





import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
snake_case								=							logging.get_logger(__name__)

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[int]      ,				snake_case__	:str=False  )   ->    List[Any]:
  _lowercase						    =			[]
  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'),
        ]  )

    # 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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:Optional[Any]      ,				snake_case__	:List[str]=False  )   ->    Union[str, Any]:
  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"""blocks.{i}.attn.qkv.weight"""  )
    _lowercase						    =			state_dict.pop(F"""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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Union[str, Any]  )   ->    Tuple:
  _lowercase						    =			['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(snake_case__      ,				snake_case__  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:Tuple      ,				snake_case__	:Tuple  )   ->    Optional[Any]:
  _lowercase						    =			dct.pop(snake_case__  )
  _lowercase						    =			val

def 				SCREAMING_SNAKE_CASE__							(	)   ->    List[str]:
  _lowercase						    =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase						    =			Image.open(requests.get(snake_case__      ,				stream=snake_case__  ).raw  )
  return im

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict      ,				snake_case__	:Dict      ,				snake_case__	:List[str]=True  )   ->    str:
  _lowercase						    =			ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    _lowercase						    =			8
  # set labels if required
  if not base_model:
    _lowercase						    =			1000
    _lowercase						    =			'huggingface/label-files'
    _lowercase						    =			'imagenet-1k-id2label.json'
    _lowercase						    =			json.load(open(hf_hub_download(snake_case__      ,				snake_case__      ,				repo_type='dataset'  )      ,				'r'  )  )
    _lowercase						    =			{int(snake_case__  ): v for k, v in idalabel.items()}
    _lowercase						    =			idalabel
    _lowercase						    =			{v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    _lowercase						    =			384
    _lowercase						    =			1536
    _lowercase						    =			12
    _lowercase						    =			6

  # load original model from torch hub
  _lowercase						    =			torch.hub.load('facebookresearch/dino:main'      ,				snake_case__  )
  original_model.eval()

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

  # load HuggingFace model
  if base_model:
    _lowercase						    =			ViTModel(snake_case__      ,				add_pooling_layer=snake_case__  ).eval()
  else:
    _lowercase						    =			ViTForImageClassification(snake_case__  ).eval()
  model.load_state_dict(snake_case__  )

  # Check outputs on an image, prepared by ViTImageProcessor
  _lowercase						    =			ViTImageProcessor()
  _lowercase						    =			image_processor(images=prepare_img()      ,				return_tensors='pt'  )
  _lowercase						    =			encoding['pixel_values']
  _lowercase						    =			model(snake_case__  )

  if base_model:
    _lowercase						    =			original_model(snake_case__  )
    assert torch.allclose(snake_case__      ,				outputs.last_hidden_state[:, 0, :]      ,				atol=1E-1  )
  else:
    _lowercase						    =			original_model(snake_case__  )
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(snake_case__      ,				outputs.logits      ,				atol=1E-3  )

  Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
  print(F"""Saving model {model_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(
          """--model_name""",
          default="""dino_vitb16""",
          type=str,
          help="""Name of the model trained with DINO you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--base_model""",
          action="""store_true""",
          help="""Whether to only convert the base model (no projection head weights).""",
      )

      parser.set_defaults(base_model=True)
      snake_case								=							parser.parse_args()
      convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[Any]:
   # This defines a "chinese character" as anything in the CJK Unicode block:
   #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
   #
   # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
   # despite its name. The modern Korean Hangul alphabet is a different block,
   # as is Japanese Hiragana and Katakana. Those alphabets are used to write
   # space-separated words, so they are not treated specially and handled
   # like the all of the other languages.
   if (
       (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
       or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
       or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
       or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
       or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
       or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
       or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
       or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
   ):  #
      return True

   return False
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->List[str]:
   # word like '180' or '身高' or '神'
   for char in word:
      UpperCAmelCase      					=  ord(lowerCAmelCase_ )
      if not _is_chinese_char(lowerCAmelCase_ ):
         return 0
   return 1
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[int]:
   UpperCAmelCase      					=  set()

   for token in tokens:
      UpperCAmelCase      					=  len(lowerCAmelCase_ ) > 1 and is_chinese(lowerCAmelCase_ )
      if chinese_word:
         word_set.add(lowerCAmelCase_ )
   UpperCAmelCase      					=  list(lowerCAmelCase_ )
   return word_list
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Union[str, Any]:
   if not chinese_word_set:
      return bert_tokens
   UpperCAmelCase      					=  max([len(lowerCAmelCase_ ) for w in chinese_word_set] )

   UpperCAmelCase      					=  bert_tokens
   UpperCAmelCase					,	UpperCAmelCase      					=  0, len(lowerCAmelCase_ )
   while start < end:
      UpperCAmelCase      					=  True
      if is_chinese(bert_word[start] ):
         UpperCAmelCase      					=  min(end - start	,    lowerCAmelCase_ )
         for i in range(lowerCAmelCase_	,    1	,    -1 ):
            UpperCAmelCase      					=  """""".join(bert_word[start : start + i] )
            if whole_word in chinese_word_set:
               for j in range(start + 1	,    start + i ):
                  UpperCAmelCase      					=  """##""" + bert_word[j]
               UpperCAmelCase      					=  start + i
               UpperCAmelCase      					=  False
               break
      if single_word:
         start += 1
   return bert_word
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   UpperCAmelCase      					=  []

   for i in range(0	,    len(lowerCAmelCase_ )	,    1_0_0 ):
      UpperCAmelCase      					=  ltp_tokenizer.pipeline(lines[i : i + 1_0_0]	,    tasks=["""cws"""] ).cws
      UpperCAmelCase      					=  [get_chinese_word(lowerCAmelCase_ ) for r in res]
      ltp_res.extend(lowerCAmelCase_ )
   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

   UpperCAmelCase      					=  []
   for i in range(0	,    len(lowerCAmelCase_ )	,    1_0_0 ):
      UpperCAmelCase      					=  bert_tokenizer(lines[i : i + 1_0_0]	,    add_special_tokens=lowerCAmelCase_	,    truncation=lowerCAmelCase_	,    max_length=5_1_2 )
      bert_res.extend(res["""input_ids"""] )
   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

   UpperCAmelCase      					=  []
   for input_ids, chinese_word in zip(lowerCAmelCase_	,    lowerCAmelCase_ ):
      UpperCAmelCase      					=  []
      for id in input_ids:
         UpperCAmelCase      					=  bert_tokenizer._convert_id_to_token(lowerCAmelCase_ )
         input_tokens.append(lowerCAmelCase_ )
      UpperCAmelCase      					=  add_sub_symbol(lowerCAmelCase_	,    lowerCAmelCase_ )
      UpperCAmelCase      					=  []
      # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
      for i, token in enumerate(lowerCAmelCase_ ):
         if token[:2] == "##":
            UpperCAmelCase      					=  token[2:]
            # save chinese tokens' pos
            if len(lowerCAmelCase_ ) == 1 and _is_chinese_char(ord(lowerCAmelCase_ ) ):
               ref_id.append(lowerCAmelCase_ )
      ref_ids.append(lowerCAmelCase_ )

   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

   return ref_ids
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
   # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
   with open(args.file_name	,    """r"""	,    encoding="""utf-8""" ) as f:
      UpperCAmelCase      					=  f.readlines()
   UpperCAmelCase      					=  [line.strip() for line in data if len(lowerCAmelCase_ ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   UpperCAmelCase      					=  LTP(args.ltp )  # faster in GPU device
   UpperCAmelCase      					=  BertTokenizer.from_pretrained(args.bert )

   UpperCAmelCase      					=  prepare_ref(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )

   with open(args.save_path	,    """w"""	,    encoding="""utf-8""" ) as f:
      UpperCAmelCase      					=  [json.dumps(lowerCAmelCase_ ) + """\n""" for ref in ref_ids]
      f.writelines(lowerCAmelCase_ )


if __name__ == "__main__":
 __a	 =      argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     required=False,
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""",
     required=False,
     type=str,
     default="""./resources/ltp""",
     help="""resources for LTP tokenizer, usually a path""",
 )
 parser.add_argument(
     """--bert""",
     required=False,
     type=str,
     default="""./resources/robert""",
     help="""resources for Bert tokenizer""",
 )
 parser.add_argument(
     """--save_path""",
     required=False,
     type=str,
     default="""./resources/ref.txt""",
     help="""path to save res""",
 )

 __a	 =      parser.parse_args()
 main(args)



