



"""simple docstring"""


from typing import TYPE_CHECKING

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


__A   :							int										=   {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A   :							Optional[int]										=   ["SpeechEncoderDecoderModel"]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A   :							Optional[Any]										=   ["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

					__A   :							Union[str, Any]										=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""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_fnet import FNetTokenizer
else:
					__A   :							Tuple										=   None

__A   :							Optional[Any]										=   logging.get_logger(__name__)
__A   :							Union[str, Any]										=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A   :							int										=   {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__A   :							Optional[int]										=   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__A   :							List[Any]										=   "▁"
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """token_type_ids"""]
			UpperCamelCase__			      =	FNetTokenizer



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple=None				,   __UpperCamelCase     :    Dict=None				,   __UpperCamelCase     :    List[str]=False				,   __UpperCamelCase     :    Union[str, Any]=True				,   __UpperCamelCase     :    List[str]=True				,   __UpperCamelCase     :    List[Any]="<unk>"				,   __UpperCamelCase     :    Any="[SEP]"				,   __UpperCamelCase     :    Dict="<pad>"				,   __UpperCamelCase     :    Optional[int]="[CLS]"				,   __UpperCamelCase     :    List[Any]="[MASK]"				,   **__UpperCamelCase     :    Optional[Any]				,   )->Dict:
							# Mask token behave like a normal word, i.e. include the space before it and
							# is included in the raw text, there should be a match in a non-normalized sentence.
							_UpperCAmelCase					=    (
							    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase				,   normalized=__UpperCamelCase   )
							    if isinstance(__UpperCamelCase				,   __UpperCamelCase   )
							    else mask_token
							)

							super().__init__(
							    __UpperCamelCase				,   tokenizer_file=__UpperCamelCase				,   do_lower_case=__UpperCamelCase				,   remove_space=__UpperCamelCase				,   keep_accents=__UpperCamelCase				,   unk_token=__UpperCamelCase				,   sep_token=__UpperCamelCase				,   pad_token=__UpperCamelCase				,   cls_token=__UpperCamelCase				,   mask_token=__UpperCamelCase				,   **__UpperCamelCase				,   )

							_UpperCAmelCase					=    do_lower_case
							_UpperCAmelCase					=    remove_space
							_UpperCAmelCase					=    keep_accents
							_UpperCAmelCase					=    vocab_file
							_UpperCAmelCase					=    False if not self.vocab_file else True



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [self.cls_token_id]
							if token_ids_a is None:
											return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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   ) * [0] + len(token_ids_a + sep   ) * [1]



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->Tuple[str]:
							if not os.path.isdir(__UpperCamelCase   ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
											return
							_UpperCAmelCase					=    os.path.join(
							    __UpperCamelCase				,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

							return (out_vocab_file,)



