





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    :					Dict =      None

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

A    :					Tuple =      {
    '''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[Any] =      {
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

A    :					Tuple =      '''▁'''


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    VOCAB_FILES_NAMES
			__lowerCamelCase	:       Union[str, Any]							=    PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase	:       Union[str, Any]							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase	:       int							=    ['''input_ids''', '''token_type_ids''']
			__lowerCamelCase	:       Optional[Any]							=    FNetTokenizer






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







					"""simple docstring"""






					A__        =   (
					    AddedToken(__lowerCAmelCase							,	lstrip=__lowerCAmelCase							,	rstrip=__lowerCAmelCase							,	normalized=__lowerCAmelCase   )
					    if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   )
					    else mask_token
					)

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

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






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   [self.sep_token_id]
					A__        =   [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    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   [self.sep_token_id]
					A__        =   [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    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[str] = None   )	->	Tuple[str]:







					"""simple docstring"""






					if not os.path.isdir(__lowerCAmelCase   ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
							return
					A__        =   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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A    :					Union[str, Any] =      logging.get_logger(__name__)

A    :					int =      {
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Any							=    '''xlm-roberta'''






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]=3_05_22							,	__lowerCAmelCase   :    int=7_68							,	__lowerCAmelCase   :    Tuple=12							,	__lowerCAmelCase   :    str=12							,	__lowerCAmelCase   :    Union[str, Any]=30_72							,	__lowerCAmelCase   :    Union[str, Any]="gelu"							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    Tuple=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-12							,	__lowerCAmelCase   :    Union[str, Any]=1							,	__lowerCAmelCase   :    str=0							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    Tuple="absolute"							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=None							,	**__lowerCAmelCase   :    str							,	)	->	Optional[Any]:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase   )

					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   hidden_act
					A__        =   intermediate_size
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   initializer_range
					A__        =   layer_norm_eps
					A__        =   position_embedding_type
					A__        =   use_cache
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__        =   {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__        =   {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]   )



