"""simple docstring"""
import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
lowercase__   			=      '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
lowercase__   			=      '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
lowercase__   			=      ''

# TODO: Add the licence for the dataset here if you can find it
lowercase__   			=      ''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
lowercase__   			=      {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}




class   __snake_case			(	datasets.GeneratorBasedBuilder							):
      a__								=						datasets.Version("""1.1.0"""							)

      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig

      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__								=						[
          datasets.BuilderConfig(name="""first_domain"""			, version=__lowerCAmelCase			, description="""This part of my dataset covers a first domain"""							),
          datasets.BuilderConfig(name="""second_domain"""			, version=__lowerCAmelCase			, description="""This part of my dataset covers a second domain"""							),
      ]

      a__								=						"""first_domain"""  # It's not mandatory to have a default configuration. Just use one if it make sense.



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
          a__:	Optional[int]     				=      datasets.Features(
              {
                  'sentence': datasets.Value('string'),
                  'option1': datasets.Value('string'),
                  'answer': datasets.Value('string')
                  # These are the features of your dataset like images, labels ...
              })
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
          a__:	Any     				=      datasets.Features(
              {
                  'sentence': datasets.Value('string'),
                  'option2': datasets.Value('string'),
                  'second_domain_answer': datasets.Value('string')
                  # These are the features of your dataset like images, labels ...
              })
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION       ,     features=lowercase       ,     homepage=_HOMEPAGE       ,     license=_LICENSE       ,     citation=_CITATION       ,     )



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Tuple     				=      _URLS[self.config.name]
        a__:	List[str]     				=      dl_manager.download_and_extract(lowercase)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN       ,     gen_kwargs={
                    'filepath': os.path.join(lowercase       ,     'train.jsonl'),
                    'split': 'train',
                }       ,     ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION       ,     gen_kwargs={
                    'filepath': os.path.join(lowercase       ,     'dev.jsonl'),
                    'split': 'dev',
                }       ,     ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST       ,     gen_kwargs={
                    'filepath': os.path.join(lowercase       ,     'test.jsonl'),
                    'split': 'test'
                }       ,     ),
        ]



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        with open(lowercase       ,     encoding='utf-8') as f:
          for key, row in enumerate(lowercase):
            a__:	List[Any]     				=      json.loads(lowercase)
            if self.config.name == "first_domain":
              # Yields examples as (key, example) tuples
              yield key, {
                  "sentence": data["sentence"],
                  "option1": data["option1"],
                  "answer": "" if split == "test" else data["answer"],
              }
            else:
              yield key, {
                  "sentence": data["sentence"],
                  "option2": data["option2"],
                  "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
              }









'''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__      :   List[str]      	=			None


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

lowerCAmelCase__      :   Tuple      	=			{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__      :   List[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__      :   int      	=			{
    "facebook/xglm-564M": 20_48,
}


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					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''', '''attention_mask''']
					SCREAMING_SNAKE_CASE							=	XGLMTokenizer
					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      str="<s>"		,	UpperCAmelCase_       :      List[Any]="</s>"		,	UpperCAmelCase_       :      Dict="</s>"		,	UpperCAmelCase_       :      Optional[Any]="<s>"		,	UpperCAmelCase_       :      List[str]="<unk>"		,	UpperCAmelCase_       :      Union[str, Any]="<pad>"		,	**UpperCAmelCase_       :      List[Any]		,	):







							"""simple docstring"""
							# Compatibility with the original tokenizer
							__UpperCAmelCase	:							Union[str, Any]     =     7
							__UpperCAmelCase	:							Tuple     =     [f"<madeupword{i}>" for i in range(self.num_madeup_words		)]

							__UpperCAmelCase	:							List[Any]     =     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__(
							    UpperCAmelCase_		,	tokenizer_file=UpperCAmelCase_		,	bos_token=UpperCAmelCase_		,	eos_token=UpperCAmelCase_		,	sep_token=UpperCAmelCase_		,	cls_token=UpperCAmelCase_		,	unk_token=UpperCAmelCase_		,	pad_token=UpperCAmelCase_		,	**UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Dict     =     vocab_file
							__UpperCAmelCase	:							Optional[int]     =     False if not self.vocab_file else True
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""

							if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
							__UpperCAmelCase	:							Any     =     [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     [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 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      Optional[str] = None		):







							"""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(UpperCAmelCase_		):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
									return
							__UpperCAmelCase	:							str     =     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,)




