





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": 10_24}




class 			__UpperCamelCase    (							A__    ):
     __A	:  List[Any]							  =   VOCAB_FILES_NAMES
     __A	:  Dict							  =   PRETRAINED_VOCAB_FILES_MAP
     __A	:  Optional[Any]							  =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  int							  =   ["""input_ids""", """attention_mask"""]





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

        _UpperCAmelCase           =   {} if sp_model_kwargs is None else sp_model_kwargs

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

        _UpperCAmelCase           =   vocab_file
        _UpperCAmelCase           =   monolingual_vocab_file
        _UpperCAmelCase           =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(str(_UpperCamelCase					)					)

        # 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(_UpperCamelCase					) not in self.fairseq_tokens_to_ids:
              _UpperCAmelCase           =   cnt
              cnt += 1
        with open(_UpperCamelCase				,							'''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(_UpperCamelCase					) 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				,							_UpperCamelCase					):
        _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				,							_UpperCamelCase				,							_UpperCamelCase = 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				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = False					):

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

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = 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(_UpperCamelCase					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab





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





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





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





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   ''''''.join(_UpperCamelCase					).replace(_UpperCamelCase				,							''' '''					).strip()
        return out_string





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

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

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

        return out_vocab_file, out_monolingual_vocab_file





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


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

SCREAMING_SNAKE_CASE:     Optional[Any]									= {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="umt5"
						lowerCAmelCase__			      =["past_key_values"]



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[str, Any]=25_01_12					,							snake_case__	:   Optional[int]=5_12					,							snake_case__	:   Optional[Any]=64					,							snake_case__	:   str=10_24					,							snake_case__	:   Dict=8					,							snake_case__	:   Union[str, Any]=None					,							snake_case__	:   List[Any]=6					,							snake_case__	:   List[Any]=32					,							snake_case__	:   List[Any]=1_28					,							snake_case__	:   Optional[int]=0.1					,							snake_case__	:   List[str]=1e-6					,							snake_case__	:   Optional[Any]=1.0					,							snake_case__	:   Optional[int]="gated-gelu"					,							snake_case__	:   Any=True					,							snake_case__	:   List[Any]=True					,							snake_case__	:   List[str]="T5Tokenizer"					,							snake_case__	:   List[str]=True					,							snake_case__	:   Tuple=0					,							snake_case__	:   Optional[int]=1					,							snake_case__	:   List[str]=0					,							**snake_case__	:   Any					,							):

								"""simple docstring"""

								super().__init__(
								    is_encoder_decoder=snake_case__					,							tokenizer_class=snake_case__					,							tie_word_embeddings=snake_case__					,							pad_token_id=snake_case__					,							eos_token_id=snake_case__					,							decoder_start_token_id=snake_case__					,							**snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							d_model
								SCREAMING_SNAKE_CASE_					      =							d_kv
								SCREAMING_SNAKE_CASE_					      =							d_ff
								SCREAMING_SNAKE_CASE_					      =							num_layers
								SCREAMING_SNAKE_CASE_					      =							(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								SCREAMING_SNAKE_CASE_					      =							num_heads
								SCREAMING_SNAKE_CASE_					      =							relative_attention_num_buckets
								SCREAMING_SNAKE_CASE_					      =							relative_attention_max_distance
								SCREAMING_SNAKE_CASE_					      =							dropout_rate
								SCREAMING_SNAKE_CASE_					      =							layer_norm_epsilon
								SCREAMING_SNAKE_CASE_					      =							initializer_factor
								SCREAMING_SNAKE_CASE_					      =							feed_forward_proj
								SCREAMING_SNAKE_CASE_					      =							use_cache

								SCREAMING_SNAKE_CASE_					      =							self.feed_forward_proj.split('-'  )
								SCREAMING_SNAKE_CASE_					      =							act_info[-1]
								SCREAMING_SNAKE_CASE_					      =							act_info[0] == 'gated'

								if len(snake_case__  ) > 1 and act_info[0] != "gated" or len(snake_case__  ) > 2:
										raise ValueError(
										    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
										    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
										    '\'gated-gelu\' or \'relu\''  )

								if feed_forward_proj == "gated-gelu":
										SCREAMING_SNAKE_CASE_					      =							'gelu_new'



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.d_model



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.num_heads






						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.num_layers



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{
								    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
								    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
								}
								if self.use_past:
										SCREAMING_SNAKE_CASE_					      =							'past_encoder_sequence + sequence'
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch'}
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
								else:
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'decoder_sequence'}
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'decoder_sequence'}

								if self.use_past:
										self.fill_with_past_key_values_(snake_case__					,							direction='inputs'  )

								return common_inputs



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return 13






						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								return 5e-4