


'''simple docstring'''
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        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=False							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=3							,      a_=4							,      a_=None							,      )    ->  Union[str, Any]:
					lowercase						:		List[str]   					=		parent
					lowercase						:		Any   					=		batch_size
					lowercase						:		List[Any]   					=		seq_length
					lowercase						:		Dict   					=		is_training
					lowercase						:		Union[str, Any]   					=		use_input_mask
					lowercase						:		Union[str, Any]   					=		use_token_type_ids
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		Dict   					=		vocab_size
					lowercase						:		Dict   					=		hidden_size
					lowercase						:		Any   					=		num_hidden_layers
					lowercase						:		Dict   					=		num_attention_heads
					lowercase						:		Union[str, Any]   					=		intermediate_size
					lowercase						:		Union[str, Any]   					=		hidden_act
					lowercase						:		str   					=		hidden_dropout_prob
					lowercase						:		Any   					=		attention_probs_dropout_prob
					lowercase						:		Optional[int]   					=		max_position_embeddings
					lowercase						:		Optional[int]   					=		type_vocab_size
					lowercase						:		Union[str, Any]   					=		type_sequence_label_size
					lowercase						:		Tuple   					=		initializer_range
					lowercase						:		int   					=		num_labels
					lowercase						:		Optional[int]   					=		num_choices
					lowercase						:		Optional[int]   					=		scope

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Optional[int]   					=		None
					if self.use_input_mask:
						lowercase						:		Union[str, Any]   					=		random_attention_mask([self.batch_size, self.seq_length]							)

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

					lowercase						:		int   					=		None
					lowercase						:		Union[str, Any]   					=		None
					lowercase						:		str   					=		None
					if self.use_labels:
						lowercase						:		str   					=		ids_tensor([self.batch_size]							,      self.type_sequence_label_size							)
						lowercase						:		Optional[int]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.num_labels							)
						lowercase						:		Any   					=		ids_tensor([self.batch_size]							,      self.num_choices							)

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

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

				def 	a__		(     self							)    ->  Optional[Any]:
					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=a_							,      initializer_range=self.initializer_range							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		LlamaModel(config=a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		int   					=		model(a_							,      attention_mask=a_							)
					lowercase						:		Any   					=		model(a_							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  Union[str, Any]:
					lowercase						:		List[Any]   					=		True
					lowercase						:		Optional[Any]   					=		LlamaModel(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		str   					=		model(
					    a_							,      attention_mask=a_							,      encoder_hidden_states=a_							,      encoder_attention_mask=a_							,      )
					lowercase						:		Dict   					=		model(
					    a_							,      attention_mask=a_							,      encoder_hidden_states=a_							,      )
					lowercase						:		Optional[int]   					=		model(a_							,      attention_mask=a_							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  Dict:
					lowercase						:		Any   					=		LlamaForCausalLM(config=a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		List[Any]   					=		model(a_							,      attention_mask=a_							,      labels=a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.vocab_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  List[str]:
					lowercase						:		Optional[Any]   					=		True
					lowercase						:		Optional[Any]   					=		True
					lowercase						:		Optional[int]   					=		LlamaForCausalLM(config=a_							)
					model.to(a_							)
					model.eval()

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

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

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

					lowercase						:		Optional[int]   					=		model(
					    a_							,      attention_mask=a_							,      encoder_hidden_states=a_							,      encoder_attention_mask=a_							,      output_hidden_states=a_							,      )["hidden_states"][0]
					lowercase						:		List[str]   					=		model(
					    a_							,      attention_mask=a_							,      encoder_hidden_states=a_							,      encoder_attention_mask=a_							,      past_key_values=a_							,      output_hidden_states=a_							,      )["hidden_states"][0]

					# select random slice
					lowercase						:		Optional[int]   					=		ids_tensor((1,)							,      output_from_past.shape[-1]							).item()
					lowercase						:		str   					=		output_from_no_past[:, -3:, random_slice_idx].detach()
					lowercase						:		str   					=		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(a_							,      a_							,      atol=1e-3							)							)


				def 	a__		(     self							)    ->  Any:
					lowercase						:		Optional[Any]   					=		self.prepare_config_and_inputs()
					(
					    (
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		
					)						:		List[Any]   					=		config_and_inputs
					lowercase						:		Optional[int]   					=		{"input_ids": input_ids, "attention_mask": input_mask}
					return config, inputs_dict




@require_torch
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
				_snake_case						       =				(LlamaForCausalLM,) if is_torch_available() else ()
				_snake_case						       =				(
				    {
				        '''feature-extraction''': LlamaModel,
				        '''text-classification''': LlamaForSequenceClassification,
				        '''text-generation''': LlamaForCausalLM,
				        '''zero-shot''': LlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		Dict   					=		LlamaModelTester(self							)
					lowercase						:		Dict   					=		ConfigTester(self							,      config_class=a_							,      hidden_size=3_7							)

				def 	a__		(     self							)    ->  Any:
					self.config_tester.run_common_tests()

				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[str]   					=		self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						lowercase						:		List[Any]   					=		type
						self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  int:
					lowercase					,		lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		str   					=		3
					lowercase						:		Union[str, Any]   					=		input_dict["input_ids"]
					lowercase						:		List[Any]   					=		input_ids.ne(1							).to(a_							)
					lowercase						:		Tuple   					=		ids_tensor([self.model_tester.batch_size]							,      self.model_tester.type_sequence_label_size							)
					lowercase						:		str   					=		LlamaForSequenceClassification(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Any   					=		model(a_							,      attention_mask=a_							,      labels=a_							)
					self.assertEqual(result.logits.shape							,      (self.model_tester.batch_size, self.model_tester.num_labels)							)

				def 	a__		(     self							)    ->  str:
					lowercase					,		lowercase						:		str   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		Any   					=		3
					lowercase						:		Optional[int]   					=		"single_label_classification"
					lowercase						:		List[str]   					=		input_dict["input_ids"]
					lowercase						:		List[Any]   					=		input_ids.ne(1							).to(a_							)
					lowercase						:		Optional[int]   					=		ids_tensor([self.model_tester.batch_size]							,      self.model_tester.type_sequence_label_size							)
					lowercase						:		Union[str, Any]   					=		LlamaForSequenceClassification(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		List[Any]   					=		model(a_							,      attention_mask=a_							,      labels=a_							)
					self.assertEqual(result.logits.shape							,      (self.model_tester.batch_size, self.model_tester.num_labels)							)

				def 	a__		(     self							)    ->  Any:
					lowercase					,		lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		List[Any]   					=		3
					lowercase						:		Dict   					=		"multi_label_classification"
					lowercase						:		Union[str, Any]   					=		input_dict["input_ids"]
					lowercase						:		Optional[int]   					=		input_ids.ne(1							).to(a_							)
					lowercase						:		Optional[Any]   					=		ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]							,      self.model_tester.type_sequence_label_size							).to(torch.float							)
					lowercase						:		Optional[Any]   					=		LlamaForSequenceClassification(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		int   					=		model(a_							,      attention_mask=a_							,      labels=a_							)
					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 	a__		(     self							)    ->  Any:
					pass


				@parameterized.expand([("linear",), ("dynamic",)]							)
				def 	a__		(     self							,      a_							)    ->  str:
					lowercase					,		lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		Optional[Any]   					=		ids_tensor([1, 1_0]							,      config.vocab_size							)
					lowercase						:		Tuple   					=		ids_tensor([1, int(config.max_position_embeddings * 1.5							)]							,      config.vocab_size							)

					set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
					lowercase						:		Union[str, Any]   					=		LlamaModel(a_							)
					original_model.to(a_							)
					original_model.eval()
					lowercase						:		Optional[int]   					=		original_model(a_							).last_hidden_state
					lowercase						:		Any   					=		original_model(a_							).last_hidden_state

					set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
					lowercase						:		Optional[int]   					=		{"type": scaling_type, "factor": 10.0}
					lowercase						:		List[Any]   					=		LlamaModel(a_							)
					scaled_model.to(a_							)
					scaled_model.eval()
					lowercase						:		List[Any]   					=		scaled_model(a_							).last_hidden_state
					lowercase						:		Any   					=		scaled_model(a_							).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(a_							,      a_							,      atol=1e-5							)							)
					else:
						self.assertFalse(torch.allclose(a_							,      a_							,      atol=1e-5							)							)

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




@require_torch
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
				@slow
				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Any   					=		[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
					lowercase						:		int   					=		LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"							,      device_map="auto"							)
					lowercase						:		Any   					=		model(torch.tensor([input_ids]							)							)
					# Expected mean on dim = -1
					lowercase						:		List[str]   					=		torch.tensor([[-6.65_50, -4.12_27, -4.98_59, -3.24_06, 0.82_62, -3.00_33, 1.29_64, -3.36_99]]							)
					torch.testing.assert_close(out.mean(-1							)							,      a_							,      atol=1e-2							,      rtol=1e-2							)
					# slicing logits[0, 0, 0:30]
					# fmt: off
					lowercase						:		Optional[int]   					=		torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,]							)
					# fmt: on
					torch.testing.assert_close(out[0, 0, :3_0]							,      a_							,      atol=1e-5							,      rtol=1e-5							)

				@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
				@slow
				def 	a__		(     self							)    ->  int:
					lowercase						:		Any   					=		[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
					lowercase						:		str   					=		LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"							,      device_map="auto"							)
					lowercase						:		Optional[int]   					=		model(torch.tensor(a_							)							)
					# Expected mean on dim = -1
					lowercase						:		str   					=		torch.tensor([[-2.06_22, -1.27_94, -1.16_38, -0.97_88, -1.46_03, -1.02_38, -1.78_93, -1.44_11]]							)
					torch.testing.assert_close(out.mean(-1							)							,      a_							,      atol=1e-2							,      rtol=1e-2							)
					# slicing logits[0, 0, 0:30]
					# fmt: off
					lowercase						:		Any   					=		torch.tensor([-8.14_06, -8.05_47, 2.74_61, -1.23_44, -0.14_48, -1.82_62, -1.00_20, -1.81_54, -1.68_95, -1.85_16, -2.35_74, -0.92_77, 3.75_98, 6.57_42, -1.29_98, -0.11_77, -8.14_06, -2.96_88, -2.91_99, -3.16_99, -3.52_54, -2.35_55, -2.79_88, -3.41_41, -2.82_62, -4.51_95, -3.33_79, -3.31_64, -2.78_32, -3.02_73]							)
					# fmt: on
					torch.testing.assert_close(out[0, 0, :3_0]							,      a_							,      atol=1e-5							,      rtol=1e-5							)

				@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
				@slow
				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		Union[str, Any]   					=		[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
					lowercase						:		Tuple   					=		LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"							,      device_map="auto"							)
					lowercase						:		Tuple   					=		model(torch.tensor(a_							)							)
					# Expected mean on dim = -1
					lowercase						:		Dict   					=		torch.tensor([[-0.85_62, -1.85_20, -0.75_51, -0.41_62, -1.51_61, -1.20_38, -2.48_23, -2.32_54]]							)
					torch.testing.assert_close(out.mean(-1							)							,      a_							,      atol=1e-2							,      rtol=1e-2							)
					# slicing logits[0, 0, 0:30]
					# fmt: off
					lowercase						:		Union[str, Any]   					=		torch.tensor([-2.22_27, 4.88_28, 0.90_23, -0.45_78, -0.78_71, -0.10_33, -0.62_21, -0.57_86, -0.78_03, -1.06_74, -1.29_20, -0.15_70, 0.80_08, 2.07_23, -0.94_97, 0.27_71, -2.22_27, -0.76_12, -1.43_46, -1.20_61, -1.64_26, -0.30_00, -0.71_39, -1.19_34, -1.86_91, -1.69_73, -1.59_47, -1.27_05, -0.35_23, -0.55_13]							)
					# fmt: on
					torch.testing.assert_close(out.mean(-1							)							,      a_							,      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 	a__		(     self							)    ->  Dict:
					lowercase						:		Tuple   					=		[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
					lowercase						:		int   					=		LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"							,      device_map="auto"							)
					lowercase						:		Tuple   					=		model(torch.tensor(a_							)							)

					lowercase						:		Tuple   					=		torch.tensor(
					    [[-4.23_27, -3.33_60, -4.66_65, -4.76_31, -1.81_80, -3.41_70, -1.42_11, -3.18_10]]							,      dtype=torch.floataa							)
					torch.testing.assert_close(out.mean(-1							)							,      a_							,      atol=1e-2							,      rtol=1e-2							)
					# fmt: off
					lowercase						:		Optional[Any]   					=		torch.tensor([-9.49_22, -3.95_51, 1.79_98, -5.67_58, -5.10_55, -5.89_84, -4.83_20, -6.80_86, -6.53_91, -5.61_72, -5.58_20, -5.53_52, 1.78_81, 3.62_89, -6.51_17, -3.47_85, -9.50_00, -6.03_52, -6.81_25, -6.01_95, -6.68_36, -5.47_27, -6.28_12, -6.03_91, -7.33_98, -7.42_97, -7.48_44, -6.58_20, -5.87_89, -5.53_12]							)
					# fmt: on
					torch.testing.assert_close(out[0, 0, :3_0]							,      a_							,      atol=1e-5							,      rtol=1e-5							)


				@unittest.skip("Model is curently gated"							)
				@slow
				def 	a__		(     self							)    ->  str:
					lowercase						:		Optional[int]   					=		"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						:		Optional[Any]   					=		"Simply put, the theory of relativity states that "
					lowercase						:		int   					=		LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"							)
					lowercase						:		Union[str, Any]   					=		tokenizer.encode(a_							,      return_tensors="pt"							)
					lowercase						:		Optional[Any]   					=		LlamaForCausalLM.from_pretrained(
					    "meta-llama/Llama-2-13b-chat-hf"							,      device_map="sequential"							,      use_safetensors=a_							)

					# greedy generation outputs
					lowercase						:		Optional[int]   					=		model.generate(a_							,      max_new_tokens=6_4							,      top_p=a_							,      temperature=1							,      do_sample=a_							)
					lowercase						:		int   					=		tokenizer.decode(generated_ids[0]							,      skip_special_tokens=a_							)
					self.assertEqual(a_							,      a_							)






'''simple docstring'''
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def 	_A  (       A							)  ->				int:
	# 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  (       A							)  ->				List[Any]:
	# word like '180' or '身高' or '神'
	for char in word:
		lowercase						:		int   					=		ord(A							)
		if not _is_chinese_char(A							):
			return 0
	return 1




def 	_A  (       A							)  ->				Dict:
	lowercase						:		Any   					=		set()

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




def 	_A  (       A							,A							)  ->				Tuple:
	if not chinese_word_set:
		return bert_tokens
	lowercase						:		Optional[Any]   					=		max([len(A							) for w in chinese_word_set]							)

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




def 	_A  (       A							,A							,A							)  ->				List[Any]:
	lowercase						:		List[Any]   					=		[]

	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		ltp_tokenizer.seg(lines[i : i + 1_0_0]							)[0]
		lowercase						:		Dict   					=		[get_chinese_word(A							) for r in res]
		ltp_res.extend(A							)
	assert len(A							) == len(A							)

	lowercase						:		List[str]   					=		[]
	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		bert_tokenizer(lines[i : i + 1_0_0]							,add_special_tokens=A							,truncation=A							,max_length=5_1_2							)
		bert_res.extend(res["input_ids"]							)
	assert len(A							) == len(A							)

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

	assert len(A							) == len(A							)

	return ref_ids




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

	lowercase						:		List[str]   					=		prepare_ref(A							,A							,A							)

	with open(args.save_path							,"w"							,encoding="utf-8"							) as f:
		lowercase						:		Optional[int]   					=		[json.dumps(A							) + "\n" for ref in ref_ids]
		f.writelines(A							)


if __name__ == "__main__":
					lowerCAmelCase   : List[Any]             =				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""")

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


