'''simple docstring'''

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def __a (	__lowerCamelCase			:						int				)	->							Tuple:
      '''simple docstring'''


      if (
          (cp >= 0x4e00 and cp <= 0x9fff)
          or (cp >= 0x3400 and cp <= 0x4dbf)  #
          or (cp >= 0x2_0000 and cp <= 0x2_a6df)  #
          or (cp >= 0x2_a700 and cp <= 0x2_b73f)  #
          or (cp >= 0x2_b740 and cp <= 0x2_b81f)  #
          or (cp >= 0x2_b820 and cp <= 0x2_ceaf)  #
          or (cp >= 0xf900 and cp <= 0xfaff)
          or (cp >= 0x2_f800 and cp <= 0x2_fa1f)  #
      ):  #
            return True

      return False


def __a (	__lowerCamelCase			:						str				)	->							Optional[int]:
      '''simple docstring'''


      for char in word:
            lowercase_     =      ord(__lowerCamelCase				)
            if not _is_chinese_char(__lowerCamelCase				):
                  return 0
      return 1


def __a (	__lowerCamelCase			:						List[str]				)	->							Optional[int]:
      '''simple docstring'''


      lowercase_     =      set()

      for token in tokens:
            lowercase_     =      len(__lowerCamelCase				) > 1 and is_chinese(__lowerCamelCase				)
            if chinese_word:
                  word_set.add(__lowerCamelCase				)
      lowercase_     =      list(__lowerCamelCase				)
      return word_list


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						set()				)	->							Optional[Any]:
      '''simple docstring'''


      if not chinese_word_set:
            return bert_tokens
      lowercase_     =      max([len(__lowerCamelCase				) for w in chinese_word_set]				)

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


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						LTP ,		__lowerCamelCase			:						BertTokenizer				)	->							str:
      '''simple docstring'''


      lowercase_     =      []

      for i in range(0 ,		len(__lowerCamelCase				) ,		100				):
            lowercase_     =      ltp_tokenizer.seg(lines[i : i + 100]				)[0]
            lowercase_     =      [get_chinese_word(__lowerCamelCase				) for r in res]
            ltp_res.extend(__lowerCamelCase				)
      assert len(__lowerCamelCase				) == len(__lowerCamelCase				)

      lowercase_     =      []
      for i in range(0 ,		len(__lowerCamelCase				) ,		100				):
            lowercase_     =      bert_tokenizer(lines[i : i + 100] ,		add_special_tokens=__lowerCamelCase ,		truncation=__lowerCamelCase ,		max_length=512				)
            bert_res.extend(res["input_ids"]				)
      assert len(__lowerCamelCase				) == len(__lowerCamelCase				)

      lowercase_     =      []
      for input_ids, chinese_word in zip(__lowerCamelCase ,		__lowerCamelCase				):
            lowercase_     =      []
            for id in input_ids:
                  lowercase_     =      bert_tokenizer._convert_id_to_token(__lowerCamelCase				)
                  input_tokens.append(__lowerCamelCase				)
            lowercase_     =      add_sub_symbol(__lowerCamelCase ,		__lowerCamelCase				)
            lowercase_     =      []
            # 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] == "##":
                        lowercase_     =      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 __a (	__lowerCamelCase			:						Tuple				)	->							int:
      '''simple docstring'''


      with open(args.file_name ,		"r" ,		encoding="utf-8"				) as f:
            lowercase_     =      f.readlines()
      lowercase_     =      [line.strip() for line in data if len(__lowerCamelCase				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      lowercase_     =      LTP(args.ltp				)  # faster in GPU device
      lowercase_     =      BertTokenizer.from_pretrained(args.bert				)

      lowercase_     =      prepare_ref(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)

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


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

       lowerCAmelCase_ : Any						  =      parser.parse_args()
       main(args)







'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def __a (	__lowerCamelCase			:						List[Any]				)	->							Tuple:
      '''simple docstring'''


      return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      {}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            lowercase_     =      key.replace("heads.cmd.mim_head.cls.predictions" ,		"mmm_image_head"				)
            lowercase_     =      key.replace("heads.cmd.mlm_head.cls.predictions" ,		"mmm_text_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.cls" ,		"itm_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.pooler" ,		"itm_head.pooler"				)
            lowercase_     =      key.replace("heads.cmd.clip_head.logit_scale" ,		"flava.logit_scale"				)
            lowercase_     =      key.replace("heads.fairseq_mlm.cls.predictions" ,		"mlm_head"				)
            lowercase_     =      key.replace("heads.imagenet.mim_head.cls.predictions" ,		"mim_head"				)
            lowercase_     =      key.replace("mm_text_projection" ,		"flava.text_to_mm_projection"				)
            lowercase_     =      key.replace("mm_image_projection" ,		"flava.image_to_mm_projection"				)
            lowercase_     =      key.replace("image_encoder.module" ,		"flava.image_model"				)
            lowercase_     =      key.replace("text_encoder.module" ,		"flava.text_model"				)
            lowercase_     =      key.replace("mm_encoder.module.encoder.cls_token" ,		"flava.multimodal_model.cls_token"				)
            lowercase_     =      key.replace("mm_encoder.module" ,		"flava.multimodal_model"				)
            lowercase_     =      key.replace("text_projection" ,		"flava.text_projection"				)
            lowercase_     =      key.replace("image_projection" ,		"flava.image_projection"				)

            lowercase_     =      value.float()

      for key, value in codebook_state_dict.items():
            lowercase_     =      value

      return upgrade


@torch.no_grad()
def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple=None				)	->							Optional[int]:
      '''simple docstring'''


      if config_path is not None:
            lowercase_     =      FlavaConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      FlavaConfig()

      lowercase_     =      FlavaForPreTraining(__lowerCamelCase				).eval()

      lowercase_     =      convert_dalle_checkpoint(__lowerCamelCase ,		__lowerCamelCase ,		save_checkpoint=__lowerCamelCase				)

      if os.path.exists(__lowerCamelCase				):
            lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      else:
            lowercase_     =      torch.hub.load_state_dict_from_url(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      upgrade_state_dict(__lowerCamelCase ,		__lowerCamelCase				)
      hf_model.load_state_dict(__lowerCamelCase				)
      lowercase_     =      hf_model.state_dict()
      lowercase_     =      count_parameters(__lowerCamelCase				)
      lowercase_     =      count_parameters(__lowerCamelCase				) + count_parameters(__lowerCamelCase				)

      assert torch.allclose(__lowerCamelCase ,		__lowerCamelCase ,		atol=1e-3				)

      hf_model.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[str]						  =      argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
       parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_ : List[Any]						  =      parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






