






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_barthez import BarthezTokenizer
else:
			_UpperCAmelCase			:		Union[str, Any]   				=    None

_UpperCAmelCase			:		str   				=    logging.get_logger(__name__)

_UpperCAmelCase			:		List[str]   				=    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCAmelCase			:		Dict   				=    {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCAmelCase			:		Optional[int]   				=    {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

_UpperCAmelCase			:		Optional[int]   				=    '▁'
class  lowerCAmelCase_       (				lowerCAmelCase__					):
							UpperCamelCase_		:int		      =    VOCAB_FILES_NAMES
							UpperCamelCase_		:Dict		      =    PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_		:Optional[Any]		      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_		:Union[str, Any]		      =    ["input_ids", "attention_mask"]
							UpperCamelCase_		:Tuple		      =    BarthezTokenizer




							def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=None ,							SCREAMING_SNAKE_CASE_     :   List[Any]="<s>" ,							SCREAMING_SNAKE_CASE_     :   List[Any]="</s>" ,							SCREAMING_SNAKE_CASE_     :   Tuple="</s>" ,							SCREAMING_SNAKE_CASE_     :   Dict="<s>" ,							SCREAMING_SNAKE_CASE_     :   Any="<unk>" ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]="<pad>" ,							SCREAMING_SNAKE_CASE_     :   int="<mask>" ,							**SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							):
											# Mask token behave like a normal word, i.e. include the space before it
											lowerCAmelCase__      			=						AddedToken(_SCREAMING_SNAKE_CASE ,							lstrip=_SCREAMING_SNAKE_CASE ,							rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE	) else mask_token

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

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




							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any] = None	):

											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 						__snake_case					(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Tuple = None	):
											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 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Any = 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(_SCREAMING_SNAKE_CASE	):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
															return
											lowerCAmelCase__      			=						os.path.join(
											    _SCREAMING_SNAKE_CASE ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

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

											return (out_vocab_file,)













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





