
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__   (   a   :				Any		):

  """simple docstring"""







  _snake_case     :						int						=   SwinvaConfig()
  _snake_case     :						Union[str, Any]						=   swinva_name.split("_"		)

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

  if model_size == "tiny":
    _snake_case     :						str						=   96
    _snake_case     :						Dict						=   (2, 2, 6, 2)
    _snake_case     :						int						=   (3, 6, 12, 24)
  elif model_size == "small":
    _snake_case     :						Tuple						=   96
    _snake_case     :						Optional[Any]						=   (2, 2, 18, 2)
    _snake_case     :						Optional[int]						=   (3, 6, 12, 24)
  elif model_size == "base":
    _snake_case     :						Dict						=   128
    _snake_case     :						str						=   (2, 2, 18, 2)
    _snake_case     :						List[Any]						=   (4, 8, 16, 32)
  else:
    _snake_case     :						Tuple						=   192
    _snake_case     :						Tuple						=   (2, 2, 18, 2)
    _snake_case     :						Optional[Any]						=   (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     :						str						=   21_841
    _snake_case     :						str						=   """huggingface/label-files"""
    _snake_case     :						List[str]						=   """imagenet-22k-id2label.json"""
    _snake_case     :						Union[str, Any]						=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     repo_type="dataset"		)       ,     "r"		)		)
    _snake_case     :						Optional[int]						=   {int(SCREAMING_SNAKE_CASE__		): v for k, v in idalabel.items()}
    _snake_case     :						int						=   idalabel
    _snake_case     :						Optional[Any]						=   {v: k for k, v in idalabel.items()}

  else:
    _snake_case     :						int						=   1_000
    _snake_case     :						List[Any]						=   """huggingface/label-files"""
    _snake_case     :						List[Any]						=   """imagenet-1k-id2label.json"""
    _snake_case     :						Union[str, Any]						=   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     :						Optional[int]						=   {v: k for k, v in idalabel.items()}

  _snake_case     :						Dict						=   img_size
  _snake_case     :						int						=   num_classes
  _snake_case     :						str						=   embed_dim
  _snake_case     :						Dict						=   depths
  _snake_case     :						int						=   num_heads
  _snake_case     :						Optional[Any]						=   window_size

  return config
def 				a__   (   a   :				Any		):

  """simple docstring"""







  if "patch_embed.proj" in name:
    _snake_case     :						Union[str, Any]						=   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     :						List[str]						=   """encoder.""" + name
  if "attn.proj" in name:
    _snake_case     :						Dict						=   name.replace("attn.proj"       ,     "attention.output.dense"		)
  if "attn" in name:
    _snake_case     :						Dict						=   name.replace("attn"       ,     "attention.self"		)
  if "norm1" in name:
    _snake_case     :						List[Any]						=   name.replace("norm1"       ,     "layernorm_before"		)
  if "norm2" in name:
    _snake_case     :						Dict						=   name.replace("norm2"       ,     "layernorm_after"		)
  if "mlp.fc1" in name:
    _snake_case     :						int						=   name.replace("mlp.fc1"       ,     "intermediate.dense"		)
  if "mlp.fc2" in name:
    _snake_case     :						List[Any]						=   name.replace("mlp.fc2"       ,     "output.dense"		)
  if "q_bias" in name:
    _snake_case     :						Optional[Any]						=   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     :						int						=   name.replace("v_bias"       ,     "value.bias"		)
  if "cpb_mlp" in name:
    _snake_case     :						str						=   name.replace("cpb_mlp"       ,     "continuous_position_bias_mlp"		)
  if name == "norm.weight":
    _snake_case     :						Tuple						=   """layernorm.weight"""
  if name == "norm.bias":
    _snake_case     :						int						=   """layernorm.bias"""

  if "head" in name:
    _snake_case     :						Union[str, Any]						=   name.replace("head"       ,     "classifier"		)
  else:
    _snake_case     :						Optional[Any]						=   """swinv2.""" + name

  return name
def 				a__   (   a   :				Union[str, Any]       ,     a   :				Union[str, Any]		):

  """simple docstring"""







  for key in orig_state_dict.copy().keys():
    _snake_case     :						Tuple						=   orig_state_dict.pop(SCREAMING_SNAKE_CASE__		)

    if "mask" in key:
      continue
    elif "qkv" in key:
      _snake_case     :						List[Any]						=   key.split("."		)
      _snake_case     :						str						=   int(key_split[1]		)
      _snake_case     :						Optional[Any]						=   int(key_split[3]		)
      _snake_case     :						List[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     :						Optional[Any]						=   val[dim : dim * 2, :]
        _snake_case     :						int						=   val[-dim:, :]
      else:
        _snake_case     :						List[str]						=   val[:dim]
        _snake_case     :						Tuple						=   val[
            dim : dim * 2
        ]
        _snake_case     :						Union[str, Any]						=   val[-dim:]
    else:
      _snake_case     :						int						=   val

  return orig_state_dict
def 				a__   (   a   :				Tuple       ,     a   :				int		):

  """simple docstring"""







  _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     :						List[str]						=   SwinvaForImageClassification(SCREAMING_SNAKE_CASE__		)
  model.eval()

  _snake_case     :						Any						=   convert_state_dict(timm_model.state_dict()       ,     SCREAMING_SNAKE_CASE__		)
  model.load_state_dict(SCREAMING_SNAKE_CASE__		)

  _snake_case     :						Any						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""

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

  _snake_case     :						Optional[int]						=   timm_model(inputs["pixel_values"]		)
  _snake_case     :						List[str]						=   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__":
    _a      :			Any					=							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."""
    )

    _a      :			Dict					=							parser.parse_args()
    convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)



"""simple docstring"""
from typing import TYPE_CHECKING

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


_a      :			str					=							{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Optional[Any]					=							["""YolosFeatureExtractor"""]
    _a      :			List[Any]					=							["""YolosImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Union[str, Any]					=							[
        """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """YolosForObjectDetection""",
        """YolosModel""",
        """YolosPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_yolos import YolosFeatureExtractor
        from .image_processing_yolos import YolosImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_yolos import (
            YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
            YolosForObjectDetection,
            YolosModel,
            YolosPreTrainedModel,
        )


else:
    import sys

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

