





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_camembert import CamembertTokenizer
else:
    _UpperCAmelCase							:			Tuple        =							None


_UpperCAmelCase							:			List[str]        =							logging.get_logger(__name__)

_UpperCAmelCase							:			int        =							{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase							:			Union[str, Any]        =							{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

_UpperCAmelCase							:			List[str]        =							{
    """camembert-base""": 5_12,
}

_UpperCAmelCase							:			Tuple        =							"""▁"""

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						VOCAB_FILES_NAMES
 UpperCAmelCase__						=						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__						=						["""input_ids""", """attention_mask"""]
 UpperCAmelCase__						=						CamembertTokenizer
 def __init__( self    : Optional[Any]     ,	UpperCAmelCase    : Optional[Any]=None     ,	UpperCAmelCase    : int=None     ,	UpperCAmelCase    : Tuple="<s>"     ,	UpperCAmelCase    : List[str]="</s>"     ,	UpperCAmelCase    : str="</s>"     ,	UpperCAmelCase    : Any="<s>"     ,	UpperCAmelCase    : List[Any]="<unk>"     ,	UpperCAmelCase    : Dict="<pad>"     ,	UpperCAmelCase    : str="<mask>"     ,	UpperCAmelCase    : Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]     ,	**UpperCAmelCase    : Optional[Any]     ,	)     ->      Tuple:
     # Mask token behave like a normal word, i.e. include the space before it
     lowerCamelCase__       : Optional[Any]   					=						AddedToken(UpperCAmelCase     ,	lstrip=UpperCAmelCase     ,	rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ) else mask_token

     super().__init__(
         UpperCAmelCase     ,	tokenizer_file=UpperCAmelCase     ,	bos_token=UpperCAmelCase     ,	eos_token=UpperCAmelCase     ,	sep_token=UpperCAmelCase     ,	cls_token=UpperCAmelCase     ,	unk_token=UpperCAmelCase     ,	pad_token=UpperCAmelCase     ,	mask_token=UpperCAmelCase     ,	additional_special_tokens=UpperCAmelCase     ,	**UpperCAmelCase     ,	)

     lowerCamelCase__       : Union[str, Any]   					=						vocab_file
     lowerCamelCase__       : Optional[int]   					=						False if not self.vocab_file else True
 def 	A_      ( self    : Any     ,	UpperCAmelCase    : List[int]     ,	UpperCAmelCase    : Optional[List[int]] = None      )     ->      List[int]:

     if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     lowerCamelCase__       : List[str]   					=						[self.cls_token_id]
     lowerCamelCase__       : Any   					=						[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : List[int]     ,	UpperCAmelCase    : Optional[List[int]] = None      )     ->      List[int]:
     lowerCamelCase__       : Any   					=						[self.sep_token_id]
     lowerCamelCase__       : Optional[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]







 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : Optional[str] = None      )     ->      Tuple[str]:
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
             'tokenizer.'      )

     if not os.path.isdir(UpperCAmelCase      ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
         return
     lowerCamelCase__       : List[str]   					=						os.path.join(
         UpperCAmelCase     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

     if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCAmelCase      ):
         copyfile(self.vocab_file     ,	UpperCAmelCase      )

     return (out_vocab_file,)












from typing import List

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


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Union[str, Any]        =							{
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""efficientformer"""
 def __init__( self    : Dict     ,	UpperCAmelCase    : List[int] = [3, 2, 6, 4]     ,	UpperCAmelCase    : List[int] = [48, 96, 224, 448]     ,	UpperCAmelCase    : List[bool] = [True, True, True, True]     ,	UpperCAmelCase    : int = 448     ,	UpperCAmelCase    : int = 32     ,	UpperCAmelCase    : int = 4     ,	UpperCAmelCase    : int = 7     ,	UpperCAmelCase    : int = 5     ,	UpperCAmelCase    : int = 8     ,	UpperCAmelCase    : int = 4     ,	UpperCAmelCase    : float = 0.0     ,	UpperCAmelCase    : int = 16     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 2     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : float = 0.0     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : float = 1e-5     ,	UpperCAmelCase    : str = "gelu"     ,	UpperCAmelCase    : float = 0.0_2     ,	UpperCAmelCase    : float = 1e-12     ,	UpperCAmelCase    : int = 224     ,	UpperCAmelCase    : float = 1e-05     ,	**UpperCAmelCase    : Tuple     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )

     lowerCamelCase__       : Optional[Any]   					=						hidden_act
     lowerCamelCase__       : int   					=						hidden_dropout_prob
     lowerCamelCase__       : List[Any]   					=						hidden_sizes
     lowerCamelCase__       : int   					=						num_hidden_layers
     lowerCamelCase__       : int   					=						num_attention_heads
     lowerCamelCase__       : Dict   					=						initializer_range
     lowerCamelCase__       : Optional[Any]   					=						layer_norm_eps
     lowerCamelCase__       : int   					=						patch_size
     lowerCamelCase__       : List[Any]   					=						num_channels
     lowerCamelCase__       : List[str]   					=						depths
     lowerCamelCase__       : Any   					=						mlp_expansion_ratio
     lowerCamelCase__       : Any   					=						downsamples
     lowerCamelCase__       : str   					=						dim
     lowerCamelCase__       : Tuple   					=						key_dim
     lowerCamelCase__       : int   					=						attention_ratio
     lowerCamelCase__       : int   					=						resolution
     lowerCamelCase__       : Dict   					=						pool_size
     lowerCamelCase__       : List[str]   					=						downsample_patch_size
     lowerCamelCase__       : Tuple   					=						downsample_stride
     lowerCamelCase__       : int   					=						downsample_pad
     lowerCamelCase__       : Optional[int]   					=						drop_path_rate
     lowerCamelCase__       : Optional[Any]   					=						num_metaad_blocks
     lowerCamelCase__       : Any   					=						distillation
     lowerCamelCase__       : Optional[int]   					=						use_layer_scale
     lowerCamelCase__       : Union[str, Any]   					=						layer_scale_init_value
     lowerCamelCase__       : Optional[int]   					=						image_size
     lowerCamelCase__       : Optional[Any]   					=						batch_norm_eps





