






import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    Adafactor,
							    AdamW,
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_inverse_sqrt_schedule,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							)

def      __lowercase					( snake_case,  snake_case=1_0 ):







				"""simple docstring"""






				__magic_name__		:int        =      []
				for _ in range(snake_case ):
								lrs.append(scheduler.get_lr()[0] )
								scheduler.step()
				return lrs

def      __lowercase					( snake_case,  snake_case=1_0 ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      []
				for step in range(snake_case ):
								lrs.append(scheduler.get_lr()[0] )
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																__magic_name__		:Dict        =      os.path.join(snake_case,  '''schedule.bin''' )
																torch.save(scheduler.state_dict(),  snake_case )

																__magic_name__		:Tuple        =      torch.load(snake_case )
																scheduler.load_state_dict(snake_case )
				return lrs
@require_torch
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								self.assertEqual(len(_lowercase     )       ,					len(_lowercase     )     )
								for a, b in zip(_lowercase       ,					_lowercase     ):
												self.assertAlmostEqual(_lowercase       ,					_lowercase       ,					delta=_lowercase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      torch.tensor([0.1, -0.2, -0.1]       ,					requires_grad=_lowercase     )
								__magic_name__		:List[Any]        =      torch.tensor([0.4, 0.2, -0.5]     )
								__magic_name__		:List[Any]        =      nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								__magic_name__		:int        =      AdamW(params=[w]       ,					lr=2E-1       ,					weight_decay=0.0     )
								for _ in range(1_0_0     ):
												__magic_name__		:Optional[Any]        =      criterion(_lowercase       ,					_lowercase     )
												loss.backward()
												optimizer.step()
												w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
												w.grad.zero_()
								self.assertListAlmostEqual(w.tolist()       ,					[0.4, 0.2, -0.5]       ,					tol=1E-2     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      torch.tensor([0.1, -0.2, -0.1]       ,					requires_grad=_lowercase     )
								__magic_name__		:Optional[Any]        =      torch.tensor([0.4, 0.2, -0.5]     )
								__magic_name__		:Dict        =      nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								__magic_name__		:Optional[Any]        =      Adafactor(
								    params=[w]       ,					lr=1E-2       ,					eps=(1E-30, 1E-3)       ,					clip_threshold=1.0       ,					decay_rate=-0.8       ,					betaa=_lowercase       ,					weight_decay=0.0       ,					relative_step=_lowercase       ,					scale_parameter=_lowercase       ,					warmup_init=_lowercase       ,					)
								for _ in range(1_0_0_0     ):
												__magic_name__		:str        =      criterion(_lowercase       ,					_lowercase     )
												loss.backward()
												optimizer.step()
												w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
												w.grad.zero_()
								self.assertListAlmostEqual(w.tolist()       ,					[0.4, 0.2, -0.5]       ,					tol=1E-2     )







@require_torch
class 						lowerCamelCase_				(      unittest.TestCase ):
				a__					       =  nn.Linear(50			,				50 ) if is_torch_available() else None
				a__					       =  AdamW(m.parameters()			,				lr=10.0 ) if is_torch_available() else None
				a__					       =  10







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								self.assertEqual(len(_lowercase     )       ,					len(_lowercase     )     )
								for a, b in zip(_lowercase       ,					_lowercase     ):
												self.assertAlmostEqual(_lowercase       ,					_lowercase       ,					delta=_lowercase       ,					msg=_lowercase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      {'num_warmup_steps': 2, 'num_training_steps': 1_0}
								# schedulers doct format
								# function: (sched_args_dict, expected_learning_rates)
								__magic_name__		:Dict        =      {
								    get_constant_schedule: ({}, [10.0] * self.num_steps),
								    get_constant_schedule_with_warmup: (
								        {'num_warmup_steps': 4},
								        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
								    ),
								    get_linear_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
								    ),
								    get_cosine_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
								    ),
								    get_cosine_with_hard_restarts_schedule_with_warmup: (
								        {**common_kwargs, 'num_cycles': 2},
								        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
								    ),
								    get_polynomial_decay_schedule_with_warmup: (
								        {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
								        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
								    ),
								    get_inverse_sqrt_schedule: (
								        {'num_warmup_steps': 2},
								        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
								    ),
								}

								for scheduler_func, data in scheds.items():
												__magic_name__		:Union[str, Any]        =      data

												__magic_name__		:Tuple        =      scheduler_func(self.optimizer       ,					**_lowercase     )
												self.assertEqual(len([scheduler.get_lr()[0]]     )       ,					1     )
												__magic_name__		:Union[str, Any]        =      unwrap_schedule(_lowercase       ,					self.num_steps     )
												self.assertListAlmostEqual(
												    _lowercase       ,					_lowercase       ,					tol=1E-2       ,					msg=F'''failed for {scheduler_func} in normal scheduler'''       ,					)

												__magic_name__		:int        =      scheduler_func(self.optimizer       ,					**_lowercase     )
												if scheduler_func.__name__ != "get_constant_schedule":
																LambdaScheduleWrapper.wrap_scheduler(_lowercase     )  # wrap to test picklability of the schedule
												__magic_name__		:List[Any]        =      unwrap_and_save_reload_schedule(_lowercase       ,					self.num_steps     )
												self.assertListEqual(_lowercase       ,					_lowercase       ,					msg=F'''failed for {scheduler_func} in save and reload'''     )







class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      fn







				def __call__(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return self.fn(*_lowercase       ,					**_lowercase     )







				@classmethod
				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      list(map(self       ,					scheduler.lr_lambdas     )     )














import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def      __lowercase					( snake_case ):







				"""simple docstring"""






				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      __lowercase					( snake_case ):







				"""simple docstring"""






				for char in word:
								__magic_name__		:Union[str, Any]        =      ord(snake_case )
								if not _is_chinese_char(snake_case ):
												return 0
				return 1

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      set()

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

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if not chinese_word_set:
								return bert_tokens
				__magic_name__		:int        =      max([len(snake_case ) for w in chinese_word_set] )

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

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      []

				for i in range(0,  len(snake_case ),  1_0_0 ):
								__magic_name__		:List[str]        =      ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
								__magic_name__		:List[str]        =      [get_chinese_word(snake_case ) for r in res]
								ltp_res.extend(snake_case )
				assert len(snake_case ) == len(snake_case )

				__magic_name__		:Union[str, Any]        =      []
				for i in range(0,  len(snake_case ),  1_0_0 ):
								__magic_name__		:str        =      bert_tokenizer(lines[i : i + 1_0_0],  add_special_tokens=snake_case,  truncation=snake_case,  max_length=5_1_2 )
								bert_res.extend(res['''input_ids'''] )
				assert len(snake_case ) == len(snake_case )

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

				assert len(snake_case ) == len(snake_case )

				return ref_ids

def      __lowercase					( snake_case ):







				"""simple docstring"""






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

				__magic_name__		:Optional[int]        =      prepare_ref(snake_case,  snake_case,  snake_case )

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		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__      :			int							=		parser.parse_args()
					main(args)






