




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:
  _snake_case										=       None

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

_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",
    },
}

_snake_case										=       {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_snake_case										=       "▁"







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						VOCAB_FILES_NAMES
     _a							 =						PRETRAINED_VOCAB_FILES_MAP
     _a							 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a							 =						["input_ids", "token_type_ids"]
     _a							 =						FNetTokenizer

     def __init__(  self		,     _a=None		,     _a=None		,     _a=False		,     _a=True		,     _a=True		,     _a="<unk>"		,     _a="[SEP]"		,     _a="<pad>"		,     _a="[CLS]"		,     _a="[MASK]"		,     **_a		,     )						->							Optional[int]:
           # 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.
           _A							:     int			=						(
               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		,     )

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

     def 					a__	(  self		,     _a		,     _a = None				)						->							List[int]:
           _A							:     str			=						[self.sep_token_id]
           _A							:     Dict			=						[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		,     _a		,     _a = None				)						->							List[int]:
           _A							:     Any			=						[self.sep_token_id]
           _A							:     Any			=						[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		,     _a		,     _a = None				)						->							Tuple[str]:
           if not os.path.isdir(_a				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           _A							:     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 collections import OrderedDict
from typing import Mapping

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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"xmod"

     def __init__(  self		,     _a=3_0522		,     _a=768		,     _a=12		,     _a=12		,     _a=3072		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=2		,     _a=0.02		,     _a=1e-12		,     _a=1		,     _a=0		,     _a=2		,     _a="absolute"		,     _a=True		,     _a=None		,     _a=False		,     _a=2		,     _a=False		,     _a=True		,     _a=True		,     _a=("en_XX",)		,     _a=None		,     **_a		,     )						->							str:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     Tuple			=						vocab_size
           _A							:     Union[str, Any]			=						hidden_size
           _A							:     Dict			=						num_hidden_layers
           _A							:     Dict			=						num_attention_heads
           _A							:     List[Any]			=						hidden_act
           _A							:     Optional[Any]			=						intermediate_size
           _A							:     Any			=						hidden_dropout_prob
           _A							:     str			=						attention_probs_dropout_prob
           _A							:     Dict			=						max_position_embeddings
           _A							:     Any			=						type_vocab_size
           _A							:     List[Any]			=						initializer_range
           _A							:     int			=						layer_norm_eps
           _A							:     int			=						position_embedding_type
           _A							:     Any			=						use_cache
           _A							:     int			=						classifier_dropout
           _A							:     int			=						pre_norm
           _A							:     Optional[Any]			=						adapter_reduction_factor
           _A							:     List[Any]			=						adapter_layer_norm
           _A							:     Optional[int]			=						adapter_reuse_layer_norm
           _A							:     Any			=						ln_before_adapter
           _A							:     Union[str, Any]			=						list(_a				)
           _A							:     List[Any]			=						default_language







class 						lowercase							(   UpperCamelCase__		):

     @property
     def 					a__	(  self				)						->							Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
                 _A							:     Dict			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                 _A							:     Dict			=						{0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
               ]				)

