




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
__SCREAMING_SNAKE_CASE			:      List[str]      			="""\
@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
__SCREAMING_SNAKE_CASE			:      Optional[int]      			="""\
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
__SCREAMING_SNAKE_CASE			:      List[Any]      			=""""""

# TODO: Add the licence for the dataset here if you can find it
__SCREAMING_SNAKE_CASE			:      List[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)
__SCREAMING_SNAKE_CASE			:      Any      			={
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}






class 	A	(						datasets.GeneratorBasedBuilder						):
     """simple docstring"""

     _snake_case:		Any   =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')
     _snake_case:		Dict   =[
         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"						),
     ]

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




     def        __snake_case      (			self     :      Optional[int]	) ->				int:
           # 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
                 lowerCamelCase__					     = 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"
                 lowerCamelCase__					     = 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        __snake_case      (			self     :      int		,     A__     :      Tuple	) ->				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
           lowerCamelCase__					     = _URLS[self.config.name]
           lowerCamelCase__					     = 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        __snake_case      (			self     :      Any		,     A__     :      Dict		,     A__     :      Tuple	) ->				List[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__	):
                       lowerCamelCase__					     = 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"],
                             }





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]