
import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class 			A_  :




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case=13						,       snake_case=7						,       snake_case=True						,       snake_case=True						,       snake_case=False						,       snake_case=True						,       snake_case=99						,       snake_case=32						,       snake_case=5						,       snake_case=4						,       snake_case=37						,       snake_case="gelu"						,       snake_case=0.1						,       snake_case=0.1						,       snake_case=512						,       snake_case=16						,       snake_case=2						,       snake_case=0.02						,       snake_case=3						,       snake_case=4						,       snake_case=None						,       ):
		lowercase	    =       parent
		lowercase	    =       batch_size
		lowercase	    =       seq_length
		lowercase	    =       is_training
		lowercase	    =       use_input_mask
		lowercase	    =       use_token_type_ids
		lowercase	    =       use_labels
		lowercase	    =       vocab_size
		lowercase	    =       hidden_size
		lowercase	    =       num_hidden_layers
		lowercase	    =       num_attention_heads
		lowercase	    =       intermediate_size
		lowercase	    =       hidden_act
		lowercase	    =       hidden_dropout_prob
		lowercase	    =       attention_probs_dropout_prob
		lowercase	    =       max_position_embeddings
		lowercase	    =       type_vocab_size
		lowercase	    =       type_sequence_label_size
		lowercase	    =       initializer_range
		lowercase	    =       num_labels
		lowercase	    =       num_choices
		lowercase	    =       scope





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size			)

		lowercase	    =       None
		if self.use_input_mask:
			lowercase	    =       random_attention_mask([self.batch_size, self.seq_length]			)

		lowercase	    =       None
		if self.use_token_type_ids:
			lowercase	    =       ids_tensor([self.batch_size, self.seq_length]						,       self.type_vocab_size			)

		lowercase	    =       None
		lowercase	    =       None
		lowercase	    =       None
		if self.use_labels:
			lowercase	    =       ids_tensor([self.batch_size]						,       self.type_sequence_label_size			)
			lowercase	    =       ids_tensor([self.batch_size, self.seq_length]						,       self.num_labels			)
			lowercase	    =       ids_tensor([self.batch_size]						,       self.num_choices			)

		lowercase	    =       self.get_config()

		return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





	def       SCREAMING_SNAKE_CASE__		(			self			):
		return LlamaConfig(
		    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						,       type_vocab_size=self.type_vocab_size						,       is_decoder=snake_case						,       initializer_range=self.initializer_range						,       )





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		lowercase	    =       LlamaModel(config=snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case			)
		lowercase	    =       model(snake_case			)
		self.parent.assertEqual(result.last_hidden_state.shape						,       (self.batch_size, self.seq_length, self.hidden_size)			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		lowercase	    =       True
		lowercase	    =       LlamaModel(snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(
		    snake_case						,       attention_mask=snake_case						,       encoder_hidden_states=snake_case						,       encoder_attention_mask=snake_case						,       )
		lowercase	    =       model(
		    snake_case						,       attention_mask=snake_case						,       encoder_hidden_states=snake_case						,       )
		lowercase	    =       model(snake_case						,       attention_mask=snake_case			)
		self.parent.assertEqual(result.last_hidden_state.shape						,       (self.batch_size, self.seq_length, self.hidden_size)			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		lowercase	    =       LlamaForCausalLM(config=snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case						,       labels=snake_case			)
		self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.vocab_size)			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		lowercase	    =       True
		lowercase	    =       True
		lowercase	    =       LlamaForCausalLM(config=snake_case			)
		model.to(snake_case			)
		model.eval()

		# first forward pass
		lowercase	    =       model(
		    snake_case						,       attention_mask=snake_case						,       encoder_hidden_states=snake_case						,       encoder_attention_mask=snake_case						,       use_cache=snake_case						,       )
		lowercase	    =       outputs.past_key_values

		# create hypothetical multiple next token and extent to next_input_ids
		lowercase	    =       ids_tensor((self.batch_size, 3)						,       config.vocab_size			)
		lowercase	    =       ids_tensor((self.batch_size, 3)						,       vocab_size=2			)

		# append to next input_ids and
		lowercase	    =       torch.cat([input_ids, next_tokens]						,       dim=-1			)
		lowercase	    =       torch.cat([input_mask, next_mask]						,       dim=-1			)

		lowercase	    =       model(
		    snake_case						,       attention_mask=snake_case						,       encoder_hidden_states=snake_case						,       encoder_attention_mask=snake_case						,       output_hidden_states=snake_case						,       )['hidden_states'][0]
		lowercase	    =       model(
		    snake_case						,       attention_mask=snake_case						,       encoder_hidden_states=snake_case						,       encoder_attention_mask=snake_case						,       past_key_values=snake_case						,       output_hidden_states=snake_case						,       )['hidden_states'][0]

		# select random slice
		lowercase	    =       ids_tensor((1,)						,       output_from_past.shape[-1]			).item()
		lowercase	    =       output_from_no_past[:, -3:, random_slice_idx].detach()
		lowercase	    =       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(snake_case						,       snake_case						,       atol=1E-3			)			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.prepare_config_and_inputs()
		(
		    (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   (
		    lowercase
		)    ,   
		)	    =       config_and_inputs
		lowercase	    =       {'input_ids': input_ids, 'attention_mask': input_mask}
		return config, inputs_dict







@require_torch
class 			A_  (     __lowerCamelCase ,      __lowerCamelCase ,      __lowerCamelCase ,      unittest.TestCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Tuple									=			(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
	_UpperCamelCase	:	List[Any]									=			(LlamaForCausalLM,) if is_torch_available() else ()
	_UpperCamelCase	:	int									=			(
	    {
	        """feature-extraction""": LlamaModel,
	        """text-classification""": LlamaForSequenceClassification,
	        """text-generation""": LlamaForCausalLM,
	        """zero-shot""": LlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_UpperCamelCase	:	int									=			False
	_UpperCamelCase	:	int									=			False





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       LlamaModelTester(self			)
		lowercase	    =       ConfigTester(self						,       config_class=snake_case						,       hidden_size=37			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		self.config_tester.run_common_tests()





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.model_tester.prepare_config_and_inputs()
		for type in ["absolute", "relative_key", "relative_key_query"]:
			lowercase	    =       type
			self.model_tester.create_and_check_model(*snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase	    =       self.model_tester.prepare_config_and_inputs_for_common()
		lowercase	    =       3
		lowercase	    =       input_dict['input_ids']
		lowercase	    =       input_ids.ne(1			).to(snake_case			)
		lowercase	    =       ids_tensor([self.model_tester.batch_size]						,       self.model_tester.type_sequence_label_size			)
		lowercase	    =       LlamaForSequenceClassification(snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case						,       labels=snake_case			)
		self.assertEqual(result.logits.shape						,       (self.model_tester.batch_size, self.model_tester.num_labels)			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase	    =       self.model_tester.prepare_config_and_inputs_for_common()
		lowercase	    =       3
		lowercase	    =       'single_label_classification'
		lowercase	    =       input_dict['input_ids']
		lowercase	    =       input_ids.ne(1			).to(snake_case			)
		lowercase	    =       ids_tensor([self.model_tester.batch_size]						,       self.model_tester.type_sequence_label_size			)
		lowercase	    =       LlamaForSequenceClassification(snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case						,       labels=snake_case			)
		self.assertEqual(result.logits.shape						,       (self.model_tester.batch_size, self.model_tester.num_labels)			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase	    =       self.model_tester.prepare_config_and_inputs_for_common()
		lowercase	    =       3
		lowercase	    =       'multi_label_classification'
		lowercase	    =       input_dict['input_ids']
		lowercase	    =       input_ids.ne(1			).to(snake_case			)
		lowercase	    =       ids_tensor(
		    [self.model_tester.batch_size, config.num_labels]						,       self.model_tester.type_sequence_label_size			).to(torch.float			)
		lowercase	    =       LlamaForSequenceClassification(snake_case			)
		model.to(snake_case			)
		model.eval()
		lowercase	    =       model(snake_case						,       attention_mask=snake_case						,       labels=snake_case			)
		self.assertEqual(result.logits.shape						,       (self.model_tester.batch_size, self.model_tester.num_labels)			)





	@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'			)
	def       SCREAMING_SNAKE_CASE__		(			self			):
		pass





	@parameterized.expand([('linear',), ('dynamic',)]			)
	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase    ,   lowercase	    =       self.model_tester.prepare_config_and_inputs_for_common()
		lowercase	    =       ids_tensor([1, 10]						,       config.vocab_size			)
		lowercase	    =       ids_tensor([1, int(config.max_position_embeddings * 1.5			)]						,       config.vocab_size			)

		set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
		lowercase	    =       LlamaModel(snake_case			)
		original_model.to(snake_case			)
		original_model.eval()
		lowercase	    =       original_model(snake_case			).last_hidden_state
		lowercase	    =       original_model(snake_case			).last_hidden_state

		set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
		lowercase	    =       {'type': scaling_type, 'factor': 10.0}
		lowercase	    =       LlamaModel(snake_case			)
		scaled_model.to(snake_case			)
		scaled_model.eval()
		lowercase	    =       scaled_model(snake_case			).last_hidden_state
		lowercase	    =       scaled_model(snake_case			).last_hidden_state

		# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
		# maximum sequence length, so the outputs for the short input should match.
		if scaling_type == "dynamic":
			self.assertTrue(torch.allclose(snake_case						,       snake_case						,       atol=1E-5			)			)
		else:
			self.assertFalse(torch.allclose(snake_case						,       snake_case						,       atol=1E-5			)			)

		# The output should be different for long inputs
		self.assertFalse(torch.allclose(snake_case						,       snake_case						,       atol=1E-5			)			)







@require_torch
class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'			)
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
		lowercase	    =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'						,       device_map='auto'			)
		lowercase	    =       model(torch.tensor([input_ids]			)			)
		# Expected mean on dim = -1
		lowercase	    =       torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]			)
		torch.testing.assert_close(out.mean(-1			)						,       snake_case						,       atol=1E-2						,       rtol=1E-2			)
		# slicing logits[0, 0, 0:30]
		# fmt: off
		lowercase	    =       torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]			)
		# fmt: on
		torch.testing.assert_close(out[0, 0, :30]						,       snake_case						,       atol=1E-5						,       rtol=1E-5			)





	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'			)
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
		lowercase	    =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'						,       device_map='auto'			)
		lowercase	    =       model(torch.tensor(snake_case			)			)
		# Expected mean on dim = -1
		lowercase	    =       torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]			)
		torch.testing.assert_close(out.mean(-1			)						,       snake_case						,       atol=1E-2						,       rtol=1E-2			)
		# slicing logits[0, 0, 0:30]
		# fmt: off
		lowercase	    =       torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]			)
		# fmt: on
		torch.testing.assert_close(out[0, 0, :30]						,       snake_case						,       atol=1E-5						,       rtol=1E-5			)





	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'			)
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
		lowercase	    =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'						,       device_map='auto'			)
		lowercase	    =       model(torch.tensor(snake_case			)			)
		# Expected mean on dim = -1
		lowercase	    =       torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]			)
		torch.testing.assert_close(out.mean(-1			)						,       snake_case						,       atol=1E-2						,       rtol=1E-2			)
		# slicing logits[0, 0, 0:30]
		# fmt: off
		lowercase	    =       torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]			)
		# fmt: on
		torch.testing.assert_close(out.mean(-1			)						,       snake_case						,       atol=1E-2						,       rtol=1E-2			)





	@unittest.skip(
	    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'			)
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
		lowercase	    =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'						,       device_map='auto'			)
		lowercase	    =       model(torch.tensor(snake_case			)			)

		lowercase	    =       torch.tensor(
		    [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]						,       dtype=torch.floataa			)
		torch.testing.assert_close(out.mean(-1			)						,       snake_case						,       atol=1E-2						,       rtol=1E-2			)
		# fmt: off
		lowercase	    =       torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]			)
		# fmt: on
		torch.testing.assert_close(out[0, 0, :30]						,       snake_case						,       atol=1E-5						,       rtol=1E-5			)





	@unittest.skip('Model is curently gated'			)
	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
		lowercase	    =       'Simply put, the theory of relativity states that '
		lowercase	    =       LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'			)
		lowercase	    =       tokenizer.encode(snake_case						,       return_tensors='pt'			)
		lowercase	    =       LlamaForCausalLM.from_pretrained(
		    'meta-llama/Llama-2-13b-chat-hf'						,       device_map='sequential'						,       use_safetensors=snake_case			)

		# greedy generation outputs
		lowercase	    =       model.generate(snake_case						,       max_new_tokens=64						,       top_p=snake_case						,       temperature=1						,       do_sample=snake_case			)
		lowercase	    =       tokenizer.decode(generated_ids[0]						,       skip_special_tokens=snake_case			)
		self.assertEqual(snake_case						,       snake_case			)



"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers 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 >= 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       A_  (				__lowercase	):
 # word like '180' or '身高' or '神'
 for char in word:
  UpperCamelCase_     :					Union[str, Any]   	=ord(__lowercase	)
  if not _is_chinese_char(__lowercase	):
   return 0
 return 1




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=set()

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




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

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




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Tuple   	=[]

 for i in range(0						,	len(__lowercase	)						,	1_00	):
  UpperCamelCase_     :					Union[str, Any]   	=ltp_tokenizer.seg(lines[i : i + 1_00]	)[0]
  UpperCamelCase_     :					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	)						,	1_00	):
  UpperCamelCase_     :					int   	=bert_tokenizer(lines[i : i + 1_00]						,	add_special_tokens=__lowercase						,	truncation=__lowercase						,	max_length=5_12	)
  bert_res.extend(res['input_ids']	)
 assert len(__lowercase	) == len(__lowercase	)

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

 UpperCamelCase_     :					int   	=prepare_ref(__lowercase						,	__lowercase						,	__lowercase	)

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


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     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')

 __SCREAMING_SNAKE_CASE							=     parser.parse_args()
 main(args)
