


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
__UpperCAmelCase      			=  """\
@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
__UpperCAmelCase      			=  """\
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
__UpperCAmelCase      			=  """"""

# TODO: Add the licence for the dataset here if you can find it
__UpperCAmelCase      			=  """"""

# 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)
__UpperCAmelCase      			=  {
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}



class 							lowercase__(						datasets.GeneratorBasedBuilder			):






      '''simple docstring'''







      snake_case__       =					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__       =					[
          datasets.BuilderConfig(name='''first_domain'''			,							version=snake_case_			,							description='''This part of my dataset covers a first domain'''			),
          datasets.BuilderConfig(name='''second_domain'''			,							version=snake_case_			,							description='''This part of my dataset covers a second domain'''			),
      ]

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




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            UpperCamelCase__ :			Tuple 						=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[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=_A   ,			homepage=_HOMEPAGE   ,			license=_LICENSE   ,			citation=_CITATION   ,			)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=_URLS[self.config.name]
         UpperCamelCase__ :			str 						=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    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         with open(_A   ,			encoding="utf-8") as f:
            for key, row in enumerate(_A):
               UpperCamelCase__ :			str 						=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"],
                  }








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

