




"""simple docstring"""


def       UpperCAmelCase__ (snake_case__							:      str					):
  """simple docstring"""

  return " ".join(input_str.split()[::-1]					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()








"""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()
A_      			=       logging.get_logger(__name__)


def       UpperCAmelCase__ (snake_case__							:      Optional[int]					):
  """simple docstring"""

  print("""Loading config file..."""					)

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

  _snake_case							:	Dict			=						argparse.Namespace()
  with open(snake_case__	,     """r"""					) as yaml_file:
    try:
      _snake_case							:	List[Any]			=						yaml.load(snake_case__	,     Loader=yaml.FullLoader					)

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


def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	Dict			=						MobileViTVaConfig()

  _snake_case							:	Optional[int]			=						False

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

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

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

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

  return config


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Tuple	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	List[str]			=						dct.pop(snake_case__					)
  _snake_case							:	List[Any]			=						val


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      int=False					):
  """simple docstring"""

  if base_model:
    _snake_case							:	Any			=						""""""
  else:
    _snake_case							:	Union[str, Any]			=						"""mobilevitv2."""

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

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

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

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

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

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

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

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


def       UpperCAmelCase__ (snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	List[str]			=						[]
  for k in state_dict.keys():
    if k.startswith("""seg_head.aux_head."""					):
      keys_to_ignore.append(snake_case__					)
  for k in keys_to_ignore:
    state_dict.pop(snake_case__	,     snake_case__					)


def       UpperCAmelCase__ ():
  """simple docstring"""

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


@torch.no_grad()
def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      Optional[int]	,     snake_case__							:      List[str]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	int			=						get_mobilevitva_config(snake_case__	,     snake_case__					)

  # load original state_dict
  _snake_case							:	Optional[int]			=						torch.load(snake_case__	,     map_location="""cpu"""					)

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

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

  # load modified state_dict
  model.load_state_dict(snake_case__					)

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

  # verify classification model
  if task_name.startswith("""imagenet"""					):
    _snake_case							:	List[str]			=						outputs.logits
    _snake_case							:	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
      _snake_case							:	List[str]			=						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]	,     snake_case__	,     atol=1e-4					)

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


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


