





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_     							= logging.get_logger(__name__)




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]      )      ->	List[Any]:
    print('''Loading config file...'''      )

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

    UpperCamelCase__							:				Optional[int]          =      argparse.Namespace()
    with open(__UpperCAmelCase		,   '''r'''      ) as yaml_file:
        try:
            UpperCamelCase__							:				str          =      yaml.load(__UpperCAmelCase		,   Loader=yaml.FullLoader      )

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Any		,   __UpperCAmelCase:			List[str]      )      ->	Any:
    UpperCamelCase__							:				List[Any]          =      MobileViTVaConfig()

    UpperCamelCase__							:				Optional[int]          =      False

    # dataset
    if task_name.startswith('''imagenet1k_'''      ):
        UpperCamelCase__							:				List[Any]          =      1000
        if int(task_name.strip().split('''_'''      )[-1]      ) == 384:
            UpperCamelCase__							:				Optional[int]          =      384
        else:
            UpperCamelCase__							:				Dict          =      256
        UpperCamelCase__							:				int          =      '''imagenet-1k-id2label.json'''
    elif task_name.startswith('''imagenet21k_to_1k_'''      ):
        UpperCamelCase__							:				int          =      2_1000
        if int(task_name.strip().split('''_'''      )[-1]      ) == 384:
            UpperCamelCase__							:				str          =      384
        else:
            UpperCamelCase__							:				int          =      256
        UpperCamelCase__							:				List[Any]          =      '''imagenet-22k-id2label.json'''
    elif task_name.startswith('''ade20k_'''      ):
        UpperCamelCase__							:				Optional[Any]          =      151
        UpperCamelCase__							:				int          =      512
        UpperCamelCase__							:				List[str]          =      '''ade20k-id2label.json'''
        UpperCamelCase__							:				List[str]          =      True
    elif task_name.startswith('''voc_'''      ):
        UpperCamelCase__							:				List[str]          =      21
        UpperCamelCase__							:				Union[str, Any]          =      512
        UpperCamelCase__							:				Optional[Any]          =      '''pascal-voc-id2label.json'''
        UpperCamelCase__							:				Any          =      True

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

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

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

    return config




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Union[str, Any]      )      ->	str:
    UpperCamelCase__							:				str          =      dct.pop(__UpperCAmelCase      )
    UpperCamelCase__							:				List[str]          =      val




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Any		,   __UpperCAmelCase:			int=False      )      ->	List[str]:
    if base_model:
        UpperCamelCase__							:				Any          =      ''''''
    else:
        UpperCamelCase__							:				List[Any]          =      '''mobilevitv2.'''

    UpperCamelCase__							:				Dict          =      []
    for k in state_dict.keys():
        if k[:8] == "encoder.":
            UpperCamelCase__							:				Optional[int]          =      k[8:]
        else:
            UpperCamelCase__							:				int          =      k

        if ".block." in k:
            UpperCamelCase__							:				Tuple          =      k_new.replace('''.block.'''		,   '''.'''      )
        if ".conv." in k:
            UpperCamelCase__							:				int          =      k_new.replace('''.conv.'''		,   '''.convolution.'''      )
        if ".norm." in k:
            UpperCamelCase__							:				Optional[int]          =      k_new.replace('''.norm.'''		,   '''.normalization.'''      )

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

        for i in [3, 4, 5]:
            if f"layer_{i}.0." in k:
                UpperCamelCase__							:				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:
                UpperCamelCase__							:				Dict          =      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__							:				Union[str, 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__							:				Dict          =      [0, 1]
            elif i == 4:
                UpperCamelCase__							:				int          =      [0, 1, 2, 3]
            elif i == 5:
                UpperCamelCase__							:				Union[str, Any]          =      [0, 1, 2]

            for j in j_in:
                if f"layer_{i}.1.global_rep.{j}." in k:
                    UpperCamelCase__							:				Any          =      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__							:				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:
                UpperCamelCase__							:				Optional[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__							:				Optional[Any]          =      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__							:				Any          =      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__							:				Tuple          =      k_new.replace('''pre_norm_ffn.3.'''		,   '''ffn.conv2.'''      )

        if "classifier.1." in k:
            UpperCamelCase__							:				List[str]          =      k_new.replace('''classifier.1.'''		,   '''classifier.'''      )

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

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]      )      ->	str:
    UpperCamelCase__							:				Tuple          =      []
    for k in state_dict.keys():
        if k.startswith('''seg_head.aux_head.'''      ):
            keys_to_ignore.append(__UpperCAmelCase      )
    for k in keys_to_ignore:
        state_dict.pop(__UpperCAmelCase		,   __UpperCAmelCase      )




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




@torch.no_grad()
def 		lowerCAmelCase_    (  __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			str      )      ->	Any:
    UpperCamelCase__							:				int          =      get_mobilevitva_config(__UpperCAmelCase		,   __UpperCAmelCase      )

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

    # load huggingface model
    if task_name.startswith('''ade20k_'''      ) or task_name.startswith('''voc_'''      ):
        UpperCamelCase__							:				Optional[Any]          =      MobileViTVaForSemanticSegmentation(__UpperCAmelCase      ).eval()
        UpperCamelCase__							:				Dict          =      False
    else:
        UpperCamelCase__							:				Any          =      MobileViTVaForImageClassification(__UpperCAmelCase      ).eval()
        UpperCamelCase__							:				Any          =      False

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

    # load modified state_dict
    model.load_state_dict(__UpperCAmelCase      )

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

    # verify classification model
    if task_name.startswith('''imagenet'''      ):
        UpperCamelCase__							:				Union[str, Any]          =      outputs.logits
        UpperCamelCase__							:				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
            UpperCamelCase__							:				Any          =      torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]      )
            assert torch.allclose(logits[0, :3]		,   __UpperCAmelCase		,   atol=1e-4      )

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


if __name__ == "__main__":
  UpperCAmelCase_     							= 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_     							= parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

       if do_resize:
           UpperCamelCase__							:				str          =      [self.resize(__magic_name__,		size_divisor=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

