"""simple docstring"""




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCamelCase_									=[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]





def 	a_    (							_lowercase       ):




						for pegasus_name, hf_name in PATTERNS:
												_UpperCamelCase      : Optional[Any]         =   k.replace(lowerCamelCase__	,						lowerCamelCase__       )
						return k





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[str]         =   DEFAULTS.copy()
						cfg_kwargs.update(lowerCamelCase__       )
						_UpperCamelCase      : str         =   PegasusConfig(**lowerCamelCase__       )
						_UpperCamelCase      : List[str]         =   PegasusForConditionalGeneration(lowerCamelCase__       )
						_UpperCamelCase      : Optional[Any]         =   torch_model.model.state_dict()
						_UpperCamelCase      : int         =   {}
						for k, v in tf_weights.items():
												_UpperCamelCase      : Optional[Any]         =   rename_state_dict_key(lowerCamelCase__       )
												if new_k not in sd:
																		raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""       )

												if "dense" in k or "proj" in new_k:
																		_UpperCamelCase      : Optional[Any]         =   v.T
												_UpperCamelCase      : Union[str, Any]         =   torch.tensor(lowerCamelCase__	,						dtype=sd[new_k].dtype       )
												assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
						# make sure embedding.padding_idx is respected
						_UpperCamelCase      : int         =   torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]       )
						_UpperCamelCase      : Optional[Any]         =   mapping['''shared.weight''']
						_UpperCamelCase      : List[Any]         =   mapping['''shared.weight''']
						_UpperCamelCase      : Dict         =   {k: torch.zeros_like(lowerCamelCase__       ) for k, v in sd.items() if k.endswith('''bias'''       ) and k not in mapping}
						mapping.update(**lowerCamelCase__       )
						_UpperCamelCase       ,							_UpperCamelCase      : Optional[Any]         =   torch_model.model.load_state_dict(lowerCamelCase__	,						strict=lowerCamelCase__       )
						_UpperCamelCase      : Union[str, Any]         =   [
						    k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
						]
						assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
						assert extra == [], F"""no matches found for the following tf keys {extra}"""
						return torch_model





def 	a_    (							_lowercase="./ckpt/aeslc/model.ckpt-32000"       ):
						_UpperCamelCase      : str         =   tf.train.list_variables(lowerCamelCase__       )
						_UpperCamelCase      : Tuple         =   {}
						_UpperCamelCase      : Union[str, Any]         =   ['''Adafactor''', '''global_step''']
						for name, shape in tqdm(lowerCamelCase__	,						desc='''converting tf checkpoint to dict'''       ):
												_UpperCamelCase      : Tuple         =   any(pat in name for pat in ignore_name       )
												if skip_key:
																		continue
												_UpperCamelCase      : List[Any]         =   tf.train.load_variable(lowerCamelCase__	,						lowerCamelCase__       )
												_UpperCamelCase      : List[str]         =   array
						return tf_weights





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Dict         =   Path(lowerCamelCase__       ).parent.name
						_UpperCamelCase      : Dict         =   task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
						_UpperCamelCase      : str         =   PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''	,						model_max_length=lowerCamelCase__       )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(lowerCamelCase__       )

						# convert model
						_UpperCamelCase      : str         =   get_tf_weights_as_numpy(lowerCamelCase__       )
						_UpperCamelCase      : str         =   task_specific_params[F"""summarization_{dataset}"""]
						if dataset == "large":
												_UpperCamelCase      : List[str]         =   task_specific_params
						_UpperCamelCase      : int         =   convert_pegasus(lowerCamelCase__	,						lowerCamelCase__       )
						torch_model.save_pretrained(lowerCamelCase__       )
						_UpperCamelCase      : Tuple         =   torch_model.state_dict()
						sd.pop('''model.decoder.embed_positions.weight'''       )
						sd.pop('''model.encoder.embed_positions.weight'''       )
						torch.save(lowerCamelCase__	,						Path(lowerCamelCase__       ) / '''pytorch_model.bin'''       )


if __name__ == "__main__":
						UpperCamelCase_									=argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
						parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
						UpperCamelCase_									=parser.parse_args()
						if args.save_dir is None:
												UpperCamelCase_									=Path(args.tf_ckpt_path).parent.name
												UpperCamelCase_									=os.path.join("""pegasus""", dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)










"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	a_    (							_lowercase       ):
						# 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_    (							_lowercase       ):
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCamelCase      : Dict         =   ord(_lowercase       )
												if not _is_chinese_char(_lowercase       ):
																		return 0
						return 1





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()

						for token in tokens:
												_UpperCamelCase      : int         =   len(_lowercase       ) > 1 and is_chinese(_lowercase       )
												if chinese_word:
																		word_set.add(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   list(_lowercase       )
						return word_list





def 	a_    (							_lowercase	,						_lowercase       ):
						if not chinese_word_set:
												return bert_tokens
						_UpperCamelCase      : Tuple         =   max([len(_lowercase       ) for w in chinese_word_set]       )

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []

						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   ltp_tokenizer.pipeline(lines[i : i + 100]	,						tasks=['''cws''']       ).cws
												_UpperCamelCase      : Optional[int]         =   [get_chinese_word(_lowercase       ) for r in res]
												ltp_res.extend(_lowercase       )
						assert len(_lowercase       ) == len(_lowercase       )

						_UpperCamelCase      : Dict         =   []
						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   bert_tokenizer(lines[i : i + 100]	,						add_special_tokens=_lowercase	,						truncation=_lowercase	,						max_length=512       )
												bert_res.extend(res['''input_ids''']       )
						assert len(_lowercase       ) == len(_lowercase       )

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

						assert len(_lowercase       ) == len(_lowercase       )

						return ref_ids





def 	a_    (							_lowercase       ):
						# 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      : Union[str, Any]         =   f.readlines()
						_UpperCamelCase      : Tuple         =   [line.strip() for line in data if len(_lowercase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_UpperCamelCase      : List[Any]         =   LTP(args.ltp       )  # faster in GPU device
						_UpperCamelCase      : int         =   BertTokenizer.from_pretrained(args.bert       )

						_UpperCamelCase      : List[str]         =   prepare_ref(_lowercase	,						_lowercase	,						_lowercase       )

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


if __name__ == "__main__":
						UpperCamelCase_									=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""",
						)

						UpperCamelCase_									=parser.parse_args()
						main(args)



