


'''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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Optional[int] ):
  '''simple docstring'''





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

  lowerCAmelCase_      :				Tuple      =       """std_conv""" if """bit""" in model_name else False

  # note that when using BiT as backbone for ViT-hybrid checkpoints,
  # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
  # config.conv_layer = "std_conv_same"
  lowerCAmelCase_      :				Optional[int]      =       BitConfig(
      conv_layer=A__							,   num_labels=10_00							,   idalabel=A__							,   labelaid=A__							,   )

  return config







def     UpperCamelCase_      (   A__ :       Optional[Any] ):
  '''simple docstring'''





  if "stem.conv" in name:
    lowerCAmelCase_      :				str      =       name.replace("""stem.conv"""							,   """bit.embedder.convolution""" )
  if "blocks" in name:
    lowerCAmelCase_      :				List[Any]      =       name.replace("""blocks"""							,   """layers""" )
  if "head.fc" in name:
    lowerCAmelCase_      :				int      =       name.replace("""head.fc"""							,   """classifier.1""" )
  if name.startswith("""norm""" ):
    lowerCAmelCase_      :				str      =       """bit.""" + name
  if "bit" not in name and "classifier" not in name:
    lowerCAmelCase_      :				List[str]      =       """bit.encoder.""" + name

  return name







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				str      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       str							,   A__ :       List[Any]=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       get_config(A__ )

  # load original model from timm
  lowerCAmelCase_      :				Any      =       create_model(A__							,   pretrained=A__ )
  timm_model.eval()

  # load state_dict of original model
  lowerCAmelCase_      :				Dict      =       timm_model.state_dict()
  for key in state_dict.copy().keys():
    lowerCAmelCase_      :				Tuple      =       state_dict.pop(A__ )
    lowerCAmelCase_      :				Optional[int]      =       val.squeeze() if """head""" in key else val

  # load HuggingFace model
  lowerCAmelCase_      :				List[str]      =       BitForImageClassification(A__ )
  model.eval()
  model.load_state_dict(A__ )

  # create image processor
  lowerCAmelCase_      :				Union[str, Any]      =       create_transform(**resolve_data_config({}							,   model=A__ ) )
  lowerCAmelCase_      :				List[Any]      =       transform.transforms

  lowerCAmelCase_      :				Optional[int]      =       {
      """bilinear""": PILImageResampling.BILINEAR,
      """bicubic""": PILImageResampling.BICUBIC,
      """nearest""": PILImageResampling.NEAREST,
  }

  lowerCAmelCase_      :				Any      =       BitImageProcessor(
      do_resize=A__							,   size={"""shortest_edge""": timm_transforms[0].size}							,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]							,   do_center_crop=A__							,   crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}							,   do_normalize=A__							,   image_mean=timm_transforms[-1].mean.tolist()							,   image_std=timm_transforms[-1].std.tolist()							,   )

  lowerCAmelCase_      :				Optional[Any]      =       prepare_img()
  lowerCAmelCase_      :				Union[str, Any]      =       transform(A__ ).unsqueeze(0 )
  lowerCAmelCase_      :				Optional[Any]      =       processor(A__							,   return_tensors="""pt""" ).pixel_values

  # verify pixel values
  assert torch.allclose(A__							,   A__ )

  # verify logits
  with torch.no_grad():
    lowerCAmelCase_      :				int      =       model(A__ )
    lowerCAmelCase_      :				Dict      =       outputs.logits

  print("""Logits:"""							,   logits[0, :3] )
  print("""Predicted class:"""							,   model.config.idalabel[logits.argmax(-1 ).item()] )
  lowerCAmelCase_      :				List[Any]      =       timm_model(A__ )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(A__							,   outputs.logits							,   atol=1E-3 )
  print("""Looks ok!""" )

  if pytorch_dump_folder_path is not None:
    Path(A__ ).mkdir(exist_ok=A__ )
    print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}' )
    model.save_pretrained(A__ )
    processor.save_pretrained(A__ )

  if push_to_hub:
    print(f'Pushing model {model_name} and processor to the hub' )
    model.push_to_hub(f'ybelkada/{model_name}' )
    processor.push_to_hub(f'ybelkada/{model_name}' )


if __name__ == "__main__":
  __A :  str    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="resnetv2_50x1_bitm",
      type=str,
      help="Name of the BiT 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."
  )
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
      help="Whether to push the model to the hub.",
  )

  __A :  Optional[Any]    							=				parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''






import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       List[str] ):
  '''simple docstring'''





  print("""Loading config file...""" )

  def flatten_yaml_as_dict(A__ :       int							,   A__ :       str=""							,   A__ :       Dict="." ):
    lowerCAmelCase_      :				int      =       []
    for k, v in d.items():
      lowerCAmelCase_      :				Any      =       parent_key + sep + k if parent_key else k
      if isinstance(A__							,   collections.abc.MutableMapping ):
        items.extend(flatten_yaml_as_dict(A__							,   A__							,   sep=A__ ).items() )
      else:
        items.append((new_key, v) )
    return dict(A__ )

  lowerCAmelCase_      :				Optional[int]      =       argparse.Namespace()
  with open(A__							,   """r""" ) as yaml_file:
    try:
      lowerCAmelCase_      :				Any      =       yaml.load(A__							,   Loader=yaml.FullLoader )

      lowerCAmelCase_      :				Any      =       flatten_yaml_as_dict(A__ )
      for k, v in flat_cfg.items():
        setattr(A__							,   A__							,   A__ )
    except yaml.YAMLError as exc:
      logger.error("""Error while loading config file: {}. Error message: {}""".format(A__							,   str(A__ ) ) )
  return config







def     UpperCamelCase_      (   A__ :       Optional[int]							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       MobileViTVaConfig()

  lowerCAmelCase_      :				Any      =       False

  # dataset
  if task_name.startswith("""imagenet1k_""" ):
    lowerCAmelCase_      :				Tuple      =       10_00
    if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
      lowerCAmelCase_      :				List[str]      =       3_84
    else:
      lowerCAmelCase_      :				Optional[int]      =       2_56
    lowerCAmelCase_      :				int      =       """imagenet-1k-id2label.json"""
  elif task_name.startswith("""imagenet21k_to_1k_""" ):
    lowerCAmelCase_      :				int      =       2_10_00
    if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
      lowerCAmelCase_      :				int      =       3_84
    else:
      lowerCAmelCase_      :				int      =       2_56
    lowerCAmelCase_      :				Optional[Any]      =       """imagenet-22k-id2label.json"""
  elif task_name.startswith("""ade20k_""" ):
    lowerCAmelCase_      :				Dict      =       1_51
    lowerCAmelCase_      :				Any      =       5_12
    lowerCAmelCase_      :				int      =       """ade20k-id2label.json"""
    lowerCAmelCase_      :				Union[str, Any]      =       True
  elif task_name.startswith("""voc_""" ):
    lowerCAmelCase_      :				Any      =       21
    lowerCAmelCase_      :				List[str]      =       5_12
    lowerCAmelCase_      :				Union[str, Any]      =       """pascal-voc-id2label.json"""
    lowerCAmelCase_      :				int      =       True

  # orig_config
  lowerCAmelCase_      :				Optional[int]      =       load_orig_config_file(A__ )
  assert getattr(A__							,   """model.classification.name"""							,   -1 ) == "mobilevit_v2", "Invalid model"
  lowerCAmelCase_      :				Union[str, Any]      =       getattr(A__							,   """model.classification.mitv2.width_multiplier"""							,   1.0 )
  assert (
      getattr(A__							,   """model.classification.mitv2.attn_norm_layer"""							,   -1 ) == "layer_norm_2d"
  ), "Norm layers other than layer_norm_2d is not supported"
  lowerCAmelCase_      :				List[Any]      =       getattr(A__							,   """model.classification.activation.name"""							,   """swish""" )
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

  if is_segmentation_model:
    lowerCAmelCase_      :				List[str]      =       getattr(A__							,   """model.segmentation.output_stride"""							,   16 )
    if "_deeplabv3" in task_name:
      lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_rates"""							,   [12, 24, 36] )
      lowerCAmelCase_      :				Optional[Any]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_out_channels"""							,   5_12 )
      lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_dropout"""							,   0.1 )

    # id2label
  lowerCAmelCase_      :				Any      =       """huggingface/label-files"""
  lowerCAmelCase_      :				Any      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
  lowerCAmelCase_      :				Any      =       {int(A__ ): v for k, v in idalabel.items()}
  lowerCAmelCase_      :				Optional[int]      =       idalabel
  lowerCAmelCase_      :				Tuple      =       {v: k for k, v in idalabel.items()}

  return config







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Dict							,   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       dct.pop(A__ )
  lowerCAmelCase_      :				Optional[int]      =       val







def     UpperCamelCase_      (   A__ :       int							,   A__ :       int=False ):
  '''simple docstring'''





  if base_model:
    lowerCAmelCase_      :				List[Any]      =       """"""
  else:
    lowerCAmelCase_      :				Any      =       """mobilevitv2."""

  lowerCAmelCase_      :				int      =       []
  for k in state_dict.keys():
    if k[:8] == "encoder.":
      lowerCAmelCase_      :				List[Any]      =       k[8:]
    else:
      lowerCAmelCase_      :				Optional[Any]      =       k

    if ".block." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace(""".block."""							,   """.""" )
    if ".conv." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace(""".conv."""							,   """.convolution.""" )
    if ".norm." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace(""".norm."""							,   """.normalization.""" )

    if "conv_1." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace("""conv_1."""							,   f'{model_prefix}conv_stem.' )
    for i in [1, 2]:
      if f'layer_{i}.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(f'layer_{i}.'							,   f'{model_prefix}encoder.layer.{i-1}.layer.' )
    if ".exp_1x1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace(""".exp_1x1."""							,   """.expand_1x1.""" )
    if ".red_1x1." in k:
      lowerCAmelCase_      :				List[str]      =       k_new.replace(""".red_1x1."""							,   """.reduce_1x1.""" )

    for i in [3, 4, 5]:
      if f'layer_{i}.0.' in k:
        lowerCAmelCase_      :				Optional[int]      =       k_new.replace(f'layer_{i}.0.'							,   f'{model_prefix}encoder.layer.{i-1}.downsampling_layer.' )
      if f'layer_{i}.1.local_rep.0.' in k:
        lowerCAmelCase_      :				str      =       k_new.replace(f'layer_{i}.1.local_rep.0.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_kxk.' )
      if f'layer_{i}.1.local_rep.1.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(f'layer_{i}.1.local_rep.1.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_1x1.' )

    for i in [3, 4, 5]:
      if i == 3:
        lowerCAmelCase_      :				Optional[Any]      =       [0, 1]
      elif i == 4:
        lowerCAmelCase_      :				Any      =       [0, 1, 2, 3]
      elif i == 5:
        lowerCAmelCase_      :				Any      =       [0, 1, 2]

      for j in j_in:
        if f'layer_{i}.1.global_rep.{j}.' in k:
          lowerCAmelCase_      :				Optional[int]      =       k_new.replace(
              f'layer_{i}.1.global_rep.{j}.'							,   f'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.' )
      if f'layer_{i}.1.global_rep.{j+1}.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(
            f'layer_{i}.1.global_rep.{j+1}.'							,   f'{model_prefix}encoder.layer.{i-1}.layernorm.' )

      if f'layer_{i}.1.conv_proj.' in k:
        lowerCAmelCase_      :				str      =       k_new.replace(f'layer_{i}.1.conv_proj.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_projection.' )

    if "pre_norm_attn.0." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace("""pre_norm_attn.0."""							,   """layernorm_before.""" )
    if "pre_norm_attn.1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace("""pre_norm_attn.1."""							,   """attention.""" )
    if "pre_norm_ffn.0." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace("""pre_norm_ffn.0."""							,   """layernorm_after.""" )
    if "pre_norm_ffn.1." in k:
      lowerCAmelCase_      :				int      =       k_new.replace("""pre_norm_ffn.1."""							,   """ffn.conv1.""" )
    if "pre_norm_ffn.3." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace("""pre_norm_ffn.3."""							,   """ffn.conv2.""" )

    if "classifier.1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace("""classifier.1."""							,   """classifier.""" )

    if "seg_head." in k:
      lowerCAmelCase_      :				Optional[Any]      =       k_new.replace("""seg_head."""							,   """segmentation_head.""" )
    if ".aspp_layer." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace(""".aspp_layer."""							,   """.""" )
    if ".aspp_pool." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace(""".aspp_pool."""							,   """.""" )

    rename_keys.append((k, k_new) )
  return rename_keys







def     UpperCamelCase_      (   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       []
  for k in state_dict.keys():
    if k.startswith("""seg_head.aux_head.""" ):
      keys_to_ignore.append(A__ )
  for k in keys_to_ignore:
    state_dict.pop(A__							,   A__ )







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
  lowerCAmelCase_      :				Optional[Any]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Union[str, Any]							,   A__ :       str							,   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       get_mobilevitva_config(A__							,   A__ )

  # load original state_dict
  lowerCAmelCase_      :				int      =       torch.load(A__							,   map_location="""cpu""" )

  # load huggingface model
  if task_name.startswith("""ade20k_""" ) or task_name.startswith("""voc_""" ):
    lowerCAmelCase_      :				Union[str, Any]      =       MobileViTVaForSemanticSegmentation(A__ ).eval()
    lowerCAmelCase_      :				Union[str, Any]      =       False
  else:
    lowerCAmelCase_      :				List[Any]      =       MobileViTVaForImageClassification(A__ ).eval()
    lowerCAmelCase_      :				Optional[int]      =       False

  # remove and rename some keys of load the original model
  lowerCAmelCase_      :				Tuple      =       checkpoint
  remove_unused_keys(A__ )
  lowerCAmelCase_      :				List[Any]      =       create_rename_keys(A__							,   base_model=A__ )
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(A__							,   A__							,   A__ )

  # load modified state_dict
  model.load_state_dict(A__ )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  lowerCAmelCase_      :				Dict      =       MobileViTImageProcessor(crop_size=config.image_size							,   size=config.image_size + 32 )
  lowerCAmelCase_      :				Dict      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				int      =       model(**A__ )

  # verify classification model
  if task_name.startswith("""imagenet""" ):
    lowerCAmelCase_      :				Optional[Any]      =       outputs.logits
    lowerCAmelCase_      :				Tuple      =       logits.argmax(-1 ).item()
    print("""Predicted class:"""							,   model.config.idalabel[predicted_class_idx] )
    if task_name.startswith("""imagenet1k_256""" ) and config.width_multiplier == 1.0:
      # expected_logits for base variant
      lowerCAmelCase_      :				int      =       torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01] )
      assert torch.allclose(logits[0, :3]							,   A__							,   atol=1E-4 )

  Path(A__ ).mkdir(exist_ok=A__ )
  print(f'Saving model {task_name} to {pytorch_dump_folder_path}' )
  model.save_pretrained(A__ )
  print(f'Saving image processor to {pytorch_dump_folder_path}' )
  image_processor.save_pretrained(A__ )


if __name__ == "__main__":
  __A :  Dict    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--task",
      default="imagenet1k_256",
      type=str,
      help=(
          "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
          "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
      ),
      choices=[
          "imagenet1k_256",
          "imagenet1k_384",
          "imagenet21k_to_1k_256",
          "imagenet21k_to_1k_384",
          "ade20k_deeplabv3",
          "voc_deeplabv3",
      ],
  )

  parser.add_argument(
      "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )

  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )



