






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 	UpperCamelCase	(							snake_case__  : Optional[int]	):




     '''simple docstring'''






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

     def flatten_yaml_as_dict(snake_case__  : Tuple						,snake_case__  : List[str]=""						,snake_case__  : Tuple="."	):
          __snake_case      :Tuple 							=    []
          for k, v in d.items():
               __snake_case      :Any 							=    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      :str 							=    argparse.Namespace()
     with open(snake_case__						,"""r"""	) as yaml_file:
          try:
               __snake_case      :str 							=    yaml.load(snake_case__						,Loader=yaml.FullLoader	)

               __snake_case      :Union[str, 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__  : Optional[int]						,snake_case__  : Optional[Any]	):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    MobileViTVaConfig()

     __snake_case      :Dict 							=    False

     # dataset
     if task_name.startswith("""imagenet1k_"""	):
          __snake_case      :List[str] 							=    1000
          if int(task_name.strip().split("""_"""	)[-1]	) == 384:
               __snake_case      :int 							=    384
          else:
               __snake_case      :List[Any] 							=    256
          __snake_case      :Union[str, Any] 							=    """imagenet-1k-id2label.json"""
     elif task_name.startswith("""imagenet21k_to_1k_"""	):
          __snake_case      :Optional[int] 							=    2_1000
          if int(task_name.strip().split("""_"""	)[-1]	) == 384:
               __snake_case      :Tuple 							=    384
          else:
               __snake_case      :Any 							=    256
          __snake_case      :Any 							=    """imagenet-22k-id2label.json"""
     elif task_name.startswith("""ade20k_"""	):
          __snake_case      :Tuple 							=    151
          __snake_case      :int 							=    512
          __snake_case      :int 							=    """ade20k-id2label.json"""
          __snake_case      :Optional[int] 							=    True
     elif task_name.startswith("""voc_"""	):
          __snake_case      :Dict 							=    21
          __snake_case      :Any 							=    512
          __snake_case      :Tuple 							=    """pascal-voc-id2label.json"""
          __snake_case      :Tuple 							=    True

     # orig_config
     __snake_case      :Union[str, Any] 							=    load_orig_config_file(snake_case__	)
     assert getattr(snake_case__						,"""model.classification.name"""						,-1	) == "mobilevit_v2", "Invalid model"
     __snake_case      :Dict 							=    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      :List[Any] 							=    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      :int 							=    getattr(snake_case__						,"""model.segmentation.output_stride"""						,16	)
          if "_deeplabv3" in task_name:
               __snake_case      :List[str] 							=    getattr(snake_case__						,"""model.segmentation.deeplabv3.aspp_rates"""						,[12, 24, 36]	)
               __snake_case      :Union[str, Any] 							=    getattr(snake_case__						,"""model.segmentation.deeplabv3.aspp_out_channels"""						,512	)
               __snake_case      :List[Any] 							=    getattr(snake_case__						,"""model.segmentation.deeplabv3.aspp_dropout"""						,0.1	)

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

     return config






def 	UpperCamelCase	(							snake_case__  : Union[str, Any]						,snake_case__  : Tuple						,snake_case__  : Optional[int]	):




     '''simple docstring'''






     __snake_case      :Tuple 							=    dct.pop(snake_case__	)
     __snake_case      :Dict 							=    val






def 	UpperCamelCase	(							snake_case__  : Optional[Any]						,snake_case__  : List[str]=False	):




     '''simple docstring'''






     if base_model:
          __snake_case      :int 							=    """"""
     else:
          __snake_case      :str 							=    """mobilevitv2."""

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

          if ".block." in k:
               __snake_case      :List[str] 							=    k_new.replace(""".block."""						,"""."""	)
          if ".conv." in k:
               __snake_case      :str 							=    k_new.replace(""".conv."""						,""".convolution."""	)
          if ".norm." in k:
               __snake_case      :Tuple 							=    k_new.replace(""".norm."""						,""".normalization."""	)

          if "conv_1." in k:
               __snake_case      :Tuple 							=    k_new.replace("""conv_1."""						,f'''{model_prefix}conv_stem.'''	)
          for i in [1, 2]:
               if f'''layer_{i}.''' in k:
                    __snake_case      :Optional[int] 							=    k_new.replace(f'''layer_{i}.'''						,f'''{model_prefix}encoder.layer.{i-1}.layer.'''	)
          if ".exp_1x1." in k:
               __snake_case      :Tuple 							=    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      :int 							=    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      :int 							=    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      :List[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:
                    __snake_case      :Tuple 							=    [0, 1]
               elif i == 4:
                    __snake_case      :List[Any] 							=    [0, 1, 2, 3]
               elif i == 5:
                    __snake_case      :int 							=    [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      :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      :List[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      :Tuple 							=    k_new.replace("""pre_norm_attn.0."""						,"""layernorm_before."""	)
          if "pre_norm_attn.1." in k:
               __snake_case      :Any 							=    k_new.replace("""pre_norm_attn.1."""						,"""attention."""	)
          if "pre_norm_ffn.0." in k:
               __snake_case      :Optional[Any] 							=    k_new.replace("""pre_norm_ffn.0."""						,"""layernorm_after."""	)
          if "pre_norm_ffn.1." in k:
               __snake_case      :List[str] 							=    k_new.replace("""pre_norm_ffn.1."""						,"""ffn.conv1."""	)
          if "pre_norm_ffn.3." in k:
               __snake_case      :Dict 							=    k_new.replace("""pre_norm_ffn.3."""						,"""ffn.conv2."""	)

          if "classifier.1." in k:
               __snake_case      :Any 							=    k_new.replace("""classifier.1."""						,"""classifier."""	)

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

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






def 	UpperCamelCase	(							snake_case__  : Dict	):




     '''simple docstring'''






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






@torch.no_grad()
def 	UpperCamelCase	(							snake_case__  : Optional[int]						,snake_case__  : Any						,snake_case__  : Optional[int]						,snake_case__  : List[str]	):




     '''simple docstring'''






     __snake_case      :int 							=    get_mobilevitva_config(snake_case__						,snake_case__	)

     # load original state_dict
     __snake_case      :Dict 							=    torch.load(snake_case__						,map_location="""cpu"""	)

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

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

     # verify classification model
     if task_name.startswith("""imagenet"""	):
          __snake_case      :List[str] 							=    outputs.logits
          __snake_case      :Tuple 							=    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      :Optional[int] 							=    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 os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__       		=  {
    """google/rembert""": 256,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Tuple								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__=False      ,    a__=True      ,    a__=True      ,    a__="[CLS]"      ,    a__="[SEP]"      ,    a__="[UNK]"      ,    a__="[SEP]"      ,    a__="[PAD]"      ,    a__="[CLS]"      ,    a__="[MASK]"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            super().__init__(
                do_lower_case=a__      ,    remove_space=a__      ,    keep_accents=a__      ,    bos_token=a__      ,    eos_token=a__      ,    unk_token=a__      ,    sep_token=a__      ,    pad_token=a__      ,    cls_token=a__      ,    mask_token=a__      ,    **a__      ,    )

            __snake_case      :List[Any] 							=    do_lower_case
            __snake_case      :Dict 							=    remove_space
            __snake_case      :int 							=    keep_accents
            __snake_case      :Optional[int] 							=    vocab_file

            __snake_case      :int 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(a__     )



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return len(self.sp_model     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :int 							=    {self.convert_ids_to_tokens(a__     ): i for i in range(self.vocab_size     )}
            vocab.update(self.added_tokens_encoder     )
            return vocab



       def __getstate__(				self     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    self.__dict__.copy()
            __snake_case      :Dict 							=    None
            return state



       def __setstate__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :Dict 							=    d
            __snake_case      :Dict 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.sp_model.EncodeAsPieces(a__     )
            return pieces



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self.sp_model.decode_pieces(a__     )
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    [self.sep_token_id]
            __snake_case      :List[Any] 							=    [self.cls_token_id]
            if token_ids_a is None:
                 return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''simple docstring'''





            if already_has_special_tokens:
                 if token_ids_a is not None:
                      raise ValueError(
                          """You should not supply a second sequence if the provided sequence of """
                          """ids is already formatted with special tokens for the model."""     )
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is not None:
                 return [1] + ([0] * len(a__     )) + [1] + ([0] * len(a__     )) + [1]
            return [1] + ([0] * len(a__     )) + [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    [self.sep_token_id]
            __snake_case      :Any 							=    [self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep     ) * [0]
            return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__     ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(a__     )     )
                 return
            __snake_case      :Optional[int] 							=    os.path.join(
                a__      ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

            if os.path.abspath(self.vocab_file     ) != os.path.abspath(a__     ):
                 copyfile(self.vocab_file      ,    a__     )

            return (out_vocab_file,)


