



'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A__    :   Dict           =					logging.get_logger(__name__)

A__    :   int           =					'''▁'''

A__    :   Any           =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

A__    :   Union[str, Any]           =					{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

A__    :   Any           =					{
    '''facebook/xglm-564M''': 2_0_4_8,
}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							List[Any]      ,     __a						:							Dict      ,     __a						:							Tuple="<s>"      ,     __a						:							List[Any]="</s>"      ,     __a						:							List[str]="</s>"      ,     __a						:							Any="<s>"      ,     __a						:							Optional[Any]="<unk>"      ,     __a						:							Optional[Any]="<pad>"      ,     __a						:							Optional[Dict[str, Any]] = None      ,     **__a						:							List[Any]      ,     )      ->   None:







       '''simple docstring'''
       __snake_case :   List[Any]													=    {} if sp_model_kwargs is None else sp_model_kwargs

       # Compatibility with the original tokenizer
       __snake_case :   Optional[Any]													=    7
       __snake_case :   List[str]													=    [f'''<madeupword{i}>''' for i in range(self.num_madeup_words  )]

       __snake_case :   Union[str, Any]													=    kwargs.get('additional_special_tokens'      ,     []  )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]

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

       __snake_case :   Union[str, Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(__a  )  )
       __snake_case :   Dict													=    vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       __snake_case :   Tuple													=    1

       # Mimic fairseq token-to-id alignment for the first 4 token
       __snake_case :   Tuple													=    {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

       __snake_case :   Tuple													=    len(self.sp_model  )
       __snake_case :   List[Any]													=    {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words  )}
       self.fairseq_tokens_to_ids.update(__a  )

       __snake_case :   int													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(   self						:							int  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    self.__dict__.copy()
       __snake_case :   int													=    None
       __snake_case :   Optional[int]													=    self.sp_model.serialized_model_proto()
       return state
 def __setstate__(   self						:							Union[str, Any]      ,     __a						:							Optional[Any]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    d

       # for backward compatibility
       if not hasattr(self      ,     'sp_model_kwargs'  ):
             __snake_case :   Optional[Any]													=    {}

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







       '''simple docstring'''

       if token_ids_a is None:
             return [self.sep_token_id] + token_ids_a
       __snake_case :   Any													=    [self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a
 def A_   (   self						:							Dict      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''simple docstring'''

       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  ))
       return [1] + ([0] * len(__a  )) + [1, 1] + ([0] * len(__a  ))
 def A_   (   self						:							List[str]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    [self.sep_token_id]

       if token_ids_a is None:
             return len(sep + token_ids_a  ) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a  ) * [0]
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Any:







       '''simple docstring'''
       return len(self.sp_model  ) + self.fairseq_offset + self.num_madeup_words
 def A_   (   self						:							Optional[Any]  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							Union[str, Any]      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							List[str]      ,     __a						:							Dict  )      ->   Dict:







       '''simple docstring'''
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       __snake_case :   Dict													=    self.sp_model.PieceToId(__a  )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
 def A_   (   self						:							List[str]      ,     __a						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset  )
 def A_   (   self						:							Dict      ,     __a						:							str  )      ->   str:







       '''simple docstring'''
       __snake_case :   Tuple													=    ''.join(__a  ).replace(__a      ,     ' '  ).strip()
       return out_string





 def A_   (   self						:							Optional[int]      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   List[Any]													=    os.path.join(
           __a      ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['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:
                   __snake_case :   int													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)








'''simple docstring'''





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


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

A__    :   List[str]           =					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''cvt'''
 def __init__(   self						:							Optional[Any]      ,     __a						:							Union[str, Any]=3      ,     __a						:							List[Any]=[7, 3, 3]      ,     __a						:							Optional[int]=[4, 2, 2]      ,     __a						:							Dict=[2, 1, 1]      ,     __a						:							Union[str, Any]=[64, 192, 384]      ,     __a						:							int=[1, 3, 6]      ,     __a						:							List[str]=[1, 2, 10]      ,     __a						:							Optional[Any]=[4.0, 4.0, 4.0]      ,     __a						:							Any=[0.0, 0.0, 0.0]      ,     __a						:							List[str]=[0.0, 0.0, 0.0]      ,     __a						:							List[Any]=[0.0, 0.0, 0.1]      ,     __a						:							List[str]=[True, True, True]      ,     __a						:							int=[False, False, True]      ,     __a						:							Dict=["dw_bn", "dw_bn", "dw_bn"]      ,     __a						:							List[str]=[3, 3, 3]      ,     __a						:							Union[str, Any]=[1, 1, 1]      ,     __a						:							Optional[int]=[2, 2, 2]      ,     __a						:							Optional[Any]=[1, 1, 1]      ,     __a						:							List[str]=[1, 1, 1]      ,     __a						:							List[str]=0.0_2      ,     __a						:							List[str]=1e-12      ,     **__a						:							List[str]      ,     )      ->   Dict:







       '''simple docstring'''
       super().__init__(**__a  )
       __snake_case :   int													=    num_channels
       __snake_case :   Union[str, Any]													=    patch_sizes
       __snake_case :   Any													=    patch_stride
       __snake_case :   List[str]													=    patch_padding
       __snake_case :   Optional[Any]													=    embed_dim
       __snake_case :   Union[str, Any]													=    num_heads
       __snake_case :   Dict													=    depth
       __snake_case :   Optional[Any]													=    mlp_ratio
       __snake_case :   List[str]													=    attention_drop_rate
       __snake_case :   Optional[int]													=    drop_rate
       __snake_case :   Optional[int]													=    drop_path_rate
       __snake_case :   Any													=    qkv_bias
       __snake_case :   int													=    cls_token
       __snake_case :   Optional[int]													=    qkv_projection_method
       __snake_case :   List[Any]													=    kernel_qkv
       __snake_case :   List[Any]													=    padding_kv
       __snake_case :   int													=    stride_kv
       __snake_case :   List[str]													=    padding_q
       __snake_case :   Dict													=    stride_q
       __snake_case :   Tuple													=    initializer_range
       __snake_case :   List[Any]													=    layer_norm_eps



