






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
__snake_case     :     Any       =  """\\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
__snake_case     :     Dict       =  """\\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
__snake_case     :     Optional[Any]       =  """"""

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

# 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)
__snake_case     :     int       =  {
    """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):
						_SCREAMING_SNAKE_CASE						:							str				=       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')
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       [
						    datasets.BuilderConfig(name='''first_domain'''		,       version=__UpperCAmelCase		,       description='''This part of my dataset covers a first domain'''),
						    datasets.BuilderConfig(name='''second_domain'''		,       version=__UpperCAmelCase		,       description='''This part of my dataset covers a second domain'''),
						]

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






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												# 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=UpperCAmelCase_  ,    homepage=_HOMEPAGE  ,    license=_LICENSE  ,    citation=_CITATION  ,    )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												# 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(UpperCAmelCase_ )
												return [
												    datasets.SplitGenerator(
												        name=datasets.Split.TRAIN  ,    gen_kwargs={
												            'filepath': os.path.join(UpperCAmelCase_  ,    'train.jsonl' ),
												            'split': 'train',
												        }  ,    ),
												    datasets.SplitGenerator(
												        name=datasets.Split.VALIDATION  ,    gen_kwargs={
												            'filepath': os.path.join(UpperCAmelCase_  ,    'dev.jsonl' ),
												            'split': 'dev',
												        }  ,    ),
												    datasets.SplitGenerator(
												        name=datasets.Split.TEST  ,    gen_kwargs={
												            'filepath': os.path.join(UpperCAmelCase_  ,    'test.jsonl' ),
												            'split': 'test'
												        }  ,    ),
												]







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												# 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(UpperCAmelCase_  ,    encoding='utf-8' ) as f:
																		for key, row in enumerate(UpperCAmelCase_ ):
																								lowerCAmelCase__         =    json.loads(UpperCAmelCase_ )
																								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 numpy as np
def 						UpperCAmelCase   (						a_,		a_="utf-8",		a_="surrogatepass"     ):





		'''simple docstring'''


		lowerCamelCase	:     Dict            =       bin(int.from_bytes(text.encode(a_,		a_     ),		'big'     )     )[2:]
		return bits.zfill(8 * ((len(a_     ) + 7) // 8)     )
def 						UpperCAmelCase   (						a_,		a_="utf-8",		a_="surrogatepass"     ):





		'''simple docstring'''


		lowerCamelCase	:     Union[str, Any]            =       int(a_,		2     )
		return n.to_bytes((n.bit_length() + 7) // 8,		'big'     ).decode(a_,		a_     ) or "\0"
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		if size_par + len(a_     ) <= 2**size_par - (len(a_     ) - 1):
				raise ValueError('size of parity don\'t match with size of data'     )

		lowerCamelCase	:     Optional[Any]            =       []
		lowerCamelCase	:     Dict            =       []
		lowerCamelCase	:     str            =       [bin(a_     )[2:] for x in range(1,		size_par + len(a_     ) + 1     )]

		# sorted information data for the size of the output data
		lowerCamelCase	:     List[Any]            =       []
		# data position template + parity
		lowerCamelCase	:     Union[str, Any]            =       []
		# parity bit counter
		lowerCamelCase	:     Union[str, Any]            =       0
		# counter position of data bits
		lowerCamelCase	:     str            =       0

		for x in range(1,		size_par + len(a_     ) + 1     ):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(a_     ) / np.log(2     )).is_integer():
								data_out_gab.append('P'     )
								lowerCamelCase	:     int            =       qtd_bp + 1
						else:
								data_out_gab.append('D'     )
				else:
						data_out_gab.append('D'     )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]     )
						cont_data += 1
				else:
						data_ord.append(a_     )

    # Calculates parity
		lowerCamelCase	:     Union[str, Any]            =       0  # parity bit counter
		for bp in range(1,		size_par + 1     ):
				# Bit counter one for a given parity
				lowerCamelCase	:     Optional[int]            =       0
				# counter to control the loop reading
				lowerCamelCase	:     int            =       0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase	:     Optional[int]            =       (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase	:     List[Any]            =       '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2     )

				qtd_bp += 1

		# Mount the message
		lowerCamelCase	:     Dict            =       0  # parity bit counter
		for x in range(0,		size_par + len(a_     )     ):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]     )     )
						cont_bp += 1
				else:
						data_out.append(data_ord[x]     )

		return data_out
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       []
		# Parity bit counter
		lowerCamelCase	:     Optional[Any]            =       0
		# Counter p data bit reading
		lowerCamelCase	:     int            =       0
		# list of parity received
		lowerCamelCase	:     Any            =       []
		lowerCamelCase	:     Optional[int]            =       []

		for x in range(1,		len(a_     ) + 1     ):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(a_     ) / np.log(2     )).is_integer():
						data_out_gab.append('P'     )
						lowerCamelCase	:     Tuple            =       qtd_bp + 1
				else:
						data_out_gab.append('D'     )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]     )
				else:
						parity_received.append(data[cont_data]     )
				cont_data += 1

		# -----------calculates the parity with the data
		lowerCamelCase	:     Tuple            =       []
		lowerCamelCase	:     str            =       []
		lowerCamelCase	:     Optional[Any]            =       [bin(a_     )[2:] for x in range(1,		size_par + len(a_     ) + 1     )]

		#  sorted information data for the size of the output data
		lowerCamelCase	:     Dict            =       []
		# Data position feedback + parity
		lowerCamelCase	:     Union[str, Any]            =       []
		# Parity bit counter
		lowerCamelCase	:     Union[str, Any]            =       0
		# Counter p data bit reading
		lowerCamelCase	:     List[Any]            =       0

		for x in range(1,		size_par + len(a_     ) + 1     ):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(a_     ) / np.log(2     )).is_integer():
						data_out_gab.append('P'     )
						lowerCamelCase	:     Any            =       qtd_bp + 1
				else:
						data_out_gab.append('D'     )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]     )
						cont_data += 1
				else:
						data_ord.append(a_     )

    # Calculates parity
		lowerCamelCase	:     Optional[int]            =       0  # parity bit counter
		for bp in range(1,		size_par + 1     ):
				# Bit counter one for a certain parity
				lowerCamelCase	:     Tuple            =       0
				# Counter to control loop reading
				lowerCamelCase	:     Any            =       0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase	:     List[str]            =       (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase	:     str            =       '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2     )     )

				qtd_bp += 1

		# Mount the message
		lowerCamelCase	:     Optional[int]            =       0  # Parity bit counter
		for x in range(0,		size_par + len(a_     )     ):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]     )     )
						cont_bp += 1
				else:
						data_out.append(data_ord[x]     )

		lowerCamelCase	:     Any            =       parity_received == parity
		return data_output, ack






