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			:						List[str]				  =		None

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		logging.get_logger(__name__)
SCREAMING_SNAKE_CASE			:						Dict				  =		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE			:						Optional[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""",
    },
}

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

SCREAMING_SNAKE_CASE			:						Any				  =		"""▁"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE     					=      ["""input_ids""", """token_type_ids"""]
  _SCREAMING_SNAKE_CASE     					=      FNetTokenizer

  def __init__(		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=False  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=True  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Any="<unk>"  ,				__SCREAMING_SNAKE_CASE			:   Dict="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="<pad>"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   str="[MASK]"  ,				**__SCREAMING_SNAKE_CASE			:   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.
   __a							=	(
       AddedToken(__SCREAMING_SNAKE_CASE  ,				lstrip=__SCREAMING_SNAKE_CASE  ,				rstrip=__SCREAMING_SNAKE_CASE  ,				normalized=__SCREAMING_SNAKE_CASE							)
       if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
       else mask_token
   )

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

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

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __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 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __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 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None							):
   if not os.path.isdir(__SCREAMING_SNAKE_CASE							):
    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
    return
   __a							=	os.path.join(
       __SCREAMING_SNAKE_CASE  ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

   return (out_vocab_file,)



import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (abs(source - target       ) / target) < 0.01






@pytest.mark.integration
def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	_TestCommandArgs(dataset=_A				,					all_configs=_A				,					save_infos=_A       )
 __a							=	TestCommand(*_A       )
 test_command.run()
 __a							=	os.path.join(_A				,					"README.md"       )
 assert os.path.exists(_A       )
 __a							=	DatasetInfosDict.from_directory(_A       )
 __a							=	DatasetInfosDict(
     {
         "default": DatasetInfo(
             features=Features(
                 {
                     "tokens": Sequence(Value("string"       )       ),
                     "ner_tags": Sequence(
                         ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]       )       ),
                     "langs": Sequence(Value("string"       )       ),
                     "spans": Sequence(Value("string"       )       ),
                 }       )				,					splits=[
                 {
                     "name": "train",
                     "num_bytes": 235_1563,
                     "num_examples": 1_0000,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 23_8418,
                     "num_examples": 1000,
                 },
             ]				,					download_size=394_0680				,					dataset_size=258_9981				,					)
     }       )
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  __a							,  __a							=	getattr(dataset_infos["default"]				,					_A       ), getattr(expected_dataset_infos["default"]				,					_A       )
  if key == "num_bytes":
   assert is_apercent_close(_A				,					_A       )
  elif key == "splits":
   assert list(_A       ) == list(_A       )
   for split in result:
    assert result[split].name == expected[split].name
    assert result[split].num_examples == expected[split].num_examples
    assert is_apercent_close(result[split].num_bytes				,					expected[split].num_bytes       )
  else:
   result == expected


