





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:
						_SCREAMING_SNAKE_CASE      	=		None

_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE      	=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE      	=		{
    """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""",
    },
}

_SCREAMING_SNAKE_CASE      	=		{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

_SCREAMING_SNAKE_CASE      	=		"""▁"""
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Union[str, Any]       =       VOCAB_FILES_NAMES
						__magic_name__:			Any       =       PRETRAINED_VOCAB_FILES_MAP
						__magic_name__:			Optional[Any]       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__magic_name__:			List[Any]       =       ["input_ids", "token_type_ids"]
						__magic_name__:			Optional[Any]       =       FNetTokenizer






						def __init__(      self      :      Dict		,   _A      :      Union[str, Any]=None		,   _A      :      Tuple=None		,   _A      :      Dict=False		,   _A      :      List[str]=True		,   _A      :      Union[str, Any]=True		,   _A      :      Optional[int]="<unk>"		,   _A      :      List[Any]="[SEP]"		,   _A      :      Dict="<pad>"		,   _A      :      Tuple="[CLS]"		,   _A      :      List[str]="[MASK]"		,   **_A      :      str		,   )							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	(
							    AddedToken(_A		,   lstrip=_A		,   rstrip=_A		,   normalized=_A	)
							    if isinstance(_A		,   _A	)
							    else mask_token
							)

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

							snake_case_		:			Tuple											=	do_lower_case
							snake_case_		:			str											=	remove_space
							snake_case_		:			int											=	keep_accents
							snake_case_		:			Optional[Any]											=	vocab_file
							snake_case_		:			List[Any]											=	False if not self.vocab_file else True






						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None	)							->    List[int]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	[self.sep_token_id]
							snake_case_		:			Union[str, Any]											=	[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 UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      List[int]		,   _A      :      Optional[List[int]] = None	)							->    List[int]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	[self.sep_token_id]
							snake_case_		:			Dict											=	[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 UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      str		,   _A      :      Optional[str] = None	)							->    Tuple[str]:







							"""simple docstring"""


							if not os.path.isdir(_A	):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
								return
							snake_case_		:			List[str]											=	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_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE      	=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwinForImageClassification""",
						    """SwinForMaskedImageModeling""",
						    """SwinModel""",
						    """SwinPreTrainedModel""",
						    """SwinBackbone""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFSwinForImageClassification""",
						    """TFSwinForMaskedImageModeling""",
						    """TFSwinModel""",
						    """TFSwinPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swin import (
												    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwinBackbone,
												    SwinForImageClassification,
												    SwinForMaskedImageModeling,
												    SwinModel,
												    SwinPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_swin import (
												    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSwinForImageClassification,
												    TFSwinForMaskedImageModeling,
												    TFSwinModel,
												    TFSwinPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE      	=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

