrained=SCREAMING_SNAKE_CASE__)
  timm_model.eval()

  __snake_case:						Any   =						get_swinva_config(SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[int]   =						SwinvaForImageClassification(SCREAMING_SNAKE_CASE__)
  model.eval()

  __snake_case:						Optional[Any]   =						convert_state_dict(timm_model.state_dict()					,					SCREAMING_SNAKE_CASE__)
  model.load_state_dict(SCREAMING_SNAKE_CASE__)

  __snake_case:						List[Any]   =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

  __snake_case:						Dict   =						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""					,					"""-""")))
  __snake_case:						int   =						Image.open(requests.get(SCREAMING_SNAKE_CASE__					,					stream=SCREAMING_SNAKE_CASE__).raw)
  __snake_case:						int   =						image_processor(images=SCREAMING_SNAKE_CASE__					,					return_tensors="""pt""")

  __snake_case:						Union[str, Any]   =						timm_model(inputs["""pixel_values"""])
  __snake_case:						Optional[Any]   =						model(**SCREAMING_SNAKE_CASE__).logits

  assert torch.allclose(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					atol=1e-3)

  print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}''')
  model.save_pretrained(SCREAMING_SNAKE_CASE__)

  print(F'''Saving image processor to {pytorch_dump_folder_path}''')
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE__)

  model.push_to_hub(
      repo_path_or_name=Path(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)					,					organization="""nandwalritik"""					,					commit_message="""Add model"""					,					)


if __name__ == "__main__":
      __UpperCAmelCase       :   Dict				       =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--swinv2_name",
          default="swinv2_tiny_patch4_window8_256",
          type=str,
          help="Name of the Swinv2 timm model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )

      __UpperCAmelCase       :   str				       =							parser.parse_args()
      convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)













__UpperCAmelCase       :   int				       =							[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






