"""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:
				__A     = None


__A     = logging.get_logger(__name__)

__A     = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A     = {
    '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',
    },
}

__A     = {
    'camembert-base': 512,
}

__A     = '▁'
class        _snake_case   (    _UpperCamelCase						):
						snake_case__	     =	VOCAB_FILES_NAMES
						snake_case__	     =	PRETRAINED_VOCAB_FILES_MAP
						snake_case__	     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__	     =	["""input_ids""", """attention_mask"""]
						snake_case__	     =	CamembertTokenizer






						def __init__(				self					:	Any							,    UpperCAmelCase					:	Any=None							,    UpperCAmelCase					:	str=None							,    UpperCAmelCase					:	Optional[Any]="<s>"							,    UpperCAmelCase					:	Tuple="</s>"							,    UpperCAmelCase					:	Any="</s>"							,    UpperCAmelCase					:	int="<s>"							,    UpperCAmelCase					:	Union[str, Any]="<unk>"							,    UpperCAmelCase					:	Dict="<pad>"							,    UpperCAmelCase					:	List[str]="<mask>"							,    UpperCAmelCase					:	Dict=["<s>NOTUSED", "</s>NOTUSED"]							,    **UpperCAmelCase					:	int							,    ):
													# Mask token behave like a normal word, i.e. include the space before it
													__lowerCamelCase    :						Optional[Any]						      =   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							,    )

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






						def 					lowerCamelCase__   (				self					:	List[str]							,    UpperCAmelCase					:	List[int]							,    UpperCAmelCase					:	Optional[List[int]] = None			):

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






						def 					lowerCamelCase__   (				self					:	Tuple							,    UpperCAmelCase					:	List[int]							,    UpperCAmelCase					:	Optional[List[int]] = None			):
													__lowerCamelCase    :						Optional[int]						      =   [self.sep_token_id]
													__lowerCamelCase    :						Union[str, Any]						      =   [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 					lowerCamelCase__   (				self					:	List[Any]							,    UpperCAmelCase					:	str							,    UpperCAmelCase					:	Optional[str] = None			):
													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
													__lowerCamelCase    :						str						      =   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_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase		:				List[str]       			=		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Dict       			=		[
		    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ResNetForImageClassification',
		    'ResNetModel',
		    'ResNetPreTrainedModel',
		    'ResNetBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFResNetForImageClassification',
		    'TFResNetModel',
		    'TFResNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[Any]       			=		[
		    'FlaxResNetForImageClassification',
		    'FlaxResNetModel',
		    'FlaxResNetPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase		:				int       			=		_LazyModule(__name__, globals()['__file__'], _import_structure)






