
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[Any]					=							logging.get_logger(__name__)
def 				a__   (   a   :				Optional[Any]		):

  """simple docstring"""







  print("Loading config file..."		)

  def flatten_yaml_as_dict(a   :				Tuple       ,     a   :				Union[str, Any]=""       ,     a   :				Dict="."		):
    _snake_case     :						Tuple						=   []
    for k, v in d.items():
      _snake_case     :						List[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		)

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

      _snake_case     :						List[str]						=   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 				a__   (   a   :				Any       ,     a   :				Dict		):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   MobileViTVaConfig()

  _snake_case     :						Union[str, Any]						=   False

  # dataset
  if task_name.startswith("imagenet1k_"		):
    _snake_case     :						Union[str, Any]						=   1_000
    if int(task_name.strip().split("_"		)[-1]		) == 384:
      _snake_case     :						Dict						=   384
    else:
      _snake_case     :						Any						=   256
    _snake_case     :						List[str]						=   "imagenet-1k-id2label.json"
  elif task_name.startswith("imagenet21k_to_1k_"		):
    _snake_case     :						Any						=   21_000
    if int(task_name.strip().split("_"		)[-1]		) == 384:
      _snake_case     :						Tuple						=   384
    else:
      _snake_case     :						str						=   256
    _snake_case     :						str						=   "imagenet-22k-id2label.json"
  elif task_name.startswith("ade20k_"		):
    _snake_case     :						Optional[Any]						=   151
    _snake_case     :						List[str]						=   512
    _snake_case     :						List[Any]						=   "ade20k-id2label.json"
    _snake_case     :						Optional[Any]						=   True
  elif task_name.startswith("voc_"		):
    _snake_case     :						List[str]						=   21
    _snake_case     :						Dict						=   512
    _snake_case     :						str						=   "pascal-voc-id2label.json"
    _snake_case     :						List[Any]						=   True

  # orig_config
  _snake_case     :						List[str]						=   load_orig_config_file(a		)
  assert getattr(a       ,     "model.classification.name"       ,     -1		) == "mobilevit_v2", "Invalid model"
  _snake_case     :						int						=   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"
  _snake_case     :						Any						=   getattr(a       ,     "model.classification.activation.name"       ,     "swish"		)
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

  if is_segmentation_model:
    _snake_case     :						Optional[Any]						=   getattr(a       ,     "model.segmentation.output_stride"       ,     16		)
    if "_deeplabv3" in task_name:
      _snake_case     :						Optional[Any]						=   getattr(a       ,     "model.segmentation.deeplabv3.aspp_rates"       ,     [12, 24, 36]		)
      _snake_case     :						Tuple						=   getattr(a       ,     "model.segmentation.deeplabv3.aspp_out_channels"       ,     512		)
      _snake_case     :						List[Any]						=   getattr(a       ,     "model.segmentation.deeplabv3.aspp_dropout"       ,     0.1		)

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

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

  """simple docstring"""







  _snake_case     :						int						=   dct.pop(a		)
  _snake_case     :						Tuple						=   val
def 				a__   (   a   :				Tuple       ,     a   :				int=False		):

  """simple docstring"""







  if base_model:
    _snake_case     :						List[Any]						=   ""
  else:
    _snake_case     :						Optional[int]						=   "mobilevitv2."

  _snake_case     :						Tuple						=   []
  for k in state_dict.keys():
    if k[:8] == "encoder.":
      _snake_case     :						List[Any]						=   k[8:]
    else:
      _snake_case     :						Tuple						=   k

    if ".block." in k:
      _snake_case     :						Optional[int]						=   k_new.replace(".block."       ,     "."		)
    if ".conv." in k:
      _snake_case     :						Dict						=   k_new.replace(".conv."       ,     ".convolution."		)
    if ".norm." in k:
      _snake_case     :						Union[str, Any]						=   k_new.replace(".norm."       ,     ".normalization."		)

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

    for i in [3, 4, 5]:
      if f'layer_{i}.0.' in k:
        _snake_case     :						List[Any]						=   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:
        _snake_case     :						Optional[int]						=   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:
        _snake_case     :						List[str]						=   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:
        _snake_case     :						Tuple						=   [0, 1]
      elif i == 4:
        _snake_case     :						str						=   [0, 1, 2, 3]
      elif i == 5:
        _snake_case     :						Optional[Any]						=   [0, 1, 2]

      for j in j_in:
        if f'layer_{i}.1.global_rep.{j}.' in k:
          _snake_case     :						Tuple						=   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:
        _snake_case     :						int						=   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:
        _snake_case     :						List[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:
      _snake_case     :						Any						=   k_new.replace("pre_norm_attn.0."       ,     "layernorm_before."		)
    if "pre_norm_attn.1." in k:
      _snake_case     :						Union[str, Any]						=   k_new.replace("pre_norm_attn.1."       ,     "attention."		)
    if "pre_norm_ffn.0." in k:
      _snake_case     :						str						=   k_new.replace("pre_norm_ffn.0."       ,     "layernorm_after."		)
    if "pre_norm_ffn.1." in k:
      _snake_case     :						str						=   k_new.replace("pre_norm_ffn.1."       ,     "ffn.conv1."		)
    if "pre_norm_ffn.3." in k:
      _snake_case     :						Tuple						=   k_new.replace("pre_norm_ffn.3."       ,     "ffn.conv2."		)

    if "classifier.1." in k:
      _snake_case     :						Union[str, Any]						=   k_new.replace("classifier.1."       ,     "classifier."		)

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

    rename_keys.append((k, k_new)		)
  return rename_keys
def 				a__   (   a   :				int		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   []
  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 				a__   (   ):

  """simple docstring"""







  _snake_case     :						List[Any]						=   "http://images.cocodataset.org/val2017/000000039769.jpg"
  # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
  _snake_case     :						List[Any]						=   Image.open(requests.get(a       ,     stream=a		).raw		)
  return im
@torch.no_grad()
def 				a__   (   a   :				Dict       ,     a   :				Optional[int]       ,     a   :				Any       ,     a   :				List[str]		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   get_mobilevitva_config(a       ,     a		)

  # load original state_dict
  _snake_case     :						List[str]						=   torch.load(a       ,     map_location="cpu"		)

  # load huggingface model
  if task_name.startswith("ade20k_"		) or task_name.startswith("voc_"		):
    _snake_case     :						Any						=   MobileViTVaForSemanticSegmentation(a		).eval()
    _snake_case     :						List[str]						=   False
  else:
    _snake_case     :						Any						=   MobileViTVaForImageClassification(a		).eval()
    _snake_case     :						Dict						=   False

  # remove and rename some keys of load the original model
  _snake_case     :						int						=   checkpoint
  remove_unused_keys(a		)
  _snake_case     :						Dict						=   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
  _snake_case     :						int						=   MobileViTImageProcessor(crop_size=config.image_size       ,     size=config.image_size + 32		)
  _snake_case     :						str						=   image_processor(images=prepare_img()       ,     return_tensors="pt"		)
  _snake_case     :						Tuple						=   model(**a		)

  # verify classification model
  if task_name.startswith("imagenet"		):
    _snake_case     :						List[str]						=   outputs.logits
    _snake_case     :						Optional[int]						=   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
      _snake_case     :						List[Any]						=   torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]		)
      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      :			List[str]					=							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 . """
            """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
        ),
        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      :			Any					=							parser.parse_args()
    convert_mobilevitva_checkpoint(
        args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
    )



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_a      :			Optional[int]					=							logging.get_logger(__name__)

_a      :			List[str]					=							{
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}






class      _UpperCAmelCase				(					_snake_case		,					_snake_case):
    __lowercase							: List[Any]  =					"""convnextv2"""
    def __init__(    self						,    snake_case_=3						,    snake_case_=4						,    snake_case_=4						,    snake_case_=None						,    snake_case_=None						,    snake_case_="gelu"						,    snake_case_=0.02						,    snake_case_=1E-12						,    snake_case_=0.0						,    snake_case_=2_24						,    snake_case_=None						,    snake_case_=None						,    **snake_case_						,    ):
      super().__init__(**snake_case_     )

      _snake_case     :						Tuple						=   num_channels
      _snake_case     :						Optional[int]						=   patch_size
      _snake_case     :						Tuple						=   num_stages
      _snake_case     :						int						=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
      _snake_case     :						str						=   [3, 3, 9, 3] if depths is None else depths
      _snake_case     :						int						=   hidden_act
      _snake_case     :						Tuple						=   initializer_range
      _snake_case     :						Union[str, Any]						=   layer_norm_eps
      _snake_case     :						Optional[int]						=   drop_path_rate
      _snake_case     :						Union[str, Any]						=   image_size
      _snake_case     :						List[Any]						=   ["stem"] + [F'stage{idx}' for idx in range(1						,    len(self.depths     ) + 1     )]
      _snake_case			,					_snake_case     :						Dict						=   get_aligned_output_features_output_indices(
          out_features=snake_case_						,    out_indices=snake_case_						,    stage_names=self.stage_names     )

