


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case							: Any      =   logging.get_logger(__name__)

snake_case							: int      =   '''▁'''

snake_case							: int      =   {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

snake_case							: Optional[Any]      =   {
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

snake_case							: Tuple      =   {'''vinai/bartpho-syllable''': 10_24}







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Tuple					   =    VOCAB_FILES_NAMES
					UpperCAmelCase__      :					int					   =    PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__      :					int					   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__      :					Optional[int]					   =    ['''input_ids''', '''attention_mask''']
					def __init__(   self     :List[Any]    ,__snake_case     :List[str]    ,__snake_case     :int    ,__snake_case     :Dict="<s>"    ,__snake_case     :Union[str, Any]="</s>"    ,__snake_case     :Dict="</s>"    ,__snake_case     :List[str]="<s>"    ,__snake_case     :str="<unk>"    ,__snake_case     :Union[str, Any]="<pad>"    ,__snake_case     :int="<mask>"    ,__snake_case     :Optional[Dict[str, Any]] = None    ,**__snake_case     :str    ,)      ->					None:
												# Mask token behave like a normal word, i.e. include the space before it
												a__    =  AddedToken(__snake_case    ,lstrip=__snake_case    ,rstrip=__snake_case   ) if isinstance(__snake_case    ,__snake_case   ) else mask_token

												a__    =  {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=__snake_case    ,eos_token=__snake_case    ,unk_token=__snake_case    ,sep_token=__snake_case    ,cls_token=__snake_case    ,pad_token=__snake_case    ,mask_token=__snake_case    ,sp_model_kwargs=self.sp_model_kwargs    ,**__snake_case    ,)

												a__    =  vocab_file
												a__    =  monolingual_vocab_file
												a__    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(str(__snake_case   )   )

												# Load the reduced vocab

												# Keep order of special tokens for backward compatibility
												a__    =  {}
												a__    =  0
												for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
																			if str(__snake_case   ) not in self.fairseq_tokens_to_ids:
																										a__    =  cnt
																										cnt += 1
												with open(__snake_case    ,'r'    ,encoding='utf-8'   ) as f:
																			for line in f.readlines():
																										a__    =  line.strip().split()[0]
																										a__    =  len(self.fairseq_tokens_to_ids   )
												if str(__snake_case   ) not in self.fairseq_tokens_to_ids:
																			a__    =  len(self.fairseq_tokens_to_ids   )

												a__    =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
					def __getstate__(   self     :Union[str, Any]   )      ->					Optional[Any]:
												a__    =  self.__dict__.copy()
												a__    =  None
												a__    =  self.sp_model.serialized_model_proto()
												return state
					def __setstate__(   self     :Any    ,__snake_case     :Dict   )      ->					str:
												a__    =  d

												# for backward compatibility
												if not hasattr(self    ,'sp_model_kwargs'   ):
																			a__    =  {}

												a__    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :List[int]    ,__snake_case     :Optional[List[int]] = None   )      ->					List[int]:

												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												a__    =  [self.cls_token_id]
												a__    =  [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :List[int]    ,__snake_case     :Optional[List[int]] = None    ,__snake_case     :bool = False   )      ->					List[int]:

												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__snake_case    ,token_ids_a=__snake_case    ,already_has_special_tokens=__snake_case   )

												if token_ids_a is None:
																			return [1] + ([0] * len(__snake_case   )) + [1]
												return [1] + ([0] * len(__snake_case   )) + [1, 1] + ([0] * len(__snake_case   )) + [1]
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :List[int]    ,__snake_case     :Optional[List[int]] = None   )      ->					List[int]:
												a__    =  [self.sep_token_id]
												a__    =  [self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep   ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]
					@property
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[Any]:
												return len(self.fairseq_ids_to_tokens   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					List[str]:
												a__    =  {self.convert_ids_to_tokens(__snake_case   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :str   )      ->					List[str]:
												return self.sp_model.encode(__snake_case    ,out_type=__snake_case   )
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :Optional[int]   )      ->					Union[str, Any]:
												if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
												else:
																			return self.unk_token_id
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Dict   )      ->					int:
												return self.fairseq_ids_to_tokens[index]
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :Any   )      ->					Any:
												a__    =  ''.join(__snake_case   ).replace(__snake_case    ,' '   ).strip()
												return out_string



					def 				lowerCamelCase__(   self     :str    ,__snake_case     :str    ,__snake_case     :Optional[str] = None   )      ->					Tuple[str]:
												if not os.path.isdir(__snake_case   ):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
																			return
												a__    =  os.path.join(
												    __snake_case    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
												a__    =  os.path.join(
												    __snake_case    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']    ,)

												if os.path.abspath(self.vocab_file   ) != os.path.abspath(__snake_case   ) and os.path.isfile(self.vocab_file   ):
																			copyfile(self.vocab_file    ,__snake_case   )
												elif not os.path.isfile(self.vocab_file   ):
																			with open(__snake_case    ,'wb'   ) as fi:
																										a__    =  self.sp_model.serialized_model_proto()
																										fi.write(__snake_case   )

												if os.path.abspath(self.monolingual_vocab_file   ) != os.path.abspath(
												    __snake_case   ) and os.path.isfile(self.monolingual_vocab_file   ):
																			copyfile(self.monolingual_vocab_file    ,__snake_case   )
												elif not os.path.isfile(self.monolingual_vocab_file   ):
																			with open(__snake_case    ,'w'    ,encoding='utf-8'   ) as fp:
																										for token in self.fairseq_tokens_to_ids:
																																	if token not in self.all_special_tokens:
																																								fp.write(F'{str(__snake_case   )} \n'   )

												return out_vocab_file, out_monolingual_vocab_file








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     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Any=1_0     ):
							a__    =  []
							for _ in range(__lowerCAmelCase     ):
														lrs.append(scheduler.get_lr()[0]     )
														scheduler.step()
							return lrs






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       List[str]=1_0     ):
							a__    =  []
							for step in range(__lowerCAmelCase     ):
														lrs.append(scheduler.get_lr()[0]     )
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												a__    =  os.path.join(__lowerCAmelCase      ,					'schedule.bin'     )
																												torch.save(scheduler.state_dict()      ,					__lowerCAmelCase     )

																												a__    =  torch.load(__lowerCAmelCase     )
																												scheduler.load_state_dict(__lowerCAmelCase     )
							return lrs







@require_torch
class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :List[Any]    ,__snake_case     :int    ,__snake_case     :Union[str, Any]   )      ->					int:
												self.assertEqual(len(__snake_case   )    ,len(__snake_case   )   )
												for a, b in zip(__snake_case    ,__snake_case   ):
																			self.assertAlmostEqual(__snake_case    ,__snake_case    ,delta=__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					str:
												a__    =  torch.tensor([0.1, -0.2, -0.1]    ,requires_grad=__snake_case   )
												a__    =  torch.tensor([0.4, 0.2, -0.5]   )
												a__    =  nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												a__    =  AdamW(params=[w]    ,lr=2E-1    ,weight_decay=0.0   )
												for _ in range(1_00   ):
																			a__    =  criterion(__snake_case    ,__snake_case   )
																			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 				lowerCamelCase__(   self     :Tuple   )      ->					int:
												a__    =  torch.tensor([0.1, -0.2, -0.1]    ,requires_grad=__snake_case   )
												a__    =  torch.tensor([0.4, 0.2, -0.5]   )
												a__    =  nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												a__    =  Adafactor(
												    params=[w]    ,lr=1E-2    ,eps=(1E-30, 1E-3)    ,clip_threshold=1.0    ,decay_rate=-0.8    ,betaa=__snake_case    ,weight_decay=0.0    ,relative_step=__snake_case    ,scale_parameter=__snake_case    ,warmup_init=__snake_case    ,)
												for _ in range(10_00   ):
																			a__    =  criterion(__snake_case    ,__snake_case   )
																			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  snake_case_     (unittest.TestCase   ):
					UpperCAmelCase__      :					str					   =    nn.Linear(5_0			,		5_0   ) if is_torch_available() else None
					UpperCAmelCase__      :					Dict					   =    AdamW(m.parameters()			,		lr=1_0.0   ) if is_torch_available() else None
					UpperCAmelCase__      :					Optional[Any]					   =    1_0
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Tuple    ,__snake_case     :int    ,__snake_case     :Any=None   )      ->					Optional[Any]:
												self.assertEqual(len(__snake_case   )    ,len(__snake_case   )   )
												for a, b in zip(__snake_case    ,__snake_case   ):
																			self.assertAlmostEqual(__snake_case    ,__snake_case    ,delta=__snake_case    ,msg=__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple   )      ->					List[Any]:
												a__    =  {'num_warmup_steps': 2, 'num_training_steps': 10}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												a__    =  {
												    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, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
												    ),
												    get_inverse_sqrt_schedule: (
												        {'num_warmup_steps': 2},
												        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
												    ),
												}

												for scheduler_func, data in scheds.items():
																			a__   ,      a__    =  data

																			a__    =  scheduler_func(self.optimizer    ,**__snake_case   )
																			self.assertEqual(len([scheduler.get_lr()[0]]   )    ,1   )
																			a__    =  unwrap_schedule(__snake_case    ,self.num_steps   )
																			self.assertListAlmostEqual(
																			    __snake_case    ,__snake_case    ,tol=1E-2    ,msg=F'failed for {scheduler_func} in normal scheduler'    ,)

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







class  snake_case_     :
					def __init__(   self     :Tuple    ,__snake_case     :str   )      ->					Any:
												a__    =  fn
					def __call__(   self     :List[str]    ,*__snake_case     :Optional[Any]    ,**__snake_case     :Optional[int]   )      ->					Union[str, Any]:
												return self.fn(*__snake_case    ,**__snake_case   )



					@classmethod
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union[str, Any]   )      ->					Dict:
												a__    =  list(map(self    ,scheduler.lr_lambdas   )   )




