






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        _snake_case   (_snake_case				:       Optional[int])					->	Tuple:
   print('Loading config file...')

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

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

         _lowercase              =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        _snake_case   (_snake_case				:       Optional[Any]      ,    _snake_case				:       str)					->	str:
   _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              =2_1000
      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(_snake_case)
   assert getattr(_snake_case      ,    'model.classification.name'      ,    -1) == "mobilevit_v2", "Invalid model"
   _lowercase              =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"
   _lowercase              =getattr(_snake_case      ,    'model.classification.activation.name'      ,    'swish')
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

   return config





def        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       Tuple      ,    _snake_case				:       List[str])					->	str:
   _lowercase              =dct.pop(_snake_case)
   _lowercase              =val





def        _snake_case   (_snake_case				:       List[Any]      ,    _snake_case				:       int=False)					->	List[Any]:
   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        _snake_case   (_snake_case				:       int)					->	Dict:
   _lowercase              =[]
   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        _snake_case   ()					->	Optional[Any]:
   _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(_snake_case      ,    stream=_snake_case).raw)
   return im





@torch.no_grad()
def        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       Optional[Any]      ,    _snake_case				:       List[str]      ,    _snake_case				:       Dict)					->	int:
   _lowercase              =get_mobilevitva_config(_snake_case      ,    _snake_case)

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

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

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

   # 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([-1.6_336E00, -7.3_204E-02, -5.1_883E-01])
         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__":
    _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
    )










class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          print(self.vertex)
          for i in self.vertex:
             print(snake_case,      ' -> ',      ' -> '.join([str(snake_case) for j in self.vertex[i]]))





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

          if from_vertex in self.vertex:
             self.vertex[from_vertex].append(snake_case)
          else:
             # else make a new vertex
             _lowercase              =[to_vertex]





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =[False] * len(self.vertex)

          # call the recursive helper function
          for i in range(len(self.vertex)):
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()

    # OUTPUT:
    # 0  ->  1 -> 2
    # 1  ->  2
    # 2  ->  0 -> 3
    # 3  ->  3
    # DFS:
    #  0 1 2 3


