

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()
lowercase_             =     logging.get_logger(__name__)
def   a   (    A__			:     Dict  )					->   Optional[Any]:





      """simple docstring"""



      print('Loading config file...'  )

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

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

                  _lowercase			 =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   a   (    A__			:     Optional[Any]   ,     A__			:     Union[str, Any]  )					->   int:





      """simple docstring"""



      _lowercase			 =MobileViTVaConfig()

      _lowercase			 =False

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

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

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

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

      return config
def   a   (    A__			:     Optional[int]   ,     A__			:     Tuple   ,     A__			:     List[Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =dct.pop(A__  )
      _lowercase			 =val
def   a   (    A__			:     List[str]   ,     A__			:     List[Any]=False  )					->   int:





      """simple docstring"""



      if base_model:
            _lowercase			 =''
      else:
            _lowercase			 ='mobilevitv2.'

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

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

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

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

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

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

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

            rename_keys.append((k, k_new)  )
      return rename_keys
def   a   (    A__			:     Any  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =[]
      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   a   (    )					->   List[Any]:





      """simple docstring"""



      _lowercase			 ='http://images.cocodataset.org/val2017/000000039769.jpg'
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      _lowercase			 =Image.open(requests.get(A__   ,     stream=A__  ).raw  )
      return im
@torch.no_grad()
def   a   (    A__			:     Optional[int]   ,     A__			:     Dict   ,     A__			:     int   ,     A__			:     Optional[int]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =get_mobilevitva_config(A__   ,     A__  )

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

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

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

      # verify classification model
      if task_name.startswith('imagenet'  ):
            _lowercase			 =outputs.logits
            _lowercase			 =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
                  _lowercase			 =torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]  )
                  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__":
  lowercase_             =     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.'
  )

  lowercase_             =     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 typing import Any





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        _lowercase			 =num_of_nodes
        _lowercase			 =[]
        _lowercase			 ={}




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        self.m_edges.append([u_node, v_node, weight]      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        if self.m_component[u_node] == u_node:
              return u_node
        return self.find_component(self.m_component[u_node]      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if self.m_component[u_node] != u_node:
              for k in self.m_component:
                    _lowercase			 =self.find_component(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        if component_size[u_node] <= component_size[v_node]:
              _lowercase			 =v_node
              component_size[v_node] += component_size[u_node]
              self.set_component(lowerCAmelCase      )

        elif component_size[u_node] >= component_size[v_node]:
              _lowercase			 =self.find_component(lowerCAmelCase      )
              component_size[u_node] += component_size[v_node]
              self.set_component(lowerCAmelCase      )




  def 						A__  (       self      )						->				None:
        '''simple docstring'''






        _lowercase			 =[]
        _lowercase			 =0

        _lowercase			 =[-1] * self.m_num_of_nodes

        # A list of components (initialized to all of the nodes)
        for node in range(self.m_num_of_nodes      ):
              self.m_component.update({node: node}      )
              component_size.append(1      )

        _lowercase			 =self.m_num_of_nodes

        while num_of_components > 1:
              for edge in self.m_edges:
                    _lowercase , _lowercase , _lowercase			 =edge

                    _lowercase			 =self.m_component[u]
                    _lowercase			 =self.m_component[v]

                    if u_component != v_component:

                          for component in (u_component, v_component):
                                if (
                                    minimum_weight_edge[component] == -1
                                    or minimum_weight_edge[component][2] > w
                                ):
                                      _lowercase			 =[u, v, w]

              for edge in minimum_weight_edge:
                    if isinstance(lowerCAmelCase							,     lowerCAmelCase      ):
                          _lowercase , _lowercase , _lowercase			 =edge

                          _lowercase			 =self.m_component[u]
                          _lowercase			 =self.m_component[v]

                          if u_component != v_component:
                                mst_weight += w
                                self.union(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )
                                print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''      )
                                num_of_components -= 1

              _lowercase			 =[-1] * self.m_num_of_nodes
        print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''      )
def   a   (    )					->   None:





      """simple docstring"""





if __name__ == "__main__":
  import doctest

  doctest.testmod()


