


'''simple docstring'''



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:
			__lowercase     :						str	=     None


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

__lowercase     :						Optional[int]	=     {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__lowercase     :						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",
    },
}

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

__lowercase     :						int	=     "▁"





class 							__UpperCamelCase    (		a_ ):
	A_           =   VOCAB_FILES_NAMES
	A_           =   PRETRAINED_VOCAB_FILES_MAP
	A_           =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_           =   ["input_ids", "attention_mask"]
	A_           =   CamembertTokenizer




	def __init__(       self						,    __a=None						,    __a=None						,    __a="<s>"						,    __a="</s>"						,    __a="</s>"						,    __a="<s>"						,    __a="<unk>"						,    __a="<pad>"						,    __a="<mask>"						,    __a=["<s>NOTUSED", "</s>NOTUSED"]						,    **__a						,    ):

						'''simple docstring'''





						__a      :	List[Any]    =  AddedToken(__a						,    lstrip=__a						,    rstrip=__a							) if isinstance(__a						,    __a							) else mask_token

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

						__a      :	Union[str, Any]    =  vocab_file
						__a      :	Dict    =  False if not self.vocab_file else True




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						__a      :	str    =  [self.sep_token_id]
						__a      :	List[str]    =  [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 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						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(__a							):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
											return
						__a      :	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							):
											copyfile(self.vocab_file						,    __a							)

						return (out_vocab_file,)







'''simple docstring'''



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase     :						List[Any]	=     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "ernie_m"
	A_           =   {"dropout": "classifier_dropout", "num_classes": "num_labels"}




	def __init__(       self						,    __a = 25_0002						,    __a = 768						,    __a = 12						,    __a = 12						,    __a = 3072						,    __a = "gelu"						,    __a = 0.1						,    __a = 0.1						,    __a = 514						,    __a = 0.02						,    __a = 1						,    __a = 1E-0_5						,    __a=None						,    __a=False						,    __a=0.0						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    **__a							)
						__a      :	int    =  vocab_size
						__a      :	Dict    =  hidden_size
						__a      :	str    =  num_hidden_layers
						__a      :	Dict    =  num_attention_heads
						__a      :	List[str]    =  intermediate_size
						__a      :	Union[str, Any]    =  hidden_act
						__a      :	List[Any]    =  hidden_dropout_prob
						__a      :	str    =  attention_probs_dropout_prob
						__a      :	Any    =  max_position_embeddings
						__a      :	int    =  initializer_range
						__a      :	Dict    =  layer_norm_eps
						__a      :	int    =  classifier_dropout
						__a      :	Dict    =  is_decoder
						__a      :	int    =  act_dropout



