



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: Dict      			=		None


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

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

_lowercase: List[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: Any      			=		{
    "camembert-base": 512,
}

_lowercase: List[Any]      			=		"▁"



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							VOCAB_FILES_NAMES
	__A							=							PRETRAINED_VOCAB_FILES_MAP
	__A							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A							=							["input_ids", "attention_mask"]
	__A							=							CamembertTokenizer




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

						a					     =   AddedToken(lowerCamelCase_		,       lstrip=lowerCamelCase_		,       rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_		,       lowerCamelCase_		) else mask_token

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

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						a					     =   [self.sep_token_id]
						a					     =   [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		,       lowerCamelCase_		,       lowerCamelCase_ = 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(lowerCamelCase_		):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
											return
						a					     =   os.path.join(
						    lowerCamelCase_		,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

						return (out_vocab_file,)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase: Union[str, Any]      			=		{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: Dict      			=		["BridgeTowerImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: int      			=		[
				    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BridgeTowerForContrastiveLearning",
				    "BridgeTowerForImageAndTextRetrieval",
				    "BridgeTowerForMaskedLM",
				    "BridgeTowerModel",
				    "BridgeTowerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_bridgetower import (
				    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BridgeTowerConfig,
				    BridgeTowerTextConfig,
				    BridgeTowerVisionConfig,
				)
				from .processing_bridgetower import BridgeTowerProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_bridgetower import BridgeTowerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bridgetower import (
								    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BridgeTowerForContrastiveLearning,
								    BridgeTowerForImageAndTextRetrieval,
								    BridgeTowerForMaskedLM,
								    BridgeTowerModel,
								    BridgeTowerPreTrainedModel,
								)


else:
				import sys

				_lowercase: Optional[Any]      			=		_LazyModule(__name__, globals()["__file__"], _import_structure)
