


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def     UpperCamelCase    (      _a		)					->							Optional[Any]:
					'''simple docstring'''





					if (
					    (cp >= 0X4e_00 and cp <= 0X9f_ff)
					    or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
					    or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
					    or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
					    or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
					    or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
					    or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
					    or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
					):  #
										return True

					return False




def     UpperCamelCase    (      _a		)					->							Tuple:
					'''simple docstring'''





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




def     UpperCamelCase    (      _a		)					->							Dict:
					'''simple docstring'''





					lowercase_       :Tuple     =       set()

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




def     UpperCamelCase    (      _a       ,		_a		)					->							Union[str, Any]:
					'''simple docstring'''





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

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




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
					'''simple docstring'''





					lowercase_       :List[Any]     =       []

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

					lowercase_       :Dict     =       []
					for i in range(0       ,		len(lowerCAmelCase_		)       ,		1_0_0		):
										lowercase_       :Dict     =       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_		)

					lowercase_       :Optional[Any]     =       []
					for input_ids, chinese_word in zip(lowerCAmelCase_       ,		lowerCAmelCase_		):
										lowercase_       :Dict     =       []
										for id in input_ids:
															lowercase_       :Optional[int]     =       bert_tokenizer._convert_id_to_token(lowerCAmelCase_		)
															input_tokens.append(lowerCAmelCase_		)
										lowercase_       :Dict     =       add_sub_symbol(lowerCAmelCase_       ,		lowerCAmelCase_		)
										lowercase_       :Tuple     =       []
										# 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_       :Tuple     =       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    (      _a		)					->							Dict:
					'''simple docstring'''





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

					lowercase_       :Tuple     =       prepare_ref(lowerCAmelCase_       ,		lowerCAmelCase_       ,		lowerCAmelCase_		)

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				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",
			)

			SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				parser.parse_args()
			main(args)





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							Dict:
     '''simple docstring'''





     assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
     lowercase_       :str     =       nn.Parameter(_a		)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
          lowercase_       :List[str]     =       nn.Parameter(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       np.asarray(weights[0]		)
     lowercase_       :str     =       np.asarray(weights[1]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[2]		)

     set_param(
         torch_layer.self_attention.query_key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       np.asarray(weights[0]		)
     lowercase_       :Tuple     =       np.asarray(weights[1]		)
     lowercase_       :List[Any]     =       np.asarray(weights[2]		)
     lowercase_       :Optional[int]     =       np.asarray(weights[3]		)

     set_param(
         torch_layer.self_attention.query       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :List[str]     =       weights[0][0][0]
     lowercase_       :Optional[Any]     =       np.asarray(layer_norm_a[0]		)
     lowercase_       :Any     =       np.asarray(layer_norm_a[1]		)
     set_param(
         torch_block.attention.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # lsh weights + output
     lowercase_       :int     =       weights[0][1]
     if len(_a		) < 4:
          set_layer_weights_in_torch_lsh(_a       ,		torch_block.attention       ,		_a		)
     else:
          set_layer_weights_in_torch_local(_a       ,		torch_block.attention       ,		_a		)

     # intermediate weighs
     lowercase_       :Optional[int]     =       weights[2][0][1][2]

     # Chunked Feed Forward
     if len(_a		) == 4:
          lowercase_       :Tuple     =       intermediate_weights[2]

     # layernorm 2
     lowercase_       :int     =       np.asarray(intermediate_weights[0][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[0][1]		)
     set_param(
         torch_block.feed_forward.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # intermediate dense
     lowercase_       :Any     =       np.asarray(intermediate_weights[1][0]		)
     lowercase_       :int     =       np.asarray(intermediate_weights[1][1]		)
     set_param(
         torch_block.feed_forward.dense.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)

     # intermediate out
     lowercase_       :Optional[int]     =       np.asarray(intermediate_weights[4][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[4][1]		)
     set_param(
         torch_block.feed_forward.output.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Any     =       torch_model.reformer

     # word embeds
     lowercase_       :int     =       np.asarray(weights[1]		)
     set_param(
         torch_model_reformer.embeddings.word_embeddings       ,		torch.tensor(_a		)       ,		)

     if isinstance(weights[3]       ,		_a		):
          lowercase_       :Union[str, Any]     =       torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights		)		):
               lowercase_       :Tuple     =       np.asarray(weights[3][emb_idx][0]		)
               assert (
                   position_embeddings.weights[emb_idx].shape == emb_weights.shape
               ), f"{position_embeddings[emb_idx]} emb does not match"
               lowercase_       :Optional[int]     =       nn.Parameter(torch.tensor(_a		)		)

     lowercase_       :Any     =       weights[5]
     assert len(torch_model_reformer.encoder.layers		) * 4 == len(
         _a		), "HF and trax model do not have the same number of layers"
     for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers		):
          lowercase_       :int     =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(_a       ,		_a       ,		_a		)

     # output layer norm
     lowercase_       :Optional[Any]     =       np.asarray(weights[7][0]		)
     lowercase_       :int     =       np.asarray(weights[7][1]		)
     set_param(
         torch_model_reformer.encoder.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # output embeddings
     lowercase_       :Optional[int]     =       np.asarray(weights[9][0]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[9][1]		)
     set_param(
         torch_model.lm_head.decoder       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							str:
     '''simple docstring'''





     lowercase_       :List[str]     =       ReformerConfig.from_json_file(_a		)
     print(f"Building PyTorch model from configuration: {config}"		)
     lowercase_       :List[str]     =       ReformerModelWithLMHead(_a		)

     with open(_a       ,		'''rb'''		) as f:
          lowercase_       :Dict     =       pickle.load(_a		)['''weights''']

     set_model_weights_in_torch(_a       ,		_a       ,		config.hidden_size		)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"		)
     torch.save(model.state_dict()       ,		_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained Reformer model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   SCREAMING_SNAKE_CASE       :		Dict         =				parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

