





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


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

snake_case__					:							Any													=     {'vocab_file': 'spiece.model'}

snake_case__					:							Tuple													=     {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}





class A_					(			UpperCamelCase__   ):




   def __init__(self	:int    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:List[Any]=False    ,   _UpperCamelCase	:Optional[Any]=True    ,   _UpperCamelCase	:List[str]=False    ,   _UpperCamelCase	:Dict="<s>"    ,   _UpperCamelCase	:List[Any]="</s>"    ,   _UpperCamelCase	:Optional[int]="<unk>"    ,   _UpperCamelCase	:List[str]="<sep>"    ,   _UpperCamelCase	:str="<pad>"    ,   _UpperCamelCase	:Tuple="<cls>"    ,   _UpperCamelCase	:Any="<mask>"    ,   _UpperCamelCase	:List[Any]=["<eop>", "<eod>"]    ,   _UpperCamelCase	:int = None    ,   **_UpperCamelCase	:str    ,   )->					None:
         __A          =				AddedToken(_a    ,   lstrip=_a    ,   rstrip=_a ) if isinstance(_a    ,   _a ) else mask_token

         __A          =				{} if sp_model_kwargs is None else sp_model_kwargs

         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    ,   additional_special_tokens=_a    ,   sp_model_kwargs=self.sp_model_kwargs    ,   **_a    ,   )

         __A          =				3

         __A          =				do_lower_case
         __A          =				remove_space
         __A          =				keep_accents
         __A          =				vocab_file

         __A          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(_a )

         try:
               import jieba
         except ModuleNotFoundError as error:
               raise error.__class__(
                   '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                   '''See https://pypi.org/project/jieba/ for installation.''' )
         __A          =				jieba
         __A          =				str.maketrans(''' \n'''    ,   '''\u2582\u2583''' )




   @property
   # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
   def _lowerCAmelCase      (self	:Tuple )->					List[Any]:
         return len(self.sp_model )




   def _lowerCAmelCase      (self	:Optional[int] )->					Tuple:
         __A          =				{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	:Dict )->					Tuple:
         __A          =				self.__dict__.copy()
         __A          =				None
         return state




   def __setstate__(self	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         __A          =				d

         # for backward compatibility
         if not hasattr(self    ,   '''sp_model_kwargs''' ):
               __A          =				{}

         __A          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:Dict )->					str:
         if self.remove_space:
               __A          =				""" """.join(inputs.strip().split() )
         else:
               __A          =				inputs
         __A          =				outputs.replace('''``'''    ,   '''\"''' ).replace('''\'\''''    ,   '''\"''' )

         if not self.keep_accents:
               __A          =				unicodedata.normalize('''NFKD'''    ,   _a )
               __A          =				"""""".join([c for c in outputs if not unicodedata.combining(_a )] )
         if self.do_lower_case:
               __A          =				outputs.lower()

         return outputs




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:str )->					List[str]:
         __A          =				self.preprocess_text(_a )
         __A          =				self.sp_model.encode(_a    ,   out_type=_a )
         __A          =				[]
         for piece in pieces:
               if len(_a ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
                     __A          =				self.sp_model.EncodeAsPieces(piece[:-1].replace(_a    ,   '''''' ) )
                     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                           if len(cur_pieces[0] ) == 1:
                                 __A          =				cur_pieces[1:]
                           else:
                                 __A          =				cur_pieces[0][1:]
                     cur_pieces.append(piece[-1] )
                     new_pieces.extend(_a )
               else:
                     new_pieces.append(_a )

         return new_pieces




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple )->					Union[str, Any]:
         return self.sp_model.PieceToId(_a )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[int] )->					Optional[Any]:
         return self.sp_model.IdToPiece(_a )




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:Optional[int] )->					int:
         __A          =				"""""".join(_a ).replace(_a    ,   ''' ''' ).strip()
         return out_string




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Dict = None )->					List[int]:
         __A          =				[self.sep_token_id]
         __A          =				[self.cls_token_id]
         if token_ids_a is None:
               return token_ids_a + sep + cls
         return token_ids_a + sep + token_ids_a + sep + cls




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:List[str] = None    ,   _UpperCamelCase	:Dict = False )->					List[int]:

         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_a    ,   token_ids_a=_a    ,   already_has_special_tokens=_a )

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




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Tuple = None )->					List[int]:
         __A          =				[self.sep_token_id]
         __A          =				[2]

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




   def _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[Any] = None )->					Tuple[str]:
         if not os.path.isdir(_a ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
               return
         __A          =				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 ) and os.path.isfile(self.vocab_file ):
               copyfile(self.vocab_file    ,   _a )
         elif not os.path.isfile(self.vocab_file ):
               with open(_a    ,   '''wb''' ) as fi:
                     __A          =				self.sp_model.serialized_model_proto()
                     fi.write(_a )

         return (out_vocab_file,)






   def _lowerCAmelCase      (self	:Any    ,   *_UpperCamelCase	:List[str]    ,   **_UpperCamelCase	:Optional[Any] )->					Optional[Any]:
         __A          =				super()._decode(*_a    ,   **_a )
         __A          =				text.replace(''' '''    ,   '''''' ).replace('''\u2582'''    ,   ''' ''' ).replace('''\u2583'''    ,   '''\n''' )
         return text











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()
_snake_case										=       logging.get_logger(__name__)







def 				lowerCAmelCase_	(     snake_case_     ):
      print("""Loading config file..."""     )

      def flatten_yaml_as_dict(snake_case_,snake_case_="",snake_case_="."     ):
            _A							:     Union[str, Any]			=						[]
            for k, v in d.items():
                  _A							:     Optional[int]			=						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_     )

      _A							:     List[Any]			=						argparse.Namespace()
      with open(snake_case_,"""r"""     ) as yaml_file:
            try:
                  _A							:     List[Any]			=						yaml.load(snake_case_,Loader=yaml.FullLoader     )

                  _A							:     Optional[int]			=						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 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						MobileViTVaConfig()

      _A							:     Tuple			=						False

      # dataset
      if task_name.startswith("""imagenet1k_"""     ):
            _A							:     Dict			=						1000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     int			=						384
            else:
                  _A							:     int			=						256
            _A							:     List[str]			=						"""imagenet-1k-id2label.json"""
      elif task_name.startswith("""imagenet21k_to_1k_"""     ):
            _A							:     Union[str, Any]			=						21000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     str			=						384
            else:
                  _A							:     List[Any]			=						256
            _A							:     List[str]			=						"""imagenet-22k-id2label.json"""
      elif task_name.startswith("""ade20k_"""     ):
            _A							:     int			=						151
            _A							:     int			=						512
            _A							:     Optional[int]			=						"""ade20k-id2label.json"""
            _A							:     Any			=						True
      elif task_name.startswith("""voc_"""     ):
            _A							:     List[Any]			=						21
            _A							:     Dict			=						512
            _A							:     Dict			=						"""pascal-voc-id2label.json"""
            _A							:     int			=						True

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

      if is_segmentation_model:
            _A							:     Optional[int]			=						getattr(snake_case_,"""model.segmentation.output_stride""",16     )
            if "_deeplabv3" in task_name:
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_rates""",[12, 24, 36]     )
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_out_channels""",512     )
                  _A							:     str			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_dropout""",0.1     )

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

      return config







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Any			=						dct.pop(snake_case_     )
      _A							:     Union[str, Any]			=						val







def 				lowerCAmelCase_	(     snake_case_,snake_case_=False     ):
      if base_model:
            _A							:     Optional[int]			=						""""""
      else:
            _A							:     Dict			=						"""mobilevitv2."""

      _A							:     int			=						[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  _A							:     Any			=						k[8:]
            else:
                  _A							:     List[str]			=						k

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

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

            for i in [3, 4, 5]:
                  if f'''layer_{i}.0.''' in k:
                        _A							:     Optional[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:
                        _A							:     Union[str, Any]			=						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:
                        _A							:     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:
                        _A							:     Optional[int]			=						[0, 1]
                  elif i == 4:
                        _A							:     Union[str, Any]			=						[0, 1, 2, 3]
                  elif i == 5:
                        _A							:     Optional[Any]			=						[0, 1, 2]

                  for j in j_in:
                        if f'''layer_{i}.1.global_rep.{j}.''' in k:
                              _A							:     Union[str, 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:
                        _A							:     List[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:
                        _A							:     Optional[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:
                  _A							:     Optional[Any]			=						k_new.replace("""pre_norm_attn.0.""","""layernorm_before."""     )
            if "pre_norm_attn.1." in k:
                  _A							:     str			=						k_new.replace("""pre_norm_attn.1.""","""attention."""     )
            if "pre_norm_ffn.0." in k:
                  _A							:     Optional[Any]			=						k_new.replace("""pre_norm_ffn.0.""","""layernorm_after."""     )
            if "pre_norm_ffn.1." in k:
                  _A							:     Dict			=						k_new.replace("""pre_norm_ffn.1.""","""ffn.conv1."""     )
            if "pre_norm_ffn.3." in k:
                  _A							:     List[str]			=						k_new.replace("""pre_norm_ffn.3.""","""ffn.conv2."""     )

            if "classifier.1." in k:
                  _A							:     List[str]			=						k_new.replace("""classifier.1.""","""classifier."""     )

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

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







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







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_     ):
      _A							:     List[Any]			=						get_mobilevitva_config(snake_case_,snake_case_     )

      # load original state_dict
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )

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

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

      # verify classification model
      if task_name.startswith("""imagenet"""     ):
            _A							:     List[Any]			=						outputs.logits
            _A							:     Optional[int]			=						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
                  _A							:     int			=						torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-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__":
  _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."
  )

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

