





'''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()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any		)		->  str:
   print("""Loading config file..."""		)

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

   snake_case 						=   argparse.Namespace()
   with open(__lowerCAmelCase      ,    """r"""		) as yaml_file:
      try:
         snake_case 						=   yaml.load(__lowerCAmelCase      ,    Loader=yaml.FullLoader		)

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							int		)		->  Union[str, Any]:
   snake_case 						=   MobileViTVaConfig()

   snake_case 						=   False

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

   # orig_config
   snake_case 						=   load_orig_config_file(__lowerCAmelCase		)
   assert getattr(__lowerCAmelCase      ,    """model.classification.name"""      ,    -1		) == "mobilevit_v2", "Invalid model"
   snake_case 						=   getattr(__lowerCAmelCase      ,    """model.classification.mitv2.width_multiplier"""      ,    1.0		)
   assert (
       getattr(__lowerCAmelCase      ,    """model.classification.mitv2.attn_norm_layer"""      ,    -1		) == "layer_norm_2d"
   ), "Norm layers other than layer_norm_2d is not supported"
   snake_case 						=   getattr(__lowerCAmelCase      ,    """model.classification.activation.name"""      ,    """swish"""		)
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      snake_case 						=   getattr(__lowerCAmelCase      ,    """model.segmentation.output_stride"""      ,    16		)
      if "_deeplabv3" in task_name:
         snake_case 						=   getattr(__lowerCAmelCase      ,    """model.segmentation.deeplabv3.aspp_rates"""      ,    [12, 24, 36]		)
         snake_case 						=   getattr(__lowerCAmelCase      ,    """model.segmentation.deeplabv3.aspp_out_channels"""      ,    5_12		)
         snake_case 						=   getattr(__lowerCAmelCase      ,    """model.segmentation.deeplabv3.aspp_dropout"""      ,    0.1		)

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

   return config





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							Optional[int]      ,    __lowerCAmelCase       :							Optional[Any]		)		->  List[Any]:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							Any=False		)		->  Union[str, Any]:
   if base_model:
      snake_case 						=   """"""
   else:
      snake_case 						=   """mobilevitv2."""

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

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

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

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

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

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

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

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]		)		->  Dict:
   snake_case 						=   []
   for k in state_dict.keys():
      if k.startswith("""seg_head.aux_head."""		):
         keys_to_ignore.append(__lowerCAmelCase		)
   for k in keys_to_ignore:
      state_dict.pop(__lowerCAmelCase      ,    __lowerCAmelCase		)





def 	__lowerCamelCase			(      )		->  Tuple:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							List[str]		)		->  Union[str, Any]:
   snake_case 						=   get_mobilevitva_config(__lowerCAmelCase      ,    __lowerCAmelCase		)

   # load original state_dict
   snake_case 						=   torch.load(__lowerCAmelCase      ,    map_location="""cpu"""		)

   # load huggingface model
   if task_name.startswith("""ade20k_"""		) or task_name.startswith("""voc_"""		):
      snake_case 						=   MobileViTVaForSemanticSegmentation(__lowerCAmelCase		).eval()
      snake_case 						=   False
   else:
      snake_case 						=   MobileViTVaForImageClassification(__lowerCAmelCase		).eval()
      snake_case 						=   False

   # remove and rename some keys of load the original model
   snake_case 						=   checkpoint
   remove_unused_keys(__lowerCAmelCase		)
   snake_case 						=   create_rename_keys(__lowerCAmelCase      ,    base_model=__lowerCAmelCase		)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)

   # load modified state_dict
   model.load_state_dict(__lowerCAmelCase		)

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

   # verify classification model
   if task_name.startswith("""imagenet"""		):
      snake_case 						=   outputs.logits
      snake_case 						=   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 						=   torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]		)
         assert torch.allclose(logits[0, :3]      ,    __lowerCAmelCase      ,    atol=1e-4		)

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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."
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_mobilevitva_checkpoint(
     args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
 )








'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

