
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:
					__snake_case    :    Optional[int]         =     None


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

__snake_case    :    Union[str, Any]         =     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case    :    Tuple         =     {
    """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""",
    },
}

__snake_case    :    str         =     {
    """camembert-base""": 5_12,
}

__snake_case    :    str         =     """▁"""


class     __SCREAMING_SNAKE_CASE					(   __SCREAMING_SNAKE_CASE):
						_SCREAMING_SNAKE_CASE	:       int   =					VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE	:       Dict   =					['input_ids', 'attention_mask']
						_SCREAMING_SNAKE_CASE	:       int   =					CamembertTokenizer






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






												"""simple docstring"""
												# Mask token behave like a normal word, i.e. include the space before it
												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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = 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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = 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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = 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 typing import TYPE_CHECKING

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


__snake_case    :    Dict         =     {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Union[str, Any]         =     ["""SpeechEncoderDecoderModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    str         =     ["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
					from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
					import sys

					__snake_case    :    Tuple         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





