



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 		a__      (		_UpperCamelCase      :			Dict				):
   # 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 >= 0x4e00 and cp <= 0x9fff)
       or (cp >= 0x3400 and cp <= 0x4dbf)  #
       or (cp >= 0x20000 and cp <= 0x2a6df)  #
       or (cp >= 0x2a700 and cp <= 0x2b73f)  #
       or (cp >= 0x2b740 and cp <= 0x2b81f)  #
       or (cp >= 0x2b820 and cp <= 0x2ceaf)  #
       or (cp >= 0xf900 and cp <= 0xfaff)
       or (cp >= 0x2f800 and cp <= 0x2fa1f)  #
   ):  #
      return True

   return False





def 		a__      (		_UpperCamelCase      :			str				):
   # word like '180' or '身高' or '神'
   for char in word:
      __lowerCamelCase    				=     ord(_UpperCamelCase				)
      if not _is_chinese_char(_UpperCamelCase				):
         return 0
   return 1





def 		a__      (		_UpperCamelCase      :			List[str]				):
   __lowerCamelCase    				=     set()

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





def 		a__      (		_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			set()				):
   if not chinese_word_set:
      return bert_tokens
   __lowerCamelCase    				=     max([len(_UpperCamelCase				) for w in chinese_word_set]				)

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





def 		a__      (		_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			LTP			,_UpperCamelCase      :			BertTokenizer				):
   __lowerCamelCase    				=     []

   for i in range(0			,len(_UpperCamelCase				)			,1_00				):
      __lowerCamelCase    				=     ltp_tokenizer.seg(lines[i : i + 1_00]				)[0]
      __lowerCamelCase    				=     [get_chinese_word(_UpperCamelCase				) for r in res]
      ltp_res.extend(_UpperCamelCase				)
   assert len(_UpperCamelCase				) == len(_UpperCamelCase				)

   __lowerCamelCase    				=     []
   for i in range(0			,len(_UpperCamelCase				)			,1_00				):
      __lowerCamelCase    				=     bert_tokenizer(lines[i : i + 1_00]			,add_special_tokens=_UpperCamelCase			,truncation=_UpperCamelCase			,max_length=5_12				)
      bert_res.extend(res['''input_ids''']				)
   assert len(_UpperCamelCase				) == len(_UpperCamelCase				)

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

   assert len(_UpperCamelCase				) == len(_UpperCamelCase				)

   return ref_ids





def 		a__      (		_UpperCamelCase      :			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:
      __lowerCamelCase    				=     f.readlines()
   __lowerCamelCase    				=     [line.strip() for line in data if len(_UpperCamelCase				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   __lowerCamelCase    				=     LTP(args.ltp				)  # faster in GPU device
   __lowerCamelCase    				=     BertTokenizer.from_pretrained(args.bert				)

   __lowerCamelCase    				=     prepare_ref(_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase				)

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


if __name__ == "__main__":
  a_								=       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""")

  a_								=       parser.parse_args()
  main(args)











import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
  raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
  raise Exception("""requires fairseq < v2""")

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

a_								=       """Hello, World!"""
a_								=       """en_XX"""





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			str			,_UpperCamelCase      :			bool				):
   __lowerCamelCase    				=     Path('''data_bin'''				)
   __lowerCamelCase    				=     FairseqXmodModel.from_pretrained(
       model_name_or_path=str(Path(_UpperCamelCase				).parent				)			,checkpoint_file=Path(_UpperCamelCase				).name			,_name='''xmod_base'''			,arch='''xmod_base'''			,task='''multilingual_masked_lm'''			,data_name_or_path=str(_UpperCamelCase				)			,bpe='''sentencepiece'''			,sentencepiece_model=str(Path(_UpperCamelCase				).parent / '''sentencepiece.bpe.model'''				)			,src_dict=str(data_dir / '''dict.txt'''				)			,)
   xmod.eval()  # disable dropout
   print(_UpperCamelCase				)

   __lowerCamelCase    				=     xmod.model.encoder.sentence_encoder
   __lowerCamelCase    				=     XmodConfig(
       vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,hidden_size=xmod.cfg.model.encoder_embed_dim			,num_hidden_layers=xmod.cfg.model.encoder_layers			,num_attention_heads=xmod.cfg.model.encoder_attention_heads			,intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,max_position_embeddings=5_14			,type_vocab_size=1			,layer_norm_eps=1e-5			,pre_norm=xmod.cfg.model.encoder_normalize_before			,adapter_reduction_factor=getattr(xmod.cfg.model			,'''bottleneck'''			,2				)			,adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,ln_before_adapter=xmod.cfg.model.ln_before_adapter			,languages=xmod.cfg.model.languages			,)
   if classification_head:
      __lowerCamelCase    				=     xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

   print('''Our X-MOD config:'''			,_UpperCamelCase				)

   __lowerCamelCase    				=     XmodForSequenceClassification(_UpperCamelCase				) if classification_head else XmodForMaskedLM(_UpperCamelCase				)
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   __lowerCamelCase    				=     xmod_sent_encoder.embed_tokens.weight
   __lowerCamelCase    				=     xmod_sent_encoder.embed_positions.weight
   __lowerCamelCase    				=     torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c xmod doesn't use them.

   __lowerCamelCase    				=     xmod_sent_encoder.layernorm_embedding.weight
   __lowerCamelCase    				=     xmod_sent_encoder.layernorm_embedding.bias

   for i in range(config.num_hidden_layers				):
      # Encoder: start of layer
      __lowerCamelCase    				=     model.roberta.encoder.layer[i]
      __lowerCamelCase    				=     xmod_sent_encoder.layers[i]

      # self attention
      __lowerCamelCase    				=     layer.attention.self
      if not (
          xmod_layer.self_attn.k_proj.weight.data.shape
          == xmod_layer.self_attn.q_proj.weight.data.shape
          == xmod_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)				)
      ):
         raise AssertionError('''Dimensions of self-attention weights do not match.'''				)

      __lowerCamelCase    				=     xmod_layer.self_attn.q_proj.weight
      __lowerCamelCase    				=     xmod_layer.self_attn.q_proj.bias
      __lowerCamelCase    				=     xmod_layer.self_attn.k_proj.weight
      __lowerCamelCase    				=     xmod_layer.self_attn.k_proj.bias
      __lowerCamelCase    				=     xmod_layer.self_attn.v_proj.weight
      __lowerCamelCase    				=     xmod_layer.self_attn.v_proj.bias

      # self-attention output
      __lowerCamelCase    				=     layer.attention.output
      if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
         raise AssertionError('''Dimensions of self-attention output weights do not match.'''				)
      __lowerCamelCase    				=     xmod_layer.self_attn.out_proj.weight
      __lowerCamelCase    				=     xmod_layer.self_attn.out_proj.bias
      __lowerCamelCase    				=     xmod_layer.self_attn_layer_norm.weight
      __lowerCamelCase    				=     xmod_layer.self_attn_layer_norm.bias

      # intermediate
      __lowerCamelCase    				=     layer.intermediate
      if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
         raise AssertionError('''Dimensions of intermediate weights do not match.'''				)
      __lowerCamelCase    				=     xmod_layer.fca.weight
      __lowerCamelCase    				=     xmod_layer.fca.bias

      # output
      __lowerCamelCase    				=     layer.output
      if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
         raise AssertionError('''Dimensions of feed-forward weights do not match.'''				)
      __lowerCamelCase    				=     xmod_layer.fca.weight
      __lowerCamelCase    				=     xmod_layer.fca.bias
      __lowerCamelCase    				=     xmod_layer.final_layer_norm.weight
      __lowerCamelCase    				=     xmod_layer.final_layer_norm.bias
      if bert_output.adapter_layer_norm is not None:
         __lowerCamelCase    				=     xmod_layer.adapter_layer_norm.weight
         __lowerCamelCase    				=     xmod_layer.adapter_layer_norm.bias

      if sorted(bert_output.adapter_modules.keys()				) != sorted(xmod_layer.adapter_modules.keys()				):
         raise AssertionError('''Lists of language adapters do not match.'''				)
      for lang_code, adapter in xmod_layer.adapter_modules.items():
         __lowerCamelCase    				=     bert_output.adapter_modules[lang_code]
         __lowerCamelCase    				=     xmod_layer.adapter_modules[lang_code]
         __lowerCamelCase    				=     from_adapter.fca.weight
         __lowerCamelCase    				=     from_adapter.fca.bias
         __lowerCamelCase    				=     from_adapter.fca.weight
         __lowerCamelCase    				=     from_adapter.fca.bias

      # end of layer

   if xmod_sent_encoder.layer_norm is not None:
      __lowerCamelCase    				=     xmod_sent_encoder.layer_norm.weight
      __lowerCamelCase    				=     xmod_sent_encoder.layer_norm.bias

   if classification_head:
      __lowerCamelCase    				=     xmod.model.classification_heads['''mnli'''].dense.weight
      __lowerCamelCase    				=     xmod.model.classification_heads['''mnli'''].dense.bias
      __lowerCamelCase    				=     xmod.model.classification_heads['''mnli'''].out_proj.weight
      __lowerCamelCase    				=     xmod.model.classification_heads['''mnli'''].out_proj.bias
   else:
      # LM Head
      __lowerCamelCase    				=     xmod.model.encoder.lm_head.dense.weight
      __lowerCamelCase    				=     xmod.model.encoder.lm_head.dense.bias
      __lowerCamelCase    				=     xmod.model.encoder.lm_head.layer_norm.weight
      __lowerCamelCase    				=     xmod.model.encoder.lm_head.layer_norm.bias
      __lowerCamelCase    				=     xmod.model.encoder.lm_head.weight
      __lowerCamelCase    				=     xmod.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   __lowerCamelCase    				=     xmod.encode(_UpperCamelCase				).unsqueeze(0				)  # batch of size 1
   model.roberta.set_default_language(_UpperCamelCase				)

   __lowerCamelCase    				=     model(_UpperCamelCase				)[0]
   if classification_head:
      __lowerCamelCase    				=     xmod.model.classification_heads['''mnli'''](xmod.extract_features(_UpperCamelCase				)				)
   else:
      __lowerCamelCase    				=     xmod.model(_UpperCamelCase			,lang_id=[SAMPLE_LANGUAGE]				)[0]
   print(our_output.shape			,their_output.shape				)
   __lowerCamelCase    				=     torch.max(torch.abs(our_output - their_output				)				).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
   __lowerCamelCase    				=     torch.allclose(_UpperCamelCase			,_UpperCamelCase			,atol=1e-3				)
   print('''Do both models output the same tensors?'''			,'''🔥''' if success else '''💩'''				)
   if not success:
      raise Exception('''Something went wRoNg'''				)

   Path(_UpperCamelCase				).mkdir(parents=_UpperCamelCase			,exist_ok=_UpperCamelCase				)
   print(F"""Saving model to {pytorch_dump_folder_path}"""				)
   model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
  )
  a_								=       parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )






