

'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_xglm import XGLMTokenizer
else:
     lowerCamelCase				:   Dict      				=  None


lowerCamelCase				:   str      				=  logging.get_logger(__name__)

lowerCamelCase				:   str      				=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase				:   str      				=  {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

lowerCamelCase				:   str      				=  {
    "facebook/xglm-564M": 2_0_4_8,
}







class 							A__					( A__						):
  A__					=       VOCAB_FILES_NAMES
  A__					=       PRETRAINED_VOCAB_FILES_MAP
  A__					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__					=       ['input_ids', 'attention_mask']
  A__					=       XGLMTokenizer





  def __init__(	self   :			int							,      _a   :			Dict=None							,      _a   :			Optional[Any]=None							,      _a   :			Dict="<s>"							,      _a   :			List[str]="</s>"							,      _a   :			Union[str, Any]="</s>"							,      _a   :			int="<s>"							,      _a   :			Dict="<unk>"							,      _a   :			Optional[Any]="<pad>"							,      **_a   :			str							,      )			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=7
       _SCREAMING_SNAKE_CASE												=[f"<madeupword{i}>" for i in range(self.num_madeup_words				)]

       _SCREAMING_SNAKE_CASE												=kwargs.get('additional_special_tokens'							,      []				)
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           _a							,      tokenizer_file=_a							,      bos_token=_a							,      eos_token=_a							,      sep_token=_a							,      cls_token=_a							,      unk_token=_a							,      pad_token=_a							,      **_a							,      )

       _SCREAMING_SNAKE_CASE												=vocab_file
       _SCREAMING_SNAKE_CASE												=False if not self.vocab_file else True





  def 		A						(	self   :			Optional[Any]							,      _a   :			List[int]							,      _a   :			Optional[List[int]] = None				)			->					List[int]:





       '''simple docstring'''






       if token_ids_a is None:
            return [self.sep_token_id] + token_ids_a
       _SCREAMING_SNAKE_CASE												=[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=[self.sep_token_id]

       if token_ids_a is None:
            return len(sep + token_ids_a				) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a				) * [0]





  def 		A						(	self   :			Any							,      _a   :			str							,      _a   :			Optional[str] = None				)			->					Tuple[str]:





       '''simple docstring'''






       if not self.can_save_slow_tokenizer:
            raise ValueError(
                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                'tokenizer.'				)

       if not os.path.isdir(_a				):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory."				)
            return
       _SCREAMING_SNAKE_CASE												=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,)



'''simple docstring'''
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase				:   Dict      				=  datasets.utils.logging.get_logger(__name__)







class 							A__					( folder_based_builder.FolderBasedBuilderConfig						):
  A__					=       None
  A__					=       None







class 							A__					( folder_based_builder.FolderBasedBuilder						):
  A__					=       datasets.Image()
  A__					=       'image'
  A__					=       ImageFolderConfig
  A__					=       42  # definition at the bottom of the script
  A__					=       ImageClassification(image_column='image'  ,     label_column='label'						)

lowerCamelCase				:   Optional[int]      				=  [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
lowerCamelCase				:   Any      				=  IMAGE_EXTENSIONS
