'''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__     :	Union[str, Any]				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   print('''Loading config file...'''    )

   def flatten_yaml_as_dict(_A					:				List[Any]  ,			_A					:				Optional[Any]=""  ,			_A					:				Dict="."    ):
      UpperCAmelCase__							:				List[str]   				=  []
      for k, v in d.items():
         UpperCAmelCase__							:				Dict   				=  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    )

   UpperCAmelCase__							:				Optional[int]   				=  argparse.Namespace()
   with open(_A  ,			'''r'''    ) as yaml_file:
      try:
         UpperCAmelCase__							:				Any   				=  yaml.load(_A  ,			Loader=yaml.FullLoader    )

         UpperCAmelCase__							:				Optional[Any]   				=  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 __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Any    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  MobileViTVaConfig()

   UpperCAmelCase__							:				Optional[Any]   				=  False

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

   # orig_config
   UpperCAmelCase__							:				Optional[Any]   				=  load_orig_config_file(_A    )
   assert getattr(_A  ,			'''model.classification.name'''  ,			-1    ) == "mobilevit_v2", "Invalid model"
   UpperCAmelCase__							:				List[str]   				=  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"
   UpperCAmelCase__							:				Union[str, Any]   				=  getattr(_A  ,			'''model.classification.activation.name'''  ,			'''swish'''    )
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      UpperCAmelCase__							:				Optional[Any]   				=  getattr(_A  ,			'''model.segmentation.output_stride'''  ,			16    )
      if "_deeplabv3" in task_name:
         UpperCAmelCase__							:				List[Any]   				=  getattr(_A  ,			'''model.segmentation.deeplabv3.aspp_rates'''  ,			[12, 24, 36]    )
         UpperCAmelCase__							:				List[Any]   				=  getattr(_A  ,			'''model.segmentation.deeplabv3.aspp_out_channels'''  ,			5_12    )
         UpperCAmelCase__							:				Optional[Any]   				=  getattr(_A  ,			'''model.segmentation.deeplabv3.aspp_dropout'''  ,			0.1    )

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

   return config







def __UpperCamelCase(   _A					:				str  ,			_A					:				List[str]  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  dct.pop(_A    )
   UpperCAmelCase__							:				Tuple   				=  val







def __UpperCamelCase(   _A					:				int  ,			_A					:				Tuple=False    ):
   '''simple docstring'''
   if base_model:
      UpperCAmelCase__							:				Any   				=  ''''''
   else:
      UpperCAmelCase__							:				List[Any]   				=  '''mobilevitv2.'''

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

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

      if "conv_1." in k:
         UpperCAmelCase__							:				str   				=  k_new.replace('''conv_1.'''  ,			F'''{model_prefix}conv_stem.'''    )
      for i in [1, 2]:
         if F'''layer_{i}.''' in k:
            UpperCAmelCase__							:				str   				=  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__							:				Optional[Any]   				=  k_new.replace('''.red_1x1.'''  ,			'''.reduce_1x1.'''    )

      for i in [3, 4, 5]:
         if F'''layer_{i}.0.''' in k:
            UpperCAmelCase__							:				Dict   				=  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__							:				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[Any]   				=  [0, 1]
         elif i == 4:
            UpperCAmelCase__							:				Dict   				=  [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__							:				int   				=  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__							:				Optional[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:
            UpperCAmelCase__							:				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:
         UpperCAmelCase__							:				Union[str, Any]   				=  k_new.replace('''pre_norm_attn.0.'''  ,			'''layernorm_before.'''    )
      if "pre_norm_attn.1." in k:
         UpperCAmelCase__							:				str   				=  k_new.replace('''pre_norm_attn.1.'''  ,			'''attention.'''    )
      if "pre_norm_ffn.0." in k:
         UpperCAmelCase__							:				Union[str, Any]   				=  k_new.replace('''pre_norm_ffn.0.'''  ,			'''layernorm_after.'''    )
      if "pre_norm_ffn.1." in k:
         UpperCAmelCase__							:				Union[str, Any]   				=  k_new.replace('''pre_norm_ffn.1.'''  ,			'''ffn.conv1.'''    )
      if "pre_norm_ffn.3." in k:
         UpperCAmelCase__							:				Optional[Any]   				=  k_new.replace('''pre_norm_ffn.3.'''  ,			'''ffn.conv2.'''    )

      if "classifier.1." in k:
         UpperCAmelCase__							:				Union[str, Any]   				=  k_new.replace('''classifier.1.'''  ,			'''classifier.'''    )

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

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







def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[Any]   				=  []
   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 __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   UpperCAmelCase__							:				Optional[Any]   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    )
   return im







@torch.no_grad()
def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Optional[Any]  ,			_A					:				Any  ,			_A					:				int    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  get_mobilevitva_config(_A  ,			_A    )

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

   # load huggingface model
   if task_name.startswith('''ade20k_'''    ) or task_name.startswith('''voc_'''    ):
      UpperCAmelCase__							:				List[str]   				=  MobileViTVaForSemanticSegmentation(_A    ).eval()
      UpperCAmelCase__							:				Optional[int]   				=  False
   else:
      UpperCAmelCase__							:				List[str]   				=  MobileViTVaForImageClassification(_A    ).eval()
      UpperCAmelCase__							:				Optional[int]   				=  False

   # remove and rename some keys of load the original model
   UpperCAmelCase__							:				int   				=  checkpoint
   remove_unused_keys(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  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
   UpperCAmelCase__							:				Union[str, Any]   				=  MobileViTImageProcessor(crop_size=config.image_size  ,			size=config.image_size + 32    )
   UpperCAmelCase__							:				Optional[int]   				=  image_processor(images=prepare_img()  ,			return_tensors='''pt'''    )
   UpperCAmelCase__							:				Optional[Any]   				=  model(**_A    )

   # verify classification model
   if task_name.startswith('''imagenet'''    ):
      UpperCAmelCase__							:				int   				=  outputs.logits
      UpperCAmelCase__							:				List[str]   				=  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([-1.6336e00, -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__":
  UpperCamelCase__     :	List[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__     :	Optional[int]				=			parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )





'''simple docstring'''

import torch

from transformers import AutoModel


class 				_lowercase	(      torch.nn.Module   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_="sayef/fsner-bert-base-uncased"							)			->			Dict:



         '''simple docstring'''



         super(lowerCamelCase_					,self							).__init__()

         UpperCAmelCase__							:				Union[str, Any]   				=  AutoModel.from_pretrained(lowerCamelCase_					,return_dict=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  torch.nn.CosineSimilarity(3					,1e-08							)
         UpperCAmelCase__							:				int   				=  torch.nn.Softmax(dim=1							)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         return self.bert(**lowerCamelCase_							).last_hidden_state




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return token_embeddings.sum(2					,keepdim=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=1							)			->			int:



         '''simple docstring'''



         return self.softmax(T * self.cos(lowerCamelCase_					,lowerCamelCase_							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  W_supports['''sizes'''].tolist()
         UpperCAmelCase__							:				Union[str, Any]   				=  W_supports['''start_token_id'''].item()
         UpperCAmelCase__							:				int   				=  W_supports['''end_token_id'''].item()

         del W_supports["sizes"]
         del W_supports["start_token_id"]
         del W_supports["end_token_id"]

         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  None
         UpperCAmelCase__							:				Tuple   				=  None

         UpperCAmelCase__							:				Dict   				=  W_supports['''input_ids'''] == start_token_id
         UpperCAmelCase__							:				Any   				=  W_supports['''input_ids'''] == end_token_id

         for i, size in enumerate(lowerCamelCase_							):
            if i == 0:
               UpperCAmelCase__							:				int   				=  0
            else:
               UpperCAmelCase__							:				str   				=  support_sizes[i - 1]

            UpperCAmelCase__							:				Dict   				=  S[s : s + size][start_token_masks[s : s + size]]
            UpperCAmelCase__							:				Union[str, Any]   				=  S[s : s + size][end_token_masks[s : s + size]]

            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_start.T							).sum(1							).softmax(0							)
            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_end.T							).sum(1							).softmax(0							)

            if p_starts is not None:
               UpperCAmelCase__							:				str   				=  torch.vstack((p_starts, p_start)							)
               UpperCAmelCase__							:				Optional[int]   				=  torch.vstack((p_ends, p_end)							)
            else:
               UpperCAmelCase__							:				Union[str, Any]   				=  p_start
               UpperCAmelCase__							:				int   				=  p_end

         return p_starts, p_ends




