





"""simple docstring"""




import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import BertGenerationDecoder, BertGenerationEncoder




class 			_a						:






				def __init__(			self		:   List[str],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Optional[int]=1_3,			lowerCAmelCase__		:   List[Any]=7,			lowerCAmelCase__		:   Optional[int]=True,			lowerCAmelCase__		:   Tuple=True,			lowerCAmelCase__		:   List[str]=9_9,			lowerCAmelCase__		:   Optional[Any]=3_2,			lowerCAmelCase__		:   List[Any]=5,			lowerCAmelCase__		:   Optional[int]=4,			lowerCAmelCase__		:   Tuple=3_7,			lowerCAmelCase__		:   Any="gelu",			lowerCAmelCase__		:   str=0.1,			lowerCAmelCase__		:   Union[str, Any]=0.1,			lowerCAmelCase__		:   Optional[Any]=5_0,			lowerCAmelCase__		:   List[Any]=0.02,			lowerCAmelCase__		:   List[str]=True,			lowerCAmelCase__		:   int=None,			)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   parent
										_UpperCamelCase      : int         =   batch_size
										_UpperCamelCase      : List[Any]         =   seq_length
										_UpperCamelCase      : List[str]         =   is_training
										_UpperCamelCase      : Union[str, Any]         =   use_input_mask
										_UpperCamelCase      : Dict         =   vocab_size
										_UpperCamelCase      : str         =   hidden_size
										_UpperCamelCase      : Optional[int]         =   num_hidden_layers
										_UpperCamelCase      : Optional[Any]         =   num_attention_heads
										_UpperCamelCase      : Any         =   intermediate_size
										_UpperCamelCase      : Tuple         =   hidden_act
										_UpperCamelCase      : Any         =   hidden_dropout_prob
										_UpperCamelCase      : int         =   attention_probs_dropout_prob
										_UpperCamelCase      : List[str]         =   max_position_embeddings
										_UpperCamelCase      : str         =   initializer_range
										_UpperCamelCase      : Optional[int]         =   use_labels
										_UpperCamelCase      : int         =   scope






				def     snake_case     (			self		:   Optional[int]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   ids_tensor([self.batch_size, self.seq_length],			self.vocab_size							)

										_UpperCamelCase      : Optional[int]         =   None
										if self.use_input_mask:
																_UpperCamelCase      : List[str]         =   random_attention_mask([self.batch_size, self.seq_length]							)

										if self.use_labels:
																_UpperCamelCase      : Optional[Any]         =   ids_tensor([self.batch_size, self.seq_length],			self.vocab_size							)

										_UpperCamelCase      : Optional[int]         =   self.get_config()

										return config, input_ids, input_mask, token_labels






				def     snake_case     (			self		:   str							)     ->				List[str]:




										'''simple docstring'''



										return BertGenerationConfig(
										    vocab_size=self.vocab_size,			hidden_size=self.hidden_size,			num_hidden_layers=self.num_hidden_layers,			num_attention_heads=self.num_attention_heads,			intermediate_size=self.intermediate_size,			hidden_act=self.hidden_act,			hidden_dropout_prob=self.hidden_dropout_prob,			attention_probs_dropout_prob=self.attention_probs_dropout_prob,			max_position_embeddings=self.max_position_embeddings,			is_decoder=lowerCAmelCase__,			initializer_range=self.initializer_range,			)






				def     snake_case     (			self		:   Tuple							)     ->				Optional[int]:




										'''simple docstring'''



										(
										    (
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							
										)      : Optional[Any]         =   self.prepare_config_and_inputs()

										_UpperCamelCase      : List[Any]         =   True
										_UpperCamelCase      : Optional[Any]         =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
										_UpperCamelCase      : Dict         =   ids_tensor([self.batch_size, self.seq_length],			vocab_size=2							)

										return (
										    config,
										    input_ids,
										    input_mask,
										    token_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   Dict,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Optional[Any],			**lowerCAmelCase__		:   Dict,			)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   BertGenerationEncoder(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : List[Any]         =   model(lowerCAmelCase__,			attention_mask=lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   model(lowerCAmelCase__							)
										self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)							)






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Optional[Any],			**lowerCAmelCase__		:   int,			)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   True
										_UpperCamelCase      : Union[str, Any]         =   BertGenerationEncoder(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : List[str]         =   model(
										    lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			encoder_attention_mask=lowerCAmelCase__,			)
										_UpperCamelCase      : Optional[Any]         =   model(
										    lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			)
										self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)							)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Dict,			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[Any],			**lowerCAmelCase__		:   List[str],			)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   True
										_UpperCamelCase      : List[Any]         =   True
										_UpperCamelCase      : str         =   BertGenerationDecoder(config=lowerCAmelCase__							).to(lowerCAmelCase__							).eval()

										# first forward pass
										_UpperCamelCase      : List[Any]         =   model(
										    lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			encoder_attention_mask=lowerCAmelCase__,			use_cache=lowerCAmelCase__,			)
										_UpperCamelCase      : str         =   outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										_UpperCamelCase      : Union[str, Any]         =   ids_tensor((self.batch_size, 3),			config.vocab_size							)
										_UpperCamelCase      : Any         =   ids_tensor((self.batch_size, 3),			vocab_size=2							)

										# append to next input_ids and
										_UpperCamelCase      : List[str]         =   torch.cat([input_ids, next_tokens],			dim=-1							)
										_UpperCamelCase      : Tuple         =   torch.cat([input_mask, next_mask],			dim=-1							)

										_UpperCamelCase      : Optional[Any]         =   model(
										    lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			encoder_attention_mask=lowerCAmelCase__,			output_hidden_states=lowerCAmelCase__,			)['''hidden_states'''][0]
										_UpperCamelCase      : Any         =   model(
										    lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			encoder_hidden_states=lowerCAmelCase__,			encoder_attention_mask=lowerCAmelCase__,			past_key_values=lowerCAmelCase__,			output_hidden_states=lowerCAmelCase__,			)['''hidden_states'''][0]

										# select random slice
										_UpperCamelCase      : int         =   ids_tensor((1,),			output_from_past.shape[-1]							).item()
										_UpperCamelCase      : List[str]         =   output_from_no_past[:, -3:, random_slice_idx].detach()
										_UpperCamelCase      : Dict         =   output_from_past[:, :, random_slice_idx].detach()

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowerCAmelCase__,			lowerCAmelCase__,			atol=1e-3							)							)






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Dict,			*lowerCAmelCase__		:   Dict,			)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   BertGenerationDecoder(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase      : Optional[Any]         =   model(lowerCAmelCase__,			attention_mask=lowerCAmelCase__,			labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size)							)






				def     snake_case     (			self		:   str							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : List[str]         =   self.prepare_config_and_inputs()
										_UpperCamelCase      : int         =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_torch
class 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
				UpperCamelCase								=   (BertGenerationDecoder,) if is_torch_available() else ()
				UpperCamelCase								=   (
				    {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
				    if is_torch_available()
				    else {}
				)






				def     snake_case     (			self		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   BertGenerationEncoderTester(self							)
										_UpperCamelCase      : str         =   ConfigTester(self,			config_class=lowerCAmelCase__,			hidden_size=3_7							)






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def     snake_case     (			self		:   int							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__							)






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : str         =   self.model_tester.prepare_config_and_inputs()
										_UpperCamelCase      : Tuple         =   '''bert'''
										self.model_tester.create_and_check_model(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Any							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(*lowerCAmelCase__							)






				def     snake_case     (			self		:   Dict							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase__							)






				def     snake_case     (			self		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										(
										    (
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							(
										    _UpperCamelCase
										)       ,							
										)      : Union[str, Any]         =   self.model_tester.prepare_config_and_inputs_for_decoder()

										_UpperCamelCase      : Union[str, Any]         =   None

										self.model_tester.create_and_check_model_as_decoder(
										    lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			)






				def     snake_case     (			self		:   str							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase__							)






				@slow
				def     snake_case     (			self		:   Dict							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''							)
										self.assertIsNotNone(lowerCAmelCase__							)




@require_torch
class 			_a						(				unittest.TestCase    ):






				@slow
				def     snake_case     (			self		:   Tuple							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''							)
										_UpperCamelCase      : Any         =   torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]							)
										with torch.no_grad():
																_UpperCamelCase      : Optional[Any]         =   model(lowerCAmelCase__							)[0]
										_UpperCamelCase      : int         =   torch.Size([1, 8, 1_0_2_4]							)
										self.assertEqual(output.shape,			lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   torch.tensor(
										    [[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]]							)
										self.assertTrue(torch.allclose(output[:, :3, :3],			lowerCAmelCase__,			atol=1e-4							)							)




@require_torch
class 			_a						(				unittest.TestCase    ):






				@slow
				def     snake_case     (			self		:   Dict							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''							)
										_UpperCamelCase      : Union[str, Any]         =   torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]							)
										with torch.no_grad():
																_UpperCamelCase      : Tuple         =   model(lowerCAmelCase__							)[0]
										_UpperCamelCase      : Any         =   torch.Size([1, 8, 5_0_3_5_8]							)
										self.assertEqual(output.shape,			lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   torch.tensor(
										    [[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]]							)
										self.assertTrue(torch.allclose(output[:, :3, :3],			lowerCAmelCase__,			atol=1e-4							)							)










"""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)



