


'''simple docstring'''


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


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




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      int):
      A_						:  Union[str, Any]          =				os.path.abspath(_SCREAMING_SNAKE_CASE)
      logger.info(F'Converting TensorFlow checkpoint from {tf_path}')
      # Load weights from TF model
      A_						:  str          =				tf.train.list_variables(_SCREAMING_SNAKE_CASE)
      A_						:  Union[str, Any]          =				[]
      A_						:  Dict          =				[]
      A_						:  Optional[Any]          =				[]
      for full_name, shape in init_vars:
            # logger.info(f"Loading TF weight {name} with shape {shape}")
            A_						:  List[str]          =				full_name.split("""/""")
            if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
                  logger.info(F'Skipping non-model layer {full_name}')
                  continue
            if "optimizer" in full_name:
                  logger.info(F'Skipping optimization layer {full_name}')
                  continue
            if name[0] == "model":
                  # ignore initial 'model'
                  A_						:  Dict          =				name[1:]
            # figure out how many levels deep the name is
            A_						:  int          =				0
            for _name in name:
                  if _name.startswith("""layer_with_weights"""):
                        depth += 1
                  else:
                        break
            layer_depth.append(_SCREAMING_SNAKE_CASE)
            # read data
            A_						:  str          =				tf.train.load_variable(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE)
            names.append("""/""".join(_SCREAMING_SNAKE_CASE))
            arrays.append(_SCREAMING_SNAKE_CASE)
      logger.info(F'Read a total of {len(_SCREAMING_SNAKE_CASE):,} layers')

      # Sanity check
      if len(set(_SCREAMING_SNAKE_CASE)) != 1:
            raise ValueError(F'Found layer names with different depths (layer depth {list(set(_SCREAMING_SNAKE_CASE))})')
      A_						:  List[Any]          =				list(set(_SCREAMING_SNAKE_CASE))[0]
      if layer_depth != 1:
            raise ValueError(
                """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
                """ heads.""")

      # convert layers
      logger.info("""Converting weights...""")
      for full_name, array in zip(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE):
            A_						:  Dict          =				full_name.split("""/""")
            A_						:  int          =				model
            A_						:  List[str]          =				[]
            for i, m_name in enumerate(_SCREAMING_SNAKE_CASE):
                  if m_name == ".ATTRIBUTES":
                        # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                        break
                  if m_name.startswith("""layer_with_weights"""):
                        A_						:  Dict          =				int(m_name.split("""-""")[-1])
                        if layer_num <= 2:
                              # embedding layers
                              # layer_num 0: word_embeddings
                              # layer_num 1: position_embeddings
                              # layer_num 2: token_type_embeddings
                              continue
                        elif layer_num == 3:
                              # embedding LayerNorm
                              trace.extend(["""embeddings""", """LayerNorm"""])
                              A_						:  Dict          =				getattr(_SCREAMING_SNAKE_CASE							,						"""embeddings""")
                              A_						:  Optional[Any]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""LayerNorm""")
                        elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                              # encoder layers
                              trace.extend(["""encoder""", """layer""", str(layer_num - 4)])
                              A_						:  Optional[int]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""encoder""")
                              A_						:  Dict          =				getattr(_SCREAMING_SNAKE_CASE							,						"""layer""")
                              A_						:  Optional[int]          =				pointer[layer_num - 4]
                        elif layer_num == config.num_hidden_layers + 4:
                              # pooler layer
                              trace.extend(["""pooler""", """dense"""])
                              A_						:  Optional[int]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""pooler""")
                              A_						:  str          =				getattr(_SCREAMING_SNAKE_CASE							,						"""dense""")
                  elif m_name == "embeddings":
                        trace.append("""embeddings""")
                        A_						:  int          =				getattr(_SCREAMING_SNAKE_CASE							,						"""embeddings""")
                        if layer_num == 0:
                              trace.append("""word_embeddings""")
                              A_						:  Optional[int]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""word_embeddings""")
                        elif layer_num == 1:
                              trace.append("""position_embeddings""")
                              A_						:  Tuple          =				getattr(_SCREAMING_SNAKE_CASE							,						"""position_embeddings""")
                        elif layer_num == 2:
                              trace.append("""token_type_embeddings""")
                              A_						:  Dict          =				getattr(_SCREAMING_SNAKE_CASE							,						"""token_type_embeddings""")
                        else:
                              raise ValueError(F'Unknown embedding layer with name {full_name}')
                        trace.append("""weight""")
                        A_						:  Optional[int]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""weight""")
                  elif m_name == "_attention_layer":
                        # self-attention layer
                        trace.extend(["""attention""", """self"""])
                        A_						:  Any          =				getattr(_SCREAMING_SNAKE_CASE							,						"""attention""")
                        A_						:  Optional[int]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""self""")
                  elif m_name == "_attention_layer_norm":
                        # output attention norm
                        trace.extend(["""attention""", """output""", """LayerNorm"""])
                        A_						:  Optional[Any]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""attention""")
                        A_						:  Union[str, Any]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""output""")
                        A_						:  Any          =				getattr(_SCREAMING_SNAKE_CASE							,						"""LayerNorm""")
                  elif m_name == "_attention_output_dense":
                        # output attention dense
                        trace.extend(["""attention""", """output""", """dense"""])
                        A_						:  str          =				getattr(_SCREAMING_SNAKE_CASE							,						"""attention""")
                        A_						:  List[Any]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""output""")
                        A_						:  int          =				getattr(_SCREAMING_SNAKE_CASE							,						"""dense""")
                  elif m_name == "_output_dense":
                        # output dense
                        trace.extend(["""output""", """dense"""])
                        A_						:  List[Any]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""output""")
                        A_						:  str          =				getattr(_SCREAMING_SNAKE_CASE							,						"""dense""")
                  elif m_name == "_output_layer_norm":
                        # output dense
                        trace.extend(["""output""", """LayerNorm"""])
                        A_						:  int          =				getattr(_SCREAMING_SNAKE_CASE							,						"""output""")
                        A_						:  str          =				getattr(_SCREAMING_SNAKE_CASE							,						"""LayerNorm""")
                  elif m_name == "_key_dense":
                        # attention key
                        trace.append("""key""")
                        A_						:  Tuple          =				getattr(_SCREAMING_SNAKE_CASE							,						"""key""")
                  elif m_name == "_query_dense":
                        # attention query
                        trace.append("""query""")
                        A_						:  Dict          =				getattr(_SCREAMING_SNAKE_CASE							,						"""query""")
                  elif m_name == "_value_dense":
                        # attention value
                        trace.append("""value""")
                        A_						:  int          =				getattr(_SCREAMING_SNAKE_CASE							,						"""value""")
                  elif m_name == "_intermediate_dense":
                        # attention intermediate dense
                        trace.extend(["""intermediate""", """dense"""])
                        A_						:  Optional[int]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""intermediate""")
                        A_						:  List[str]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""dense""")
                  elif m_name == "_output_layer_norm":
                        # output layer norm
                        trace.append("""output""")
                        A_						:  int          =				getattr(_SCREAMING_SNAKE_CASE							,						"""output""")
                  # weights & biases
                  elif m_name in ["bias", "beta"]:
                        trace.append("""bias""")
                        A_						:  Dict          =				getattr(_SCREAMING_SNAKE_CASE							,						"""bias""")
                  elif m_name in ["kernel", "gamma"]:
                        trace.append("""weight""")
                        A_						:  List[str]          =				getattr(_SCREAMING_SNAKE_CASE							,						"""weight""")
                  else:
                        logger.warning(F'Ignored {m_name}')
                        # for certain layers reshape is necessary
            A_						:  int          =				""".""".join(_SCREAMING_SNAKE_CASE)
            if re.match(r"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""							,						_SCREAMING_SNAKE_CASE) or re.match(
                r"""(\S+)\.attention\.output\.dense\.weight"""							,						_SCREAMING_SNAKE_CASE):
                  A_						:  Optional[Any]          =				array.reshape(pointer.data.shape)
            if "kernel" in full_name:
                  A_						:  Optional[Any]          =				array.transpose()
            if pointer.shape == array.shape:
                  A_						:  Dict          =				torch.from_numpy(_SCREAMING_SNAKE_CASE)
            else:
                  raise ValueError(
                      F'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                      F' {array.shape}')
            logger.info(F'Successfully set variable {full_name} to PyTorch layer {trace}')
      return model




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      int							,						lowerCamelCase  :      Dict):
      # Instantiate model
      logger.info(F'Loading model based on config from {config_path}...')
      A_						:  str          =				BertConfig.from_json_file(_SCREAMING_SNAKE_CASE)
      A_						:  Optional[int]          =				BertModel(_SCREAMING_SNAKE_CASE)

      # Load weights from checkpoint
      logger.info(F'Loading weights from checkpoint {tf_checkpoint_path}...')
      load_tfa_weights_in_bert(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE)

      # Save pytorch-model
      logger.info(F'Saving PyTorch model to {pytorch_dump_path}...')
      torch.save(model.state_dict()							,						_SCREAMING_SNAKE_CASE)


if __name__ == "__main__":
     __magic_name__			=      argparse.ArgumentParser()
     parser.add_argument(
         '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
     )
     parser.add_argument(
         '--bert_config_file',
         type=str,
         required=True,
         help='The config json file corresponding to the BERT model. This specifies the model architecture.',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         type=str,
         required=True,
         help='Path to the output PyTorch model (must include filename).',
     )
     __magic_name__			=      parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def      lowerCamelCase					(      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      lowerCamelCase					(      lowerCamelCase  :      str):
      # word like '180' or '身高' or '神'
      for char in word:
            A_						:  Optional[Any]          =				ord(lowerCamelCase)
            if not _is_chinese_char(lowerCamelCase):
                  return 0
      return 1




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      A_						:  Any          =				set()

      for token in tokens:
            A_						:  str          =				len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
            if chinese_word:
                  word_set.add(lowerCamelCase)
      A_						:  Any          =				list(lowerCamelCase)
      return word_list




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      set()):
      if not chinese_word_set:
            return bert_tokens
      A_						:  Any          =				max([len(lowerCamelCase) for w in chinese_word_set])

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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      LTP							,						lowerCamelCase  :      BertTokenizer):
      A_						:  Union[str, Any]          =				[]

      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  List[Any]          =				ltp_tokenizer.pipeline(lines[i : i + 100]							,						tasks=["""cws"""]).cws
            A_						:  int          =				[get_chinese_word(lowerCamelCase) for r in res]
            ltp_res.extend(lowerCamelCase)
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  List[Any]          =				[]
      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  Dict          =				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)

      A_						:  Union[str, Any]          =				[]
      for input_ids, chinese_word in zip(lowerCamelCase							,						lowerCamelCase):
            A_						:  List[Any]          =				[]
            for id in input_ids:
                  A_						:  List[Any]          =				bert_tokenizer._convert_id_to_token(lowerCamelCase)
                  input_tokens.append(lowerCamelCase)
            A_						:  int          =				add_sub_symbol(lowerCamelCase							,						lowerCamelCase)
            A_						:  str          =				[]
            # 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] == "##":
                        A_						:  Optional[Any]          =				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      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      # 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:
            A_						:  Optional[int]          =				f.readlines()
      A_						:  Union[str, Any]          =				[line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      A_						:  Optional[Any]          =				LTP(args.ltp)  # faster in GPU device
      A_						:  Dict          =				BertTokenizer.from_pretrained(args.bert)

      A_						:  str          =				prepare_ref(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

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


if __name__ == "__main__":
     __magic_name__			=      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',
     )

     __magic_name__			=      parser.parse_args()
     main(args)
