



'''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
a  :     List[Any]       =       """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
a  :     Union[str, Any]       =       """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
a  :     List[str]       =       """"""

# TODO: Add the licence for the dataset here if you can find it
a  :     Optional[Any]       =       """"""

# 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)
a  :     Optional[Any]       =       {
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}

class      UpperCamelCase_					(				datasets.GeneratorBasedBuilder     ):
	lowercase		=					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')
	lowercase		=					[
	    datasets.BuilderConfig(name='first_domain' ,	version=__magic_name__ ,	description='This part of my dataset covers a first domain'     ),
	    datasets.BuilderConfig(name='second_domain' ,	version=__magic_name__ ,	description='This part of my dataset covers a second domain'     ),
	]

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





	def 					_lowercase(    self       )     ->     List[str]:
								# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
								if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
															UpperCAmelCase	:						List[Any]					=						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"
															UpperCAmelCase	:						List[str]					=						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=A						,						homepage=_HOMEPAGE						,						license=_LICENSE						,						citation=_CITATION						,						)





	def 					_lowercase(    self						,						A       )     ->     Optional[int]:
								# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
								# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

								# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
								# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
								# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
								UpperCAmelCase	:						Optional[Any]					=						_URLS[self.config.name]
								UpperCAmelCase	:						Optional[int]					=						dl_manager.download_and_extract(A       )
								return [
								    datasets.SplitGenerator(
								        name=datasets.Split.TRAIN						,						gen_kwargs={
								            """filepath""": os.path.join(A						,						"""train.jsonl"""       ),
								            """split""": """train""",
								        }						,						),
								    datasets.SplitGenerator(
								        name=datasets.Split.VALIDATION						,						gen_kwargs={
								            """filepath""": os.path.join(A						,						"""dev.jsonl"""       ),
								            """split""": """dev""",
								        }						,						),
								    datasets.SplitGenerator(
								        name=datasets.Split.TEST						,						gen_kwargs={
								            """filepath""": os.path.join(A						,						"""test.jsonl"""       ),
								            """split""": """test"""
								        }						,						),
								]






	def 					_lowercase(    self						,						A						,						A       )     ->     Union[str, Any]:
								# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
								# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
								with open(A						,						encoding="""utf-8"""       ) as f:
															for key, row in enumerate(A       ):
																						UpperCAmelCase	:						Union[str, Any]					=						json.loads(A       )
																						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 AddedToken, PreTrainedTokenizerFast
from ...utils import logging


a  :     Any       =       logging.get_logger(__name__)

a  :     int       =       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

a  :     List[Any]       =       {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}

class      UpperCamelCase_					(				__magic_name__     ):





	def __init__(    self						,						A=None						,						A=None						,						A=False						,						A=True						,						A=False						,						A="<s>"						,						A="</s>"						,						A="<unk>"						,						A="<sep>"						,						A="<pad>"						,						A="<cls>"						,						A="<mask>"						,						A=["<eop>", "<eod>"]						,						**A						,						)     ->     Tuple:
								UpperCAmelCase	:						str					=						AddedToken(A						,						lstrip=A						,						rstrip=A       ) if isinstance(A						,						A       ) else mask_token

								super().__init__(
								    vocab_file=A						,						tokenizer_file=A						,						do_lower_case=A						,						remove_space=A						,						keep_accents=A						,						bos_token=A						,						eos_token=A						,						unk_token=A						,						sep_token=A						,						pad_token=A						,						cls_token=A						,						mask_token=A						,						additional_special_tokens=A						,						**A						,						)

								UpperCAmelCase	:						List[Any]					=						3
								UpperCAmelCase	:						Tuple					=						do_lower_case
								UpperCAmelCase	:						Union[str, Any]					=						remove_space
								UpperCAmelCase	:						Tuple					=						keep_accents
								UpperCAmelCase	:						Any					=						vocab_file
								UpperCAmelCase	:						Tuple					=						False if not self.vocab_file else True

								try:
															import jieba
								except ModuleNotFoundError as error:
															raise error.__class__(
															    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
															    """See https://pypi.org/project/jieba/ for installation."""       )
								UpperCAmelCase	:						int					=						jieba
								UpperCAmelCase	:						Optional[int]					=						str.maketrans(""" \n"""						,						"""\u2582\u2583"""       )





	def 					_lowercase(    self						,						A						,						A = None       )     ->     List[int]:
								UpperCAmelCase	:						Optional[int]					=						[self.sep_token_id]
								UpperCAmelCase	:						str					=						[self.cls_token_id]
								if token_ids_a is None:
															return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls





	def 					_lowercase(    self						,						A						,						A = None       )     ->     List[int]:
								UpperCAmelCase	:						Union[str, Any]					=						[self.sep_token_id]
								UpperCAmelCase	:						str					=						[2]

								if token_ids_a is None:
															return len(token_ids_a + sep       ) * [0] + cls_segment_id
								return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1] + cls_segment_id





	def 					_lowercase(    self						,						A						,						A = None       )     ->     Tuple[str]:
								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
								UpperCAmelCase	:						Tuple					=						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,)





	def 					_lowercase(    self						,						A						,						*A						,						**A       )     ->     List[Any]:
								UpperCAmelCase	:						Optional[Any]					=						[
								    """ """.join([x.translate(self.translator       ) for x in self.jieba.cut(A						,						cut_all=A       )]       )
								    for text in batch_text_or_text_pairs
								]
								return super()._batch_encode_plus(A						,						*A						,						**A       )






	def 					_lowercase(    self						,						*A						,						**A       )     ->     str:
								UpperCAmelCase	:						Optional[int]					=						super()._decode(*A						,						**A       )
								UpperCAmelCase	:						int					=						text.replace(""" """						,						""""""       ).replace("""\u2582"""						,						""" """       ).replace("""\u2583"""						,						"""\n"""       )
								return text

