



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()
__lowerCamelCase         =			logging.get_logger(__name__)







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      print("Loading config file..."    )

      def flatten_yaml_as_dict(__snake_case					,  __snake_case=""					,  __snake_case="."    ):
            UpperCAmelCase_  :	Optional[int]				      =		[]
            for k, v in d.items():
                  UpperCAmelCase_  :	Dict				      =		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    )

      UpperCAmelCase_  :	Dict				      =		argparse.Namespace()
      with open(__snake_case					,  "r"    ) as yaml_file:
            try:
                  UpperCAmelCase_  :	int				      =		yaml.load(__snake_case					,  Loader=yaml.FullLoader    )

                  UpperCAmelCase_  :	Dict				      =		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					,  __snake_case    )     ->     int:






      '''simple docstring'''







      UpperCAmelCase_  :	List[Any]				      =		MobileViTVaConfig()

      UpperCAmelCase_  :	Optional[int]				      =		False

      # dataset
      if task_name.startswith("imagenet1k_"    ):
            UpperCAmelCase_  :	str				      =		1_0_0_0
            if int(task_name.strip().split("_"    )[-1]    ) == 3_8_4:
                  UpperCAmelCase_  :	List[str]				      =		3_8_4
            else:
                  UpperCAmelCase_  :	str				      =		2_5_6
            UpperCAmelCase_  :	Optional[Any]				      =		"imagenet-1k-id2label.json"
      elif task_name.startswith("imagenet21k_to_1k_"    ):
            UpperCAmelCase_  :	List[str]				      =		2_1_0_0_0
            if int(task_name.strip().split("_"    )[-1]    ) == 3_8_4:
                  UpperCAmelCase_  :	Any				      =		3_8_4
            else:
                  UpperCAmelCase_  :	Any				      =		2_5_6
            UpperCAmelCase_  :	str				      =		"imagenet-22k-id2label.json"
      elif task_name.startswith("ade20k_"    ):
            UpperCAmelCase_  :	Tuple				      =		1_5_1
            UpperCAmelCase_  :	List[str]				      =		5_1_2
            UpperCAmelCase_  :	Any				      =		"ade20k-id2label.json"
            UpperCAmelCase_  :	Optional[int]				      =		True
      elif task_name.startswith("voc_"    ):
            UpperCAmelCase_  :	int				      =		2_1
            UpperCAmelCase_  :	str				      =		5_1_2
            UpperCAmelCase_  :	List[Any]				      =		"pascal-voc-id2label.json"
            UpperCAmelCase_  :	Optional[Any]				      =		True

      # orig_config
      UpperCAmelCase_  :	Union[str, Any]				      =		load_orig_config_file(__snake_case    )
      assert getattr(__snake_case					,  "model.classification.name"					,  -1    ) == "mobilevit_v2", "Invalid model"
      UpperCAmelCase_  :	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"
      UpperCAmelCase_  :	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:
            UpperCAmelCase_  :	Optional[int]				      =		getattr(__snake_case					,  "model.segmentation.output_stride"					,  1_6    )
            if "_deeplabv3" in task_name:
                  UpperCAmelCase_  :	List[Any]				      =		getattr(__snake_case					,  "model.segmentation.deeplabv3.aspp_rates"					,  [1_2, 2_4, 3_6]    )
                  UpperCAmelCase_  :	Optional[int]				      =		getattr(__snake_case					,  "model.segmentation.deeplabv3.aspp_out_channels"					,  5_1_2    )
                  UpperCAmelCase_  :	Tuple				      =		getattr(__snake_case					,  "model.segmentation.deeplabv3.aspp_dropout"					,  0.1    )

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

      return config







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Dict				      =		dct.pop(__snake_case    )
      UpperCAmelCase_  :	Optional[Any]				      =		val







def    _snake_case (       __snake_case					,  __snake_case=False    )     ->     List[Any]:






      '''simple docstring'''







      if base_model:
            UpperCAmelCase_  :	List[str]				      =		""
      else:
            UpperCAmelCase_  :	int				      =		"mobilevitv2."

      UpperCAmelCase_  :	Dict				      =		[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  UpperCAmelCase_  :	str				      =		k[8:]
            else:
                  UpperCAmelCase_  :	Optional[Any]				      =		k

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

            if "conv_1." in k:
                  UpperCAmelCase_  :	Any				      =		k_new.replace("conv_1."					,  F"""{model_prefix}conv_stem."""    )
            for i in [1, 2]:
                  if F"""layer_{i}.""" in k:
                        UpperCAmelCase_  :	Dict				      =		k_new.replace(F"""layer_{i}."""					,  F"""{model_prefix}encoder.layer.{i-1}.layer."""    )
            if ".exp_1x1." in k:
                  UpperCAmelCase_  :	List[str]				      =		k_new.replace(".exp_1x1."					,  ".expand_1x1."    )
            if ".red_1x1." in k:
                  UpperCAmelCase_  :	int				      =		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_  :	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:
                        UpperCAmelCase_  :	Union[str, Any]				      =		[0, 1]
                  elif i == 4:
                        UpperCAmelCase_  :	int				      =		[0, 1, 2, 3]
                  elif i == 5:
                        UpperCAmelCase_  :	Any				      =		[0, 1, 2]

                  for j in j_in:
                        if F"""layer_{i}.1.global_rep.{j}.""" in k:
                              UpperCAmelCase_  :	Dict				      =		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_  :	str				      =		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_  :	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[int]				      =		k_new.replace("pre_norm_attn.0."					,  "layernorm_before."    )
            if "pre_norm_attn.1." in k:
                  UpperCAmelCase_  :	Optional[int]				      =		k_new.replace("pre_norm_attn.1."					,  "attention."    )
            if "pre_norm_ffn.0." in k:
                  UpperCAmelCase_  :	Optional[int]				      =		k_new.replace("pre_norm_ffn.0."					,  "layernorm_after."    )
            if "pre_norm_ffn.1." in k:
                  UpperCAmelCase_  :	List[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_  :	Tuple				      =		k_new.replace("classifier.1."					,  "classifier."    )

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

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







def    _snake_case (       __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Tuple				      =		[]
      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]:






      '''simple docstring'''







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







@torch.no_grad()
def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     List[str]:






      '''simple docstring'''







      UpperCAmelCase_  :	Tuple				      =		get_mobilevitva_config(__snake_case					,  __snake_case    )

      # load original state_dict
      UpperCAmelCase_  :	List[Any]				      =		torch.load(__snake_case					,  map_location="cpu"    )

      # load huggingface model
      if task_name.startswith("ade20k_"    ) or task_name.startswith("voc_"    ):
            UpperCAmelCase_  :	List[Any]				      =		MobileViTVaForSemanticSegmentation(__snake_case    ).eval()
            UpperCAmelCase_  :	int				      =		False
      else:
            UpperCAmelCase_  :	Tuple				      =		MobileViTVaForImageClassification(__snake_case    ).eval()
            UpperCAmelCase_  :	List[Any]				      =		False

      # remove and rename some keys of load the original model
      UpperCAmelCase_  :	int				      =		checkpoint
      remove_unused_keys(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		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
      UpperCAmelCase_  :	Optional[int]				      =		MobileViTImageProcessor(crop_size=config.image_size					,  size=config.image_size + 3_2    )
      UpperCAmelCase_  :	List[str]				      =		image_processor(images=prepare_img()					,  return_tensors="pt"    )
      UpperCAmelCase_  :	Dict				      =		model(**__snake_case    )

      # verify classification model
      if task_name.startswith("imagenet"    ):
            UpperCAmelCase_  :	Dict				      =		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_  :	str				      =		torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-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__":
   __lowerCamelCase         =			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.'''
   )

   __lowerCamelCase         =			parser.parse_args()
   convert_mobilevitva_checkpoint(
       args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
   )





import logging

from transformers import PretrainedConfig


__lowerCamelCase         =			logging.getLogger(__name__)


__lowerCamelCase         =			{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""bertabs"""



       def __init__(     self     ,lowercase=30522     ,lowercase=512     ,lowercase=6     ,lowercase=512     ,lowercase=8     ,lowercase=512     ,lowercase=0.2     ,lowercase=6     ,lowercase=768     ,lowercase=8     ,lowercase=2048     ,lowercase=0.2     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_  :	Any				      =		vocab_size
             UpperCAmelCase_  :	int				      =		max_pos

             UpperCAmelCase_  :	List[str]				      =		enc_layers
             UpperCAmelCase_  :	List[str]				      =		enc_hidden_size
             UpperCAmelCase_  :	Any				      =		enc_heads
             UpperCAmelCase_  :	Any				      =		enc_ff_size
             UpperCAmelCase_  :	Optional[int]				      =		enc_dropout

             UpperCAmelCase_  :	List[str]				      =		dec_layers
             UpperCAmelCase_  :	List[Any]				      =		dec_hidden_size
             UpperCAmelCase_  :	List[str]				      =		dec_heads
             UpperCAmelCase_  :	List[Any]				      =		dec_ff_size
             UpperCAmelCase_  :	List[str]				      =		dec_dropout
