


"""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  		=			"""\
@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
lowercase  		=			"""\
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
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 			SCREAMING_SNAKE_CASE_  (   datasets.GeneratorBasedBuilder):




							'''simple docstring'''





							__magic_name__					:     int				  =  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')
							__magic_name__					:     Any				  =  [
							    datasets.BuilderConfig(name='''first_domain'''	,      version=a__	,      description='''This part of my dataset covers a first domain'''),
							    datasets.BuilderConfig(name='''second_domain'''	,      version=a__	,      description='''This part of my dataset covers a second domain'''),
							]

							__magic_name__					:     Union[str, Any]				  =  '''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
									snake_case__  :						Union[str, 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"
									snake_case__  :						int		=      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=_lowerCamelCase	,					homepage=_HOMEPAGE	,					license=_LICENSE	,					citation=_CITATION	,					)




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




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







							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								with open(_lowerCamelCase	,					encoding="utf-8") as f:
									for key, row in enumerate(_lowerCamelCase):
										snake_case__  :						Optional[Any]		=      json.loads(_lowerCamelCase)
										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'''




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


snake_case__ 							=    logging.get_logger(__name__)

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      UpperCamelCase_  (a__  ):







						"""simple docstring"""


						_lowerCAmelCase         =       'nystromformer'







						def __init__(  self   :							Optional[Any]				,			_lowerCamelCase   :							str=30000				,			_lowerCamelCase   :							Optional[Any]=768				,			_lowerCamelCase   :							Optional[Any]=12				,			_lowerCamelCase   :							str=12				,			_lowerCamelCase   :							List[str]=3072				,			_lowerCamelCase   :							Any="gelu_new"				,			_lowerCamelCase   :							List[Any]=0.1				,			_lowerCamelCase   :							List[str]=0.1				,			_lowerCamelCase   :							List[str]=510				,			_lowerCamelCase   :							Optional[Any]=2				,			_lowerCamelCase   :							List[Any]=64				,			_lowerCamelCase   :							str=64				,			_lowerCamelCase   :							str=65				,			_lowerCamelCase   :							List[Any]=False				,			_lowerCamelCase   :							List[Any]=0.02				,			_lowerCamelCase   :							List[Any]=1E-5				,			_lowerCamelCase   :							Dict=1				,			_lowerCamelCase   :							Tuple=0				,			_lowerCamelCase   :							List[str]=2				,			**_lowerCamelCase   :							List[str]				,			):





											"""simple docstring"""







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



