




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__       :      str  =   None

SCREAMING_SNAKE_CASE__       :      Tuple  =   logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__       :      List[Any]  =   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__       :      Any  =   {
    '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__       :      Tuple  =   {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   '▁'




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	int	     =      VOCAB_FILES_NAMES
    lowerCamelCase_      :	Tuple	     =      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_      :	Dict	     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_      :	Optional[int]	     =      ["""input_ids""", """token_type_ids"""]
    lowerCamelCase_      :	List[str]	     =      FNetTokenizer







    def __init__(    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=False     ,  UpperCamelCase__=True     ,  UpperCamelCase__=True     ,  UpperCamelCase__="<unk>"     ,  UpperCamelCase__="[SEP]"     ,  UpperCamelCase__="<pad>"     ,  UpperCamelCase__="[CLS]"     ,  UpperCamelCase__="[MASK]"     ,  **UpperCamelCase__     ,  )     ->      str:
           # 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.
           lowerCamelCase   :      Any			 =					(
               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__     ,  )

           lowerCamelCase   :      Optional[Any]			 =					do_lower_case
           lowerCamelCase   :      Union[str, Any]			 =					remove_space
           lowerCamelCase   :      str			 =					keep_accents
           lowerCamelCase   :      Optional[Any]			 =					vocab_file
           lowerCamelCase   :      List[Any]			 =					False if not self.vocab_file else True







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      int			 =					[self.sep_token_id]
           lowerCamelCase   :      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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      List[str]			 =					[self.sep_token_id]
           lowerCamelCase   :      Tuple			 =					[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     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      Tuple[str]:
           if not os.path.isdir(UpperCamelCase__     ):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                  return
           lowerCamelCase   :      int			 =					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,)














import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowercase  							=					logging.getLogger(__name__)




@dataclass
class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= field(
	    default=0.0       ,					metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''whether to use adafactor'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
	lowerCAmelCase													= field(
	    default='''linear'''       ,					metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}       ,					)





