






"""simple docstring"""







import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
		from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
		from tensorflow.keras.optimizers import Adam







class  lowerCamelCase__ (	tf.keras.optimizers.schedules.LearningRateSchedule			):






				def __init__(			self     ,A     ,A     ,A     ,A = 1.0     ,A = None     ,):
										super().__init__()
										UpperCAmelCase          =	initial_learning_rate
										UpperCAmelCase          =	warmup_steps
										UpperCAmelCase          =	power
										UpperCAmelCase          =	decay_schedule_fn
										UpperCAmelCase          =	name






				def __call__(			self     ,A ):
										with tf.name_scope(self.name or """WarmUp""" ) as name:
																# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
																# learning rate will be `global_step/num_warmup_steps * init_lr`.
																UpperCAmelCase          =	tf.cast(A     ,tf.floataa )
																UpperCAmelCase          =	tf.cast(self.warmup_steps     ,tf.floataa )
																UpperCAmelCase          =	global_step_float / warmup_steps_float
																UpperCAmelCase          =	self.initial_learning_rate * tf.math.pow(A     ,self.power )
																return tf.cond(
																    global_step_float < warmup_steps_float     ,lambda: warmup_learning_rate     ,lambda: self.decay_schedule_fn(step - self.warmup_steps )     ,name=A     ,)






				def     _UpperCamelCase							(			self ):
										return {
										    "initial_learning_rate": self.initial_learning_rate,
										    "decay_schedule_fn": self.decay_schedule_fn,
										    "warmup_steps": self.warmup_steps,
										    "power": self.power,
										    "name": self.name,
										}







def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case = 0.0				,	_snake_case = 0.9				,	_snake_case = 0.999				,	_snake_case = 1E-8				,	_snake_case = None				,	_snake_case = None				,	_snake_case = 0.0				,	_snake_case = 1.0				,	_snake_case = None				,	):







						"""simple docstring"""






						UpperCAmelCase          =	tf.keras.optimizers.schedules.PolynomialDecay(
						    initial_learning_rate=_snake_case				,	decay_steps=num_train_steps - num_warmup_steps				,	end_learning_rate=init_lr * min_lr_ratio				,	power=_snake_case				,	)
						if num_warmup_steps:
												UpperCAmelCase          =	WarmUp(
												    initial_learning_rate=_snake_case				,	decay_schedule_fn=_snake_case				,	warmup_steps=_snake_case				,	)
						if weight_decay_rate > 0.0:
												UpperCAmelCase          =	AdamWeightDecay(
												    learning_rate=_snake_case				,	weight_decay_rate=_snake_case				,	beta_a=_snake_case				,	beta_a=_snake_case				,	epsilon=_snake_case				,	clipnorm=_snake_case				,	global_clipnorm=_snake_case				,	exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]				,	include_in_weight_decay=_snake_case				,	)
						else:
												UpperCAmelCase          =	tf.keras.optimizers.Adam(
												    learning_rate=_snake_case				,	beta_a=_snake_case				,	beta_a=_snake_case				,	epsilon=_snake_case				,	clipnorm=_snake_case				,	global_clipnorm=_snake_case				,	)
						# We return the optimizer and the LR scheduler in order to better track the
						# evolution of the LR independently of the optimizer.
						return optimizer, lr_schedule







class  lowerCamelCase__ (	snake_case			):






				def __init__(			self     ,A = 0.001     ,A = 0.9     ,A = 0.999     ,A = 1e-7     ,A = False     ,A = 0.0     ,A = None     ,A = None     ,A = "AdamWeightDecay"     ,**A     ,):
										super().__init__(A     ,A     ,A     ,A     ,A     ,A     ,**A )
										UpperCAmelCase          =	weight_decay_rate
										UpperCAmelCase          =	include_in_weight_decay
										UpperCAmelCase          =	exclude_from_weight_decay






				@classmethod
				def     _UpperCamelCase							(			cls     ,A ):
										UpperCAmelCase          =	{"""WarmUp""": WarmUp}
										return super(A     ,cls ).from_config(A     ,custom_objects=A )






				def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
										super(A     ,self )._prepare_local(A     ,A     ,A )
										UpperCAmelCase          =	tf.constant(
										    self.weight_decay_rate     ,name="""adam_weight_decay_rate""" )






				def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
										UpperCAmelCase          =	self._do_use_weight_decay(var.name )
										if do_decay:
																return var.assign_sub(
																    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""]     ,use_locking=self._use_locking     ,)
										return tf.no_op()






				def     _UpperCamelCase							(			self     ,A     ,A=None     ,**A ):
										UpperCAmelCase      , UpperCAmelCase          =	list(zip(*A ) )
										return super(A     ,self ).apply_gradients(zip(A     ,A )     ,name=A     ,**A )






				def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
										if apply_state is None:
																return self._decayed_lr_t[var_dtype], {}

										UpperCAmelCase          =	apply_state or {}
										UpperCAmelCase          =	apply_state.get((var_device, var_dtype) )
										if coefficients is None:
																UpperCAmelCase          =	self._fallback_apply_state(A     ,A )
																UpperCAmelCase          =	coefficients

										return coefficients["lr_t"], {"apply_state": apply_state}






				def     _UpperCamelCase							(			self     ,A     ,A     ,A=None ):
										UpperCAmelCase      , UpperCAmelCase          =	self._get_lr(var.device     ,var.dtype.base_dtype     ,A )
										UpperCAmelCase          =	self._decay_weights_op(A     ,A     ,A )
										with tf.control_dependencies([decay] ):
																return super(A     ,self )._resource_apply_dense(A     ,A     ,**A )






				def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A=None ):
										UpperCAmelCase      , UpperCAmelCase          =	self._get_lr(var.device     ,var.dtype.base_dtype     ,A )
										UpperCAmelCase          =	self._decay_weights_op(A     ,A     ,A )
										with tf.control_dependencies([decay] ):
																return super(A     ,self )._resource_apply_sparse(A     ,A     ,A     ,**A )






				def     _UpperCamelCase							(			self ):
										UpperCAmelCase          =	super().get_config()
										config.update({"""weight_decay_rate""": self.weight_decay_rate} )
										return config






				def     _UpperCamelCase							(			self     ,A ):
										if self.weight_decay_rate == 0:
																return False

										if self._include_in_weight_decay:
																for r in self._include_in_weight_decay:
																						if re.search(A     ,A ) is not None:
																												return True

										if self._exclude_from_weight_decay:
																for r in self._exclude_from_weight_decay:
																						if re.search(A     ,A ) is not None:
																												return False
										return True







class  lowerCamelCase__ (	snake_case			):






				def __init__(			self ):
										UpperCAmelCase          =	[]
										UpperCAmelCase          =	None






				@property
				def     _UpperCamelCase							(			self ):
										if self._accum_steps is None:
																UpperCAmelCase          =	tf.Variable(
																    tf.constant(0     ,dtype=tf.intaa )     ,trainable=A     ,synchronization=tf.VariableSynchronization.ON_READ     ,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,)

										return self._accum_steps.value()






				@property
				def     _UpperCamelCase							(			self ):
										if not self._gradients:
																raise ValueError("""The accumulator should be called first to initialize the gradients""" )
										return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






				def __call__(			self     ,A ):
										if not self._gradients:
																UpperCAmelCase          =	self.step  # Create the step variable.
																self._gradients.extend(
																    [
																        tf.Variable(
																            tf.zeros_like(A )     ,trainable=A     ,synchronization=tf.VariableSynchronization.ON_READ     ,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,)
																        if gradient is not None
																        else gradient
																        for gradient in gradients
																    ] )
										if len(A ) != len(self._gradients ):
																raise ValueError(F'''Expected {len(self._gradients )} gradients, but got {len(A )}''' )

										for accum_gradient, gradient in zip(self._gradients     ,A ):
																if accum_gradient is not None and gradient is not None:
																						accum_gradient.assign_add(A )

										self._accum_steps.assign_add(1 )






				def     _UpperCamelCase							(			self ):
										if not self._gradients:
																return
										self._accum_steps.assign(0 )
										for gradient in self._gradients:
																if gradient is not None:
																						gradient.assign(tf.zeros_like(A ) )











"""simple docstring"""







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


_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

_UpperCamelCase					      =  {
    """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""",
    },
}

_UpperCamelCase					      =  {"""vinai/bartpho-syllable""": 1024}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A     ,A     ,A="<s>"     ,A="</s>"     ,A="</s>"     ,A="<s>"     ,A="<unk>"     ,A="<pad>"     ,A="<mask>"     ,A = None     ,**A     ,):
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase          =	AddedToken(A     ,lstrip=A     ,rstrip=A ) if isinstance(A     ,A ) else mask_token

          UpperCAmelCase          =	{} if sp_model_kwargs is None else sp_model_kwargs

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

          UpperCAmelCase          =	vocab_file
          UpperCAmelCase          =	monolingual_vocab_file
          UpperCAmelCase          =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(str(A ) )

          # Load the reduced vocab

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

          UpperCAmelCase          =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(			self ):
          UpperCAmelCase          =	self.__dict__.copy()
          UpperCAmelCase          =	None
          UpperCAmelCase          =	self.sp_model.serialized_model_proto()
          return state






    def __setstate__(			self     ,A ):
          UpperCAmelCase          =	d

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

          UpperCAmelCase          =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






    def     _UpperCamelCase							(			self     ,A     ,A = None ):

          if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase          =	[self.cls_token_id]
          UpperCAmelCase          =	[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):

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

          if token_ids_a is None:
                return [1] + ([0] * len(A )) + [1]
          return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	[self.sep_token_id]
          UpperCAmelCase          =	[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     _UpperCamelCase							(			self ):
          return len(self.fairseq_ids_to_tokens )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






    def     _UpperCamelCase							(			self     ,A ):
          return self.sp_model.encode(A     ,out_type=A )






    def     _UpperCamelCase							(			self     ,A ):
          if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
          else:
                return self.unk_token_id






    def     _UpperCamelCase							(			self     ,A ):
          return self.fairseq_ids_to_tokens[index]






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	"""""".join(A ).replace(A     ,""" """ ).strip()
          return out_string






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          if not os.path.isdir(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]     ,)

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

          if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
              A ) and os.path.isfile(self.monolingual_vocab_file ):
                copyfile(self.monolingual_vocab_file     ,A )
          elif not os.path.isfile(self.monolingual_vocab_file ):
                with open(A     ,"""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(A )} \n''' )

          return out_vocab_file, out_monolingual_vocab_file



