





import os
import re
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 logging


_snake_case           =			logging.get_logger(__name__)

_snake_case           =			{"""vocab_file""": """spiece.model"""}

_snake_case           =			{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

_snake_case           =			{
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      VOCAB_FILES_NAMES
  __lowerCamelCase		=      PRETRAINED_VOCAB_FILES_MAP
  __lowerCamelCase		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCamelCase		=      ['input_ids', 'attention_mask']
  __lowerCamelCase		=      []







  def __init__(      self  :Optional[Any]   ,							_lowercase  :Optional[Any]   ,							_lowercase  :Any="<unk>"   ,							_lowercase  :Tuple="<s>"   ,							_lowercase  :Any="</s>"   ,							_lowercase  :Dict="<pad>"   ,							_lowercase  :int="[SEP]"   ,							_lowercase  :Union[str, Any]="[MASK]"   ,							_lowercase  :Tuple="[CLS]"   ,							_lowercase  :Optional[Dict[str, Any]] = None   ,							**_lowercase  :Optional[int]   ,							):






     '''simple docstring'''





     lowercase__			       =  AddedToken(_lowercase   ,							lstrip=_lowercase   ,							rstrip=_lowercase ) if isinstance(_lowercase   ,							_lowercase ) else bos_token
     lowercase__			       =  AddedToken(_lowercase   ,							lstrip=_lowercase   ,							rstrip=_lowercase ) if isinstance(_lowercase   ,							_lowercase ) else eos_token
     lowercase__			       =  AddedToken(_lowercase   ,							lstrip=_lowercase   ,							rstrip=_lowercase ) if isinstance(_lowercase   ,							_lowercase ) else unk_token
     lowercase__			       =  AddedToken(_lowercase   ,							lstrip=_lowercase   ,							rstrip=_lowercase ) if isinstance(_lowercase   ,							_lowercase ) else pad_token
     lowercase__			       =  AddedToken(_lowercase   ,							lstrip=_lowercase   ,							rstrip=_lowercase ) if isinstance(_lowercase   ,							_lowercase ) else cls_token
     lowercase__			       =  AddedToken(_lowercase   ,							lstrip=_lowercase   ,							rstrip=_lowercase ) if isinstance(_lowercase   ,							_lowercase ) else sep_token

     # Mask token behave like a normal word, i.e. include the space before it
     lowercase__			       =  AddedToken(_lowercase   ,							lstrip=_lowercase   ,							rstrip=_lowercase ) if isinstance(_lowercase   ,							_lowercase ) else mask_token

     lowercase__			       =  {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=_lowercase   ,							eos_token=_lowercase   ,							unk_token=_lowercase   ,							pad_token=_lowercase   ,							sep_token=_lowercase   ,							mask_token=_lowercase   ,							cls_token=_lowercase   ,							sp_model_kwargs=self.sp_model_kwargs   ,							**_lowercase   ,							)

     lowercase__			       =  vocab_file

     lowercase__			       =  spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(_lowercase )







  @property
  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     return self.sp_model.get_piece_size()







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
     vocab.update(self.added_tokens_encoder )
     return vocab







  def __getstate__(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.__dict__.copy()
     lowercase__			       =  None
     return state







  def __setstate__(      self  :Union[str, Any]   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  d

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

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







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :str ):






     '''simple docstring'''





     return self.sp_model.encode(_lowercase   ,							out_type=_lowercase )







  def   UpperCAmelCase					(      self  :Union[str, Any]   ,							_lowercase  :Union[str, Any] ):






     '''simple docstring'''





     return self.sp_model.piece_to_id(_lowercase )







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  self.sp_model.IdToPiece(_lowercase )
     return token







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  []
     lowercase__			       =  ""
     lowercase__			       =  False
     for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
           if not prev_is_special:
              out_string += " "
           out_string += self.sp_model.decode(_lowercase ) + token
           lowercase__			       =  True
           lowercase__			       =  []
        else:
           current_sub_tokens.append(_lowercase )
           lowercase__			       =  False
     out_string += self.sp_model.decode(_lowercase )
     return out_string.strip()







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :List[int]   ,							_lowercase  :bool = False   ,							_lowercase  :bool = None   ,							_lowercase  :bool = True   ,							**_lowercase  :Union[str, Any]   ,							):






     '''simple docstring'''





     lowercase__			       =  kwargs.pop("use_source_tokenizer"   ,							_lowercase )

     lowercase__			       =  self.convert_ids_to_tokens(_lowercase   ,							skip_special_tokens=_lowercase )

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     lowercase__			       =  []
     lowercase__			       =  []
     for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
           continue
        if token in self.added_tokens_encoder:
           if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(_lowercase ) )
              lowercase__			       =  []
           sub_texts.append(_lowercase )
        else:
           current_sub_text.append(_lowercase )
     if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(_lowercase ) )

     # Mimic the behavior of the Rust tokenizer:
     # No space before [MASK] and [SEP]
     if spaces_between_special_tokens:
        lowercase__			       =  re.sub(r" (\[(MASK|SEP)\])"   ,							r"\1"   ,							" ".join(_lowercase ) )
     else:
        lowercase__			       =  "".join(_lowercase )

     lowercase__			       =  (
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
        lowercase__			       =  self.clean_up_tokenization(_lowercase )
        return clean_text
     else:
        return text







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :str   ,							_lowercase  :Optional[str] = None ):






     '''simple docstring'''





     if not os.path.isdir(_lowercase ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
        return
     lowercase__			       =  os.path.join(
         _lowercase   ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ) and os.path.isfile(self.vocab_file ):
        copyfile(self.vocab_file   ,							_lowercase )
     elif not os.path.isfile(self.vocab_file ):
        with open(_lowercase   ,							"wb" ) as fi:
           lowercase__			       =  self.sp_model.serialized_model_proto()
           fi.write(_lowercase )

     return (out_vocab_file,)







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :List[int]   ,							_lowercase  :Optional[List[int]] = None ):






     '''simple docstring'''





     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     lowercase__			       =  [self.cls_token_id]
     lowercase__			       =  [self.sep_token_id]
     return cls + token_ids_a + sep + token_ids_a + sep







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :List[int]   ,							_lowercase  :Optional[List[int]] = None   ,							_lowercase  :bool = False ):






     '''simple docstring'''





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

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







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :List[int]   ,							_lowercase  :Optional[List[int]] = None ):






     '''simple docstring'''





     lowercase__			       =  [self.sep_token_id]
     lowercase__			       =  [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]











import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)


def _A       (   __magic_name__  , __magic_name__=False	):
   lowercase__			       =  []
   for i in range(config.num_hidden_layers	):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((f'''module.blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
      rename_keys.append(
          (f'''module.blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
      rename_keys.append((f'''module.blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
      rename_keys.append((f'''module.blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
      rename_keys.append((f'''module.blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')	)
      rename_keys.append((f'''module.blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')	)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ("module.cls_token", "vit.embeddings.cls_token"),
           ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
           ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
           ("module.pos_embed", "vit.embeddings.position_embeddings"),
       ]	)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ("module.norm.weight", "layernorm.weight"),
              ("module.norm.bias", "layernorm.bias"),
          ]	)

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      lowercase__			       =  [(pair[0], pair[1][4:]) if pair[1].startswith("vit"	) else pair for pair in rename_keys]
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("norm.weight", "vit.layernorm.weight"),
              ("norm.bias", "vit.layernorm.bias"),
              ("head.weight", "classifier.weight"),
              ("head.bias", "classifier.bias"),
          ]	)

   return rename_keys


def _A       (   __magic_name__  , __magic_name__  , __magic_name__=False	):
   for i in range(config.num_hidden_layers	):
      if base_model:
         lowercase__			       =  ""
      else:
         lowercase__			       =  "vit."
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      lowercase__			       =  state_dict.pop(f'''module.blocks.{i}.attn.qkv.weight'''	)
      lowercase__			       =  state_dict.pop(f'''module.blocks.{i}.attn.qkv.bias'''	)
      # next, add query, keys and values (in that order) to the state dict
      lowercase__			       =  in_proj_weight[
          : config.hidden_size, :
      ]
      lowercase__			       =  in_proj_bias[: config.hidden_size]
      lowercase__			       =  in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowercase__			       =  in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      lowercase__			       =  in_proj_weight[
          -config.hidden_size :, :
      ]
      lowercase__			       =  in_proj_bias[-config.hidden_size :]


def _A       (   __magic_name__	):
   lowercase__			       =  ["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(__magic_name__  , __magic_name__	)


def _A       (   __magic_name__	):
   # projection head is used in the self-supervised pre-training in MSN,
   # for downstream task it's not needed.
   lowercase__			       =  [
       "module.fc.fc1.weight",
       "module.fc.fc1.bias",
       "module.fc.bn1.weight",
       "module.fc.bn1.bias",
       "module.fc.bn1.running_mean",
       "module.fc.bn1.running_var",
       "module.fc.bn1.num_batches_tracked",
       "module.fc.fc2.weight",
       "module.fc.fc2.bias",
       "module.fc.bn2.weight",
       "module.fc.bn2.bias",
       "module.fc.bn2.running_mean",
       "module.fc.bn2.running_var",
       "module.fc.bn2.num_batches_tracked",
       "module.fc.fc3.weight",
       "module.fc.fc3.bias",
   ]
   for k in ignore_keys:
      state_dict.pop(__magic_name__  , __magic_name__	)


def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  dct.pop(__magic_name__	)
   lowercase__			       =  val


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  ViTMSNConfig()
   lowercase__			       =  1000

   lowercase__			       =  "datasets/huggingface/label-files"
   lowercase__			       =  "imagenet-1k-id2label.json"
   lowercase__			       =  json.load(open(hf_hub_download(__magic_name__  , __magic_name__	)  , "r"	)	)
   lowercase__			       =  {int(__magic_name__	): v for k, v in idalabel.items()}
   lowercase__			       =  idalabel
   lowercase__			       =  {v: k for k, v in idalabel.items()}

   if "s16" in checkpoint_url:
      lowercase__			       =  384
      lowercase__			       =  1536
      lowercase__			       =  6
   elif "l16" in checkpoint_url:
      lowercase__			       =  1024
      lowercase__			       =  4096
      lowercase__			       =  24
      lowercase__			       =  16
      lowercase__			       =  0.1
   elif "b4" in checkpoint_url:
      lowercase__			       =  4
   elif "l7" in checkpoint_url:
      lowercase__			       =  7
      lowercase__			       =  1024
      lowercase__			       =  4096
      lowercase__			       =  24
      lowercase__			       =  16
      lowercase__			       =  0.1

   lowercase__			       =  ViTMSNModel(__magic_name__	)

   lowercase__			       =  torch.hub.load_state_dict_from_url(__magic_name__  , map_location="cpu"	)["target_encoder"]

   lowercase__			       =  ViTImageProcessor(size=config.image_size	)

   remove_projection_head(__magic_name__	)
   lowercase__			       =  create_rename_keys(__magic_name__  , base_model=__magic_name__	)

   for src, dest in rename_keys:
      rename_key(__magic_name__  , __magic_name__  , __magic_name__	)
   read_in_q_k_v(__magic_name__  , __magic_name__  , base_model=__magic_name__	)

   model.load_state_dict(__magic_name__	)
   model.eval()

   lowercase__			       =  "http://images.cocodataset.org/val2017/000000039769.jpg"

   lowercase__			       =  Image.open(requests.get(__magic_name__  , stream=__magic_name__	).raw	)
   lowercase__			       =  ViTImageProcessor(
       size=config.image_size  , image_mean=__magic_name__  , image_std=__magic_name__	)
   lowercase__			       =  image_processor(images=__magic_name__  , return_tensors="pt"	)

   # forward pass
   torch.manual_seed(2	)
   lowercase__			       =  model(**__magic_name__	)
   lowercase__			       =  outputs.last_hidden_state

   # The following Colab Notebook was used to generate these outputs:
   # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
   if "s16" in checkpoint_url:
      lowercase__			       =  torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]	)
   elif "b16" in checkpoint_url:
      lowercase__			       =  torch.tensor([[14.2_889, -18.9_045, 11.7_281]]	)
   elif "l16" in checkpoint_url:
      lowercase__			       =  torch.tensor([[41.5_028, -22.8_681, 45.6_475]]	)
   elif "b4" in checkpoint_url:
      lowercase__			       =  torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]	)
   else:
      lowercase__			       =  torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]	)

   # verify logits
   assert torch.allclose(last_hidden_state[:, 0, :3]  , __magic_name__  , atol=1e-4	)

   print(f'''Saving model to {pytorch_dump_folder_path}'''	)
   model.save_pretrained(__magic_name__	)

   print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
   image_processor.save_pretrained(__magic_name__	)


if __name__ == "__main__":
  _snake_case           =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  _snake_case           =			parser.parse_args()
  convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






