


'''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()
__UpperCAmelCase    :Tuple				       =     logging.get_logger(__name__)




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     print('''Loading config file...'''						)

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

     __UpperCAmelCase			:				Any													=    argparse.Namespace()
     with open(_lowercase    ,      '''r'''						) as yaml_file:
          try:
               __UpperCAmelCase			:				Union[str, Any]													=    yaml.load(_lowercase    ,      Loader=yaml.FullLoader						)

               __UpperCAmelCase			:				List[Any]													=    flatten_yaml_as_dict(_lowercase						)
               for k, v in flat_cfg.items():
                    setattr(_lowercase    ,      _lowercase    ,      _lowercase						)
          except yaml.YAMLError as exc:
               logger.error('''Error while loading config file: {}. Error message: {}'''.format(_lowercase    ,      str(_lowercase						)						)						)
     return config




def 			_a			(		_lowercase      :			List[str]    ,      _lowercase      :			List[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    MobileViTVaConfig()

     __UpperCAmelCase			:				List[Any]													=    False

     # dataset
     if task_name.startswith('''imagenet1k_'''						):
          __UpperCAmelCase			:				Any													=    1000
          if int(task_name.strip().split('''_'''						)[-1]						) == 384:
               __UpperCAmelCase			:				int													=    384
          else:
               __UpperCAmelCase			:				int													=    256
          __UpperCAmelCase			:				Any													=    '''imagenet-1k-id2label.json'''
     elif task_name.startswith('''imagenet21k_to_1k_'''						):
          __UpperCAmelCase			:				Optional[Any]													=    21000
          if int(task_name.strip().split('''_'''						)[-1]						) == 384:
               __UpperCAmelCase			:				Union[str, Any]													=    384
          else:
               __UpperCAmelCase			:				int													=    256
          __UpperCAmelCase			:				Optional[int]													=    '''imagenet-22k-id2label.json'''
     elif task_name.startswith('''ade20k_'''						):
          __UpperCAmelCase			:				Dict													=    151
          __UpperCAmelCase			:				Dict													=    512
          __UpperCAmelCase			:				str													=    '''ade20k-id2label.json'''
          __UpperCAmelCase			:				int													=    True
     elif task_name.startswith('''voc_'''						):
          __UpperCAmelCase			:				Union[str, Any]													=    21
          __UpperCAmelCase			:				Dict													=    512
          __UpperCAmelCase			:				Dict													=    '''pascal-voc-id2label.json'''
          __UpperCAmelCase			:				Optional[Any]													=    True

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

     if is_segmentation_model:
          __UpperCAmelCase			:				int													=    getattr(_lowercase    ,      '''model.segmentation.output_stride'''    ,      16						)
          if "_deeplabv3" in task_name:
               __UpperCAmelCase			:				int													=    getattr(_lowercase    ,      '''model.segmentation.deeplabv3.aspp_rates'''    ,      [12, 24, 36]						)
               __UpperCAmelCase			:				Tuple													=    getattr(_lowercase    ,      '''model.segmentation.deeplabv3.aspp_out_channels'''    ,      512						)
               __UpperCAmelCase			:				Union[str, Any]													=    getattr(_lowercase    ,      '''model.segmentation.deeplabv3.aspp_dropout'''    ,      0.1						)

    # id2label
     __UpperCAmelCase			:				int													=    '''huggingface/label-files'''
     __UpperCAmelCase			:				Dict													=    json.load(open(hf_hub_download(_lowercase    ,      _lowercase    ,      repo_type='''dataset'''						)    ,      '''r'''						)						)
     __UpperCAmelCase			:				Optional[int]													=    {int(_lowercase						): v for k, v in idalabel.items()}
     __UpperCAmelCase			:				Optional[int]													=    idalabel
     __UpperCAmelCase			:				List[str]													=    {v: k for k, v in idalabel.items()}

     return config




def 			_a			(		_lowercase      :			Union[str, Any]    ,      _lowercase      :			int    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    dct.pop(_lowercase						)
     __UpperCAmelCase			:				Optional[Any]													=    val




def 			_a			(		_lowercase      :			Any    ,      _lowercase      :			Optional[Any]=False						):





     '''simple docstring'''






     if base_model:
          __UpperCAmelCase			:				List[Any]													=    ''''''
     else:
          __UpperCAmelCase			:				Optional[Any]													=    '''mobilevitv2.'''

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

          if ".block." in k:
               __UpperCAmelCase			:				List[str]													=    k_new.replace('''.block.'''    ,      '''.'''						)
          if ".conv." in k:
               __UpperCAmelCase			:				List[Any]													=    k_new.replace('''.conv.'''    ,      '''.convolution.'''						)
          if ".norm." in k:
               __UpperCAmelCase			:				Tuple													=    k_new.replace('''.norm.'''    ,      '''.normalization.'''						)

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

          for i in [3, 4, 5]:
               if F'layer_{i}.0.' in k:
                    __UpperCAmelCase			:				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:
                    __UpperCAmelCase			:				List[Any]													=    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			:				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:
                    __UpperCAmelCase			:				Optional[int]													=    [0, 1]
               elif i == 4:
                    __UpperCAmelCase			:				List[Any]													=    [0, 1, 2, 3]
               elif i == 5:
                    __UpperCAmelCase			:				Dict													=    [0, 1, 2]

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

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

          if "seg_head." in k:
               __UpperCAmelCase			:				List[str]													=    k_new.replace('''seg_head.'''    ,      '''segmentation_head.'''						)
          if ".aspp_layer." in k:
               __UpperCAmelCase			:				Tuple													=    k_new.replace('''.aspp_layer.'''    ,      '''.'''						)
          if ".aspp_pool." in k:
               __UpperCAmelCase			:				Union[str, Any]													=    k_new.replace('''.aspp_pool.'''    ,      '''.'''						)

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




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[int]													=    []
     for k in state_dict.keys():
          if k.startswith('''seg_head.aux_head.'''						):
               keys_to_ignore.append(_lowercase						)
     for k in keys_to_ignore:
          state_dict.pop(_lowercase    ,      _lowercase						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
     __UpperCAmelCase			:				List[str]													=    Image.open(requests.get(_lowercase    ,      stream=_lowercase						).raw						)
     return im




@torch.no_grad()
def 			_a			(		_lowercase      :			int    ,      _lowercase      :			Any    ,      _lowercase      :			List[Any]    ,      _lowercase      :			List[str]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    get_mobilevitva_config(_lowercase    ,      _lowercase						)

     # load original state_dict
     __UpperCAmelCase			:				Optional[int]													=    torch.load(_lowercase    ,      map_location='''cpu'''						)

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

     # remove and rename some keys of load the original model
     __UpperCAmelCase			:				Optional[Any]													=    checkpoint
     remove_unused_keys(_lowercase						)
     __UpperCAmelCase			:				str													=    create_rename_keys(_lowercase    ,      base_model=_lowercase						)
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(_lowercase    ,      _lowercase    ,      _lowercase						)

     # load modified state_dict
     model.load_state_dict(_lowercase						)

     # Check outputs on an image, prepared by MobileViTImageProcessor
     __UpperCAmelCase			:				Optional[int]													=    MobileViTImageProcessor(crop_size=config.image_size    ,      size=config.image_size + 32						)
     __UpperCAmelCase			:				Any													=    image_processor(images=prepare_img()    ,      return_tensors='''pt'''						)
     __UpperCAmelCase			:				str													=    model(**_lowercase						)

     # verify classification model
     if task_name.startswith('''imagenet'''						):
          __UpperCAmelCase			:				Tuple													=    outputs.logits
          __UpperCAmelCase			:				Any													=    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			:				Union[str, Any]													=    torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]						)
               assert torch.allclose(logits[0, :3]    ,      _lowercase    ,      atol=1E-4						)

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


if __name__ == "__main__":
      __UpperCAmelCase    :Any				       =     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."
      )

      __UpperCAmelCase    :Union[str, 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 __future__ import annotations




def 			_a			(		_lowercase      :			list						):





     '''simple docstring'''






     if not nums:
          raise ValueError('''List is empty'''						)
     return sum(_lowercase						) / len(_lowercase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()