


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


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= '▁'

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

lowerCAmelCase__      		= {
    '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',
    },
}

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







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						)			->				None:
            # Mask token behave like a normal word, i.e. include the space before it
            _A      :       Optional[int]				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else mask_token

            _A      :       Optional[Any]				    = {} if sp_model_kwargs is None else sp_model_kwargs

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

            _A      :       Tuple				    = vocab_file
            _A      :       Tuple				    = monolingual_vocab_file
            _A      :       List[Any]				    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
            self.sp_model.Load(str(__lowerCamelCase))

            # Load the reduced vocab

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

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


     def __getstate__(						self)			->				List[Any]:
            _A      :       Dict				    = self.__dict__.copy()
            _A      :       int				    = None
            _A      :       Union[str, Any]				    = self.sp_model.serialized_model_proto()
            return state


     def __setstate__(						self ,						__lowerCamelCase)			->				List[Any]:
            _A      :       List[str]				    = d

            # for backward compatibility
            if not hasattr(self ,						"sp_model_kwargs"):
                   _A      :       Optional[int]				    = {}

            _A      :       List[Any]				    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
            self.sp_model.LoadFromSerializedProto(self.sp_model_proto)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = False)			->				List[int]:

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

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       str				    = [self.sep_token_id]
            _A      :       int				    = [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)			->				int:
            return len(self.fairseq_ids_to_tokens)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Optional[Any]				    = {self.convert_ids_to_tokens(__lowerCamelCase): i for i in range(self.vocab_size)}
            vocab.update(self.added_tokens_encoder)
            return vocab


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[str]:
            return self.sp_model.encode(__lowerCamelCase ,						out_type=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				str:
            if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
            else:
                   return self.unk_token_id


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[int]:
            return self.fairseq_ids_to_tokens[index]


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            _A      :       Optional[Any]				    = "".join(__lowerCamelCase).replace(__lowerCamelCase ,						" ").strip()
            return out_string


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                   return
            _A      :       Union[str, Any]				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
            _A      :       Optional[int]				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"] ,						)

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

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

            return out_vocab_file, out_monolingual_vocab_file





"""simple docstring"""






from ....configuration_utils import PretrainedConfig
from ....utils import logging


__snake_case							:   List[Any]      =    logging.get_logger(__name__)

__snake_case							:   Any      =    {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'mctct'

	def __init__(     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		str=8065       ,			_SCREAMING_SNAKE_CASE:		str=1536       ,			_SCREAMING_SNAKE_CASE:		str=36       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=6144       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=4       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=384       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=920       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1e-5       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0.3       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="relu"       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=0.02       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.3       ,			_SCREAMING_SNAKE_CASE:		Dict=0.3       ,			_SCREAMING_SNAKE_CASE:		List[Any]=1       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0       ,			_SCREAMING_SNAKE_CASE:		List[str]=2       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.3       ,			_SCREAMING_SNAKE_CASE:		Dict=1       ,			_SCREAMING_SNAKE_CASE:		int=(7,)       ,			_SCREAMING_SNAKE_CASE:		str=(3,)       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=80       ,			_SCREAMING_SNAKE_CASE:		Tuple=1       ,			_SCREAMING_SNAKE_CASE:		Dict=None       ,			_SCREAMING_SNAKE_CASE:		Tuple="sum"       ,			_SCREAMING_SNAKE_CASE:		List[str]=False       ,			**_SCREAMING_SNAKE_CASE:		Tuple       ,			)				->       Tuple:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE       ,			pad_token_id=_SCREAMING_SNAKE_CASE       ,			bos_token_id=_SCREAMING_SNAKE_CASE       ,			eos_token_id=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       vocab_size
				__lowerCAmelCase      :  str												=       hidden_size
				__lowerCAmelCase      :  str												=       num_hidden_layers
				__lowerCAmelCase      :  str												=       intermediate_size
				__lowerCAmelCase      :  List[Any]												=       num_attention_heads
				__lowerCAmelCase      :  Dict												=       attention_head_dim
				__lowerCAmelCase      :  Optional[int]												=       max_position_embeddings
				__lowerCAmelCase      :  str												=       layer_norm_eps
				__lowerCAmelCase      :  Tuple												=       layerdrop
				__lowerCAmelCase      :  str												=       hidden_act
				__lowerCAmelCase      :  List[Any]												=       initializer_range
				__lowerCAmelCase      :  int												=       hidden_dropout_prob
				__lowerCAmelCase      :  Optional[Any]												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  str												=       pad_token_id
				__lowerCAmelCase      :  Optional[int]												=       bos_token_id
				__lowerCAmelCase      :  Union[str, Any]												=       eos_token_id
				__lowerCAmelCase      :  Any												=       conv_glu_dim
				__lowerCAmelCase      :  Optional[int]												=       conv_dropout
				__lowerCAmelCase      :  Union[str, Any]												=       num_conv_layers
				__lowerCAmelCase      :  Optional[int]												=       input_feat_per_channel
				__lowerCAmelCase      :  Union[str, Any]												=       input_channels
				__lowerCAmelCase      :  Optional[Any]												=       conv_channels
				__lowerCAmelCase      :  Dict												=       ctc_loss_reduction
				__lowerCAmelCase      :  int												=       ctc_zero_infinity

				# prevents config testing fail with exporting to json
				__lowerCAmelCase      :  List[str]												=       list(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       list(_SCREAMING_SNAKE_CASE)

				if len(self.conv_kernel) != self.num_conv_layers:
							raise ValueError(
							    "Configuration for convolutional module is incorrect. "
							    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
							    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, """
							    F"""`config.num_conv_layers = {self.num_conv_layers}`.""")