




"""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:
					UpperCamelCase__         =      None


UpperCamelCase__         =      logging.get_logger(__name__)

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

UpperCamelCase__         =      {
    """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__         =      {
    """camembert-base""": 512,
}

UpperCamelCase__         =      """▁"""
class       a__   ( snake_case__				):
						_a							:   Union[str, Any] =   VOCAB_FILES_NAMES
						_a							:   List[str] =   PRETRAINED_VOCAB_FILES_MAP
						_a							:   Optional[Any] =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:   Tuple =   ["""input_ids""", """attention_mask"""]
						_a							:   Optional[Any] =   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"""



											__lowerCAmelCase           =	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		,      )

											__lowerCAmelCase           =	vocab_file
											__lowerCAmelCase           =	False if not self.vocab_file else True






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



											if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowerCAmelCase           =	[self.cls_token_id]
											__lowerCAmelCase           =	[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[self.sep_token_id]
											__lowerCAmelCase           =	[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     __SCREAMING_SNAKE_CASE(       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
											__lowerCAmelCase           =	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,)










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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    """alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class       a__   ( snake_case__				):
						_a							:   Dict =   """mgp-str"""






						def __init__(       self		,      _A=[3_2, 1_2_8]		,      _A=4		,      _A=3		,      _A=2_7		,      _A=3_8		,      _A=5_0_2_5_7		,      _A=3_0_5_2_2		,      _A=7_6_8		,      _A=1_2		,      _A=1_2		,      _A=4.0		,      _A=True		,      _A=False		,      _A=1E-5		,      _A=0.0		,      _A=0.0		,      _A=0.0		,      _A=False		,      _A=0.02		,      **_A		,      ):




											"""simple docstring"""



											super().__init__(**_A      )

											__lowerCAmelCase           =	image_size
											__lowerCAmelCase           =	patch_size
											__lowerCAmelCase           =	num_channels
											__lowerCAmelCase           =	max_token_length
											__lowerCAmelCase           =	num_character_labels
											__lowerCAmelCase           =	num_bpe_labels
											__lowerCAmelCase           =	num_wordpiece_labels
											__lowerCAmelCase           =	hidden_size
											__lowerCAmelCase           =	num_hidden_layers
											__lowerCAmelCase           =	num_attention_heads
											__lowerCAmelCase           =	mlp_ratio
											__lowerCAmelCase           =	distilled
											__lowerCAmelCase           =	layer_norm_eps
											__lowerCAmelCase           =	drop_rate
											__lowerCAmelCase           =	qkv_bias
											__lowerCAmelCase           =	attn_drop_rate
											__lowerCAmelCase           =	drop_path_rate
											__lowerCAmelCase           =	output_aa_attentions
											__lowerCAmelCase           =	initializer_range




