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


a  :      Optional[Any]            =    logging.get_logger(__name__)

a  :      Union[str, Any]            =    """▁"""

a  :      Union[str, Any]            =    {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

a  :      int            =    {
    """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""",
    },
}

a  :      Optional[Any]            =    {"""vinai/bartpho-syllable""": 1_0_2_4}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[str]          =    VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:				Tuple          =    PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE:				List[str]          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE:				List[Any]          =    ['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__       ,						):
       # Mask token behave like a normal word, i.e. include the space before it
       lowerCAmelCase_:							Optional[Any]       =	AddedToken(lowerCamelCase__       ,						lstrip=lowerCamelCase__       ,						rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__       ,						lowerCamelCase__ ) else mask_token

       lowerCAmelCase_:							Union[str, 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__       ,						)

       lowerCAmelCase_:							Dict       =	vocab_file
       lowerCAmelCase_:							Dict       =	monolingual_vocab_file
       lowerCAmelCase_:							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
       lowerCAmelCase_:							Tuple       =	{}
       lowerCAmelCase_:							int       =	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:
             lowerCAmelCase_:							List[Any]       =	cnt
             cnt += 1
       with open(lowerCamelCase__       ,						"r"       ,						encoding="utf-8" ) as f:
          for line in f.readlines():
             lowerCAmelCase_:							Tuple       =	line.strip().split()[0]
             lowerCAmelCase_:							Tuple       =	len(self.fairseq_tokens_to_ids )
       if str(lowerCamelCase__ ) not in self.fairseq_tokens_to_ids:
          lowerCAmelCase_:							Union[str, Any]       =	len(self.fairseq_tokens_to_ids )

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




    def __getstate__(    self ):
       lowerCAmelCase_:							List[str]       =	self.__dict__.copy()
       lowerCAmelCase_:							Tuple       =	None
       lowerCAmelCase_:							List[Any]       =	self.sp_model.serialized_model_proto()
       return state




    def __setstate__(    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	d

       # for backward compatibility
       if not hasattr(self       ,						"sp_model_kwargs" ):
          lowerCAmelCase_:							Union[str, Any]       =	{}

       lowerCAmelCase_:							Union[str, Any]       =	spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):

       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowerCAmelCase_:							Union[str, Any]       =	[self.cls_token_id]
       lowerCAmelCase_:							Dict       =	[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = False ):

       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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       lowerCAmelCase_:							int       =	[self.sep_token_id]
       lowerCAmelCase_:							Any       =	[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      _a    (    self ):
       return len(self.fairseq_ids_to_tokens )




    def      _a    (    self ):
       lowerCAmelCase_:							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      _a    (    self       ,						lowerCamelCase__ ):
       return self.sp_model.encode(lowerCamelCase__       ,						out_type=lowerCamelCase__ )




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




    def      _a    (    self       ,						lowerCamelCase__ ):
       return self.fairseq_ids_to_tokens[index]




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	"".join(lowerCamelCase__ ).replace(lowerCamelCase__       ,						" " ).strip()
       return out_string




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       if not os.path.isdir(lowerCamelCase__ ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
          return
       lowerCAmelCase_:							Dict       =	os.path.join(
           lowerCamelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
       lowerCAmelCase_:							Dict       =	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:
             lowerCAmelCase_:							Any       =	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
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


a  :      Optional[int]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Dict          =    'efficientnet'




    def __init__(    self       ,						lowerCamelCase__ = 3       ,						lowerCamelCase__ = 600       ,						lowerCamelCase__ = 2.0       ,						lowerCamelCase__ = 3.1       ,						lowerCamelCase__ = 8       ,						lowerCamelCase__ = [3, 3, 5, 3, 5, 5, 3]       ,						lowerCamelCase__ = [32, 16, 24, 40, 80, 112, 192]       ,						lowerCamelCase__ = [16, 24, 40, 80, 112, 192, 320]       ,						lowerCamelCase__ = []       ,						lowerCamelCase__ = [1, 2, 2, 2, 1, 2, 1]       ,						lowerCamelCase__ = [1, 2, 2, 3, 3, 4, 1]       ,						lowerCamelCase__ = [1, 6, 6, 6, 6, 6, 6]       ,						lowerCamelCase__ = 0.2_5       ,						lowerCamelCase__ = "swish"       ,						lowerCamelCase__ = 2_560       ,						lowerCamelCase__ = "mean"       ,						lowerCamelCase__ = 0.0_2       ,						lowerCamelCase__ = 0.0_0_1       ,						lowerCamelCase__ = 0.9_9       ,						lowerCamelCase__ = 0.5       ,						lowerCamelCase__ = 0.2       ,						**lowerCamelCase__       ,						):
       super().__init__(**lowerCamelCase__ )

       lowerCAmelCase_:							str       =	num_channels
       lowerCAmelCase_:							str       =	image_size
       lowerCAmelCase_:							int       =	width_coefficient
       lowerCAmelCase_:							Union[str, Any]       =	depth_coefficient
       lowerCAmelCase_:							int       =	depth_divisor
       lowerCAmelCase_:							List[str]       =	kernel_sizes
       lowerCAmelCase_:							Tuple       =	in_channels
       lowerCAmelCase_:							List[str]       =	out_channels
       lowerCAmelCase_:							List[str]       =	depthwise_padding
       lowerCAmelCase_:							Optional[int]       =	strides
       lowerCAmelCase_:							List[str]       =	num_block_repeats
       lowerCAmelCase_:							Any       =	expand_ratios
       lowerCAmelCase_:							List[Any]       =	squeeze_expansion_ratio
       lowerCAmelCase_:							Optional[int]       =	hidden_act
       lowerCAmelCase_:							Optional[int]       =	hidden_dim
       lowerCAmelCase_:							Dict       =	pooling_type
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	batch_norm_eps
       lowerCAmelCase_:							List[str]       =	batch_norm_momentum
       lowerCAmelCase_:							List[Any]       =	dropout_rate
       lowerCAmelCase_:							Union[str, Any]       =	drop_connect_rate
       lowerCAmelCase_:							Tuple       =	sum(lowerCamelCase__ ) * 4





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    version.parse('1.11'			)




    @property
    def      _a    (    self ):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ] )




    @property
    def      _a    (    self ):
       return 1E-5