






import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase	:		Any										=   logging.get_logger(__name__)







def 			A__							(      _a  :					Tuple		,  _a  :					Union[str, Any]=False	):





 '''simple docstring'''







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

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

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

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  snake_case__  :    int							       =[(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__							(      _a  :					int		,  _a  :					Dict		,  _a  :					Optional[int]=False	):





 '''simple docstring'''







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







def 			A__							(      _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    int							       =["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(_a		,  _a	)







def 			A__							(      _a  :					Tuple		,  _a  :					List[str]		,  _a  :					List[Any]	):





 '''simple docstring'''







 snake_case__  :    Optional[Any]							       =dct.pop(_a	)
 snake_case__  :    Optional[int]							       =val







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
 snake_case__  :    Optional[int]							       =Image.open(requests.get(_a		,  stream=_a	).raw	)
 return im







@torch.no_grad()
def 			A__							(      _a  :					Tuple		,  _a  :					List[Any]	):





 '''simple docstring'''







 snake_case__  :    Any							       =ViTConfig()
 snake_case__  :    List[str]							       =False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  snake_case__  :    str							       =True
  snake_case__  :    Optional[int]							       =int(vit_name[-12:-10]	)
  snake_case__  :    Dict							       =int(vit_name[-9:-6]	)
 else:
  snake_case__  :    Optional[Any]							       =1000
  snake_case__  :    int							       ="""huggingface/label-files"""
  snake_case__  :    Dict							       ="""imagenet-1k-id2label.json"""
  snake_case__  :    Any							       =json.load(open(hf_hub_download(_a		,  _a		,  repo_type="""dataset"""	)		,  """r"""	)	)
  snake_case__  :    Union[str, Any]							       ={int(_a	): v for k, v in idalabel.items()}
  snake_case__  :    Optional[Any]							       =idalabel
  snake_case__  :    List[str]							       ={v: k for k, v in idalabel.items()}
  snake_case__  :    Union[str, Any]							       =int(vit_name[-6:-4]	)
  snake_case__  :    Optional[int]							       =int(vit_name[-3:]	)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("""tiny"""	):
   snake_case__  :    int							       =192
   snake_case__  :    List[Any]							       =768
   snake_case__  :    str							       =12
   snake_case__  :    Optional[int]							       =3
  elif vit_name[9:].startswith("""small"""	):
   snake_case__  :    Tuple							       =384
   snake_case__  :    Dict							       =1536
   snake_case__  :    Any							       =12
   snake_case__  :    str							       =6
  else:
   pass
 else:
  if vit_name[4:].startswith("""small"""	):
   snake_case__  :    Optional[int]							       =768
   snake_case__  :    Dict							       =2304
   snake_case__  :    int							       =8
   snake_case__  :    List[Any]							       =8
  elif vit_name[4:].startswith("""base"""	):
   pass
  elif vit_name[4:].startswith("""large"""	):
   snake_case__  :    Tuple							       =1024
   snake_case__  :    List[str]							       =4096
   snake_case__  :    str							       =24
   snake_case__  :    Optional[int]							       =16
  elif vit_name[4:].startswith("""huge"""	):
   snake_case__  :    Dict							       =1280
   snake_case__  :    int							       =5120
   snake_case__  :    Optional[int]							       =32
   snake_case__  :    Optional[int]							       =16

    # load original model from timm
 snake_case__  :    int							       =timm.create_model(_a		,  pretrained=_a	)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 snake_case__  :    str							       =timm_model.state_dict()
 if base_model:
  remove_classification_head_(_a	)
 snake_case__  :    Dict							       =create_rename_keys(_a		,  _a	)
 for src, dest in rename_keys:
  rename_key(_a		,  _a		,  _a	)
 read_in_q_k_v(_a		,  _a		,  _a	)

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  snake_case__  :    str							       =ViTModel(_a	).eval()
 else:
  snake_case__  :    Dict							       =ViTForImageClassification(_a	).eval()
 model.load_state_dict(_a	)

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  snake_case__  :    List[str]							       =DeiTImageProcessor(size=config.image_size	)
 else:
  snake_case__  :    Tuple							       =ViTImageProcessor(size=config.image_size	)
 snake_case__  :    Tuple							       =image_processor(images=prepare_img()		,  return_tensors="""pt"""	)
 snake_case__  :    Optional[int]							       =encoding["""pixel_values"""]
 snake_case__  :    Optional[int]							       =model(_a	)

 if base_model:
  snake_case__  :    Optional[Any]							       =timm_model.forward_features(_a	)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(_a		,  outputs.pooler_output		,  atol=1E-3	)
 else:
  snake_case__  :    List[Any]							       =timm_model(_a	)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(_a		,  outputs.logits		,  atol=1E-3	)

 Path(_a	).mkdir(exist_ok=_a	)
 print(f"Saving model {vit_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__":
       __lowerCamelCase	:		List[Any]										=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--vit_name""",
           default="""vit_base_patch16_224""",
           type=str,
           help="""Name of the ViT timm model you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
       )

       __lowerCamelCase	:		Any										=   parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)













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


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Any										=   {"""vocab_file""": """spiece.model"""}

__lowerCamelCase	:		Optional[Any]										=   {
    """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"""
        ),
    }
}

__lowerCamelCase	:		List[str]										=   {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}







class  _lowercase   (   _A						):
      _a							:						int										=							VOCAB_FILES_NAMES
      _a							:						List[str]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						Any										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						Tuple										=							['input_ids', 'attention_mask']
      _a							:						List[int]										=							[]
      def __init__(     self				,			a				,			a="<unk>"				,			a="<s>"				,			a="</s>"				,			a="<pad>"				,			a="[SEP]"				,			a="[MASK]"				,			a="[CLS]"				,			a = None				,			**a				,			):
       snake_case__  :    str							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else bos_token
       snake_case__  :    Optional[int]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else eos_token
       snake_case__  :    Any							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else unk_token
       snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else pad_token
       snake_case__  :    int							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else cls_token
       snake_case__  :    Tuple							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       snake_case__  :    Union[str, Any]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else mask_token

       snake_case__  :    Union[str, Any]							       ={} if sp_model_kwargs is None else sp_model_kwargs

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

       snake_case__  :    Optional[int]							       =vocab_file

       snake_case__  :    List[str]							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(a   )
      @property
      def 						lowercase__					(     self   ):
       return self.sp_model.get_piece_size()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       ={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   ):
       snake_case__  :    List[Any]							       =self.__dict__.copy()
       snake_case__  :    Optional[int]							       =None
       return state
      def __setstate__(     self				,			a   ):
       snake_case__  :    Optional[Any]							       =d

       # for backward compatibility
       if not hasattr(self				,			"""sp_model_kwargs"""   ):
        snake_case__  :    Optional[Any]							       ={}

       snake_case__  :    Tuple							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.encode(a				,			out_type=a   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.piece_to_id(a   )
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Dict							       =self.sp_model.IdToPiece(a   )
       return token
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[int]							       =[]
       snake_case__  :    Dict							       =""""""
       snake_case__  :    Dict							       =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(a   ) + token
         snake_case__  :    Optional[Any]							       =True
         snake_case__  :    Dict							       =[]
        else:
         current_sub_tokens.append(a   )
         snake_case__  :    List[Any]							       =False
       out_string += self.sp_model.decode(a   )
       return out_string.strip()
      def 						lowercase__					(     self				,			a				,			a = False				,			a = None				,			a = True				,			**a				,			):
       snake_case__  :    List[str]							       =kwargs.pop("""use_source_tokenizer"""				,			a   )

       snake_case__  :    int							       =self.convert_ids_to_tokens(a				,			skip_special_tokens=a   )

       # 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
       snake_case__  :    List[Any]							       =[]
       snake_case__  :    int							       =[]
       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(a   )   )
          snake_case__  :    List[Any]							       =[]
         sub_texts.append(a   )
        else:
         current_sub_text.append(a   )
       if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(a   )   )

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

       snake_case__  :    str							       =(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
        snake_case__  :    Any							       =self.clean_up_tokenization(a   )
        return clean_text
       else:
        return text
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if not os.path.isdir(a   ):
        logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
        return
       snake_case__  :    Any							       =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:
         snake_case__  :    Any							       =self.sp_model.serialized_model_proto()
         fi.write(a   )

       return (out_vocab_file,)
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case__  :    Union[str, Any]							       =[self.cls_token_id]
       snake_case__  :    Dict							       =[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
      def 						lowercase__					(     self				,			a				,			a = None				,			a = False   ):
       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 None:
        return [1] + ([0] * len(a   )) + [1]
       return [1] + ([0] * len(a   )) + [1] + ([0] * len(a   )) + [1]



      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    List[str]							       =[self.sep_token_id]
       snake_case__  :    List[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]





