



'''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()
_lowerCamelCase	:       Dict      	=	logging.get_logger(__name__)







def 				__lowerCamelCase				(   A__    )       ->	Union[str, Any]:






   """simple docstring"""
   print('Loading config file...'    )

   def flatten_yaml_as_dict(A__    ,						A__=""    ,						A__="."    ):
      UpperCamelCase					=  []
      for k, v in d.items():
         UpperCamelCase					=  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__    )

   UpperCamelCase					=  argparse.Namespace()
   with open(A__    ,						'r'    ) as yaml_file:
      try:
         UpperCamelCase					=  yaml.load(A__    ,						Loader=yaml.FullLoader    )

         UpperCamelCase					=  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 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  MobileViTVaConfig()

   UpperCamelCase					=  False

   # dataset
   if task_name.startswith('imagenet1k_'    ):
      UpperCamelCase					=  1_000
      if int(task_name.strip().split('_'    )[-1]    ) == 384:
         UpperCamelCase					=  384
      else:
         UpperCamelCase					=  256
      UpperCamelCase					=  'imagenet-1k-id2label.json'
   elif task_name.startswith('imagenet21k_to_1k_'    ):
      UpperCamelCase					=  21_000
      if int(task_name.strip().split('_'    )[-1]    ) == 384:
         UpperCamelCase					=  384
      else:
         UpperCamelCase					=  256
      UpperCamelCase					=  'imagenet-22k-id2label.json'
   elif task_name.startswith('ade20k_'    ):
      UpperCamelCase					=  151
      UpperCamelCase					=  512
      UpperCamelCase					=  'ade20k-id2label.json'
      UpperCamelCase					=  True
   elif task_name.startswith('voc_'    ):
      UpperCamelCase					=  21
      UpperCamelCase					=  512
      UpperCamelCase					=  'pascal-voc-id2label.json'
      UpperCamelCase					=  True

   # orig_config
   UpperCamelCase					=  load_orig_config_file(A__    )
   assert getattr(A__    ,						'model.classification.name'    ,						-1    ) == "mobilevit_v2", "Invalid model"
   UpperCamelCase					=  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"
   UpperCamelCase					=  getattr(A__    ,						'model.classification.activation.name'    ,						'swish'    )
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      UpperCamelCase					=  getattr(A__    ,						'model.segmentation.output_stride'    ,						16    )
      if "_deeplabv3" in task_name:
         UpperCamelCase					=  getattr(A__    ,						'model.segmentation.deeplabv3.aspp_rates'    ,						[12, 24, 36]    )
         UpperCamelCase					=  getattr(A__    ,						'model.segmentation.deeplabv3.aspp_out_channels'    ,						512    )
         UpperCamelCase					=  getattr(A__    ,						'model.segmentation.deeplabv3.aspp_dropout'    ,						0.1    )

    # id2label
   UpperCamelCase					=  'huggingface/label-files'
   UpperCamelCase					=  json.load(open(hf_hub_download(A__    ,						A__    ,						repo_type='dataset'    )    ,						'r'    )    )
   UpperCamelCase					=  {int(A__    ): v for k, v in idalabel.items()}
   UpperCamelCase					=  idalabel
   UpperCamelCase					=  {v: k for k, v in idalabel.items()}

   return config







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  dct.pop(A__    )
   UpperCamelCase					=  val







def 				__lowerCamelCase				(   A__    ,						A__=False    )       ->	Tuple:






   """simple docstring"""
   if base_model:
      UpperCamelCase					=  ''
   else:
      UpperCamelCase					=  'mobilevitv2.'

   UpperCamelCase					=  []
   for k in state_dict.keys():
      if k[:8] == "encoder.":
         UpperCamelCase					=  k[8:]
      else:
         UpperCamelCase					=  k

      if ".block." in k:
         UpperCamelCase					=  k_new.replace('.block.'    ,						'.'    )
      if ".conv." in k:
         UpperCamelCase					=  k_new.replace('.conv.'    ,						'.convolution.'    )
      if ".norm." in k:
         UpperCamelCase					=  k_new.replace('.norm.'    ,						'.normalization.'    )

      if "conv_1." in k:
         UpperCamelCase					=  k_new.replace('conv_1.'    ,						F"""{model_prefix}conv_stem."""    )
      for i in [1, 2]:
         if F"""layer_{i}.""" in k:
            UpperCamelCase					=  k_new.replace(F"""layer_{i}."""    ,						F"""{model_prefix}encoder.layer.{i-1}.layer."""    )
      if ".exp_1x1." in k:
         UpperCamelCase					=  k_new.replace('.exp_1x1.'    ,						'.expand_1x1.'    )
      if ".red_1x1." in k:
         UpperCamelCase					=  k_new.replace('.red_1x1.'    ,						'.reduce_1x1.'    )

      for i in [3, 4, 5]:
         if F"""layer_{i}.0.""" in k:
            UpperCamelCase					=  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:
            UpperCamelCase					=  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:
            UpperCamelCase					=  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:
            UpperCamelCase					=  [0, 1]
         elif i == 4:
            UpperCamelCase					=  [0, 1, 2, 3]
         elif i == 5:
            UpperCamelCase					=  [0, 1, 2]

         for j in j_in:
            if F"""layer_{i}.1.global_rep.{j}.""" in k:
               UpperCamelCase					=  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:
            UpperCamelCase					=  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:
            UpperCamelCase					=  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:
         UpperCamelCase					=  k_new.replace('pre_norm_attn.0.'    ,						'layernorm_before.'    )
      if "pre_norm_attn.1." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_attn.1.'    ,						'attention.'    )
      if "pre_norm_ffn.0." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_ffn.0.'    ,						'layernorm_after.'    )
      if "pre_norm_ffn.1." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_ffn.1.'    ,						'ffn.conv1.'    )
      if "pre_norm_ffn.3." in k:
         UpperCamelCase					=  k_new.replace('pre_norm_ffn.3.'    ,						'ffn.conv2.'    )

      if "classifier.1." in k:
         UpperCamelCase					=  k_new.replace('classifier.1.'    ,						'classifier.'    )

      if "seg_head." in k:
         UpperCamelCase					=  k_new.replace('seg_head.'    ,						'segmentation_head.'    )
      if ".aspp_layer." in k:
         UpperCamelCase					=  k_new.replace('.aspp_layer.'    ,						'.'    )
      if ".aspp_pool." in k:
         UpperCamelCase					=  k_new.replace('.aspp_pool.'    ,						'.'    )

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







def 				__lowerCamelCase				(   A__    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  []
   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 				__lowerCamelCase				(   )       ->	List[Any]:






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







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  get_mobilevitva_config(A__    ,						A__    )

   # load original state_dict
   UpperCamelCase					=  torch.load(A__    ,						map_location='cpu'    )

   # load huggingface model
   if task_name.startswith('ade20k_'    ) or task_name.startswith('voc_'    ):
      UpperCamelCase					=  MobileViTVaForSemanticSegmentation(A__    ).eval()
      UpperCamelCase					=  False
   else:
      UpperCamelCase					=  MobileViTVaForImageClassification(A__    ).eval()
      UpperCamelCase					=  False

   # remove and rename some keys of load the original model
   UpperCamelCase					=  checkpoint
   remove_unused_keys(A__    )
   UpperCamelCase					=  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
   UpperCamelCase					=  MobileViTImageProcessor(crop_size=config.image_size    ,						size=config.image_size + 32    )
   UpperCamelCase					=  image_processor(images=prepare_img()    ,						return_tensors='pt'    )
   UpperCamelCase					=  model(**A__    )

   # verify classification model
   if task_name.startswith('imagenet'    ):
      UpperCamelCase					=  outputs.logits
      UpperCamelCase					=  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
         UpperCamelCase					=  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__":
   _lowerCamelCase	:       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 . "
           "\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."
   )

   _lowerCamelCase	:       Tuple      	=	parser.parse_args()
   convert_mobilevitva_checkpoint(
       args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
   )



from __future__ import annotations

from collections.abc import Iterator




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Dict			,							UpperCamelCase__			: int    )			->	None:







    """simple docstring"""






    __magic_name__				     =       value
    __magic_name__				     =       None
    __magic_name__				     =       None







class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Union[str, Any]			,							UpperCamelCase__			: Node    )			->	None:







    """simple docstring"""






    __magic_name__				     =       tree
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: Node | None    )			->	int:







    """simple docstring"""






    if node is None:
      return 0
    return node.value + (
        self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
    )





  def __iter__( self			: int    )			->	Iterator[int]:







    """simple docstring"""






    yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

