
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
__magic_name__  :  Union[str, 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
__magic_name__  :  Union[str, Any]     			=   '\\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
__magic_name__  :  Optional[int]     			=   ''

# TODO: Add the licence for the dataset here if you can find it
__magic_name__  :  str     			=   ''

# 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)
__magic_name__  :  List[Any]     			=   {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}


class 							lowerCamelCase       (		datasets.GeneratorBasedBuilder					):



  """simple docstring"""





  lowerCAmelCase_								=		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')
  lowerCAmelCase_								=		[
      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"""					),
  ]

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

  def 					lowercase_						(					self					):
      # 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
          A_								=					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"
          A_								=					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 					lowercase_						(					self						,    __UpperCamelCase					):
      # 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
      A_								=					_URLS[self.config.name]
      A_								=					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 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      # 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					):
              A_								=					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"],
                  }








import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def        lowerCAmelCase       (				snake_case__   :      int = 1				,					snake_case__   :      int = 1 )->			qiskit.result.counts.Counts:
    if isinstance(snake_case__				,					snake_case__ ) or isinstance(snake_case__				,					snake_case__ ):
        raise TypeError("inputs must be integers." )
    if (bit_a < 0) or (bit_a < 0):
        raise ValueError("inputs must be positive." )
    if (math.floor(snake_case__ ) != bit_a) or (math.floor(snake_case__ ) != bit_a):
        raise ValueError("inputs must be exact integers." )
    if (bit_a > 1) or (bit_a > 1):
        raise ValueError("inputs must be less or equal to 1." )

    # build registers
    A_								=					QuantumRegister(2				,					"qr" )
    A_								=					ClassicalRegister(2				,					"cr" )

    A_								=					QuantumCircuit(snake_case__				,					snake_case__ )

    # entanglement the qubits
    quantum_circuit.h(1 )
    quantum_circuit.cx(1				,					0 )

    # send the information
    A_								=					str(snake_case__ ) + str(snake_case__ )

    if c_information == "11":
        quantum_circuit.x(1 )
        quantum_circuit.z(1 )
    elif c_information == "10":
        quantum_circuit.z(1 )
    elif c_information == "01":
        quantum_circuit.x(1 )
    else:
        quantum_circuit.i(1 )

    # unentangled the circuit
    quantum_circuit.cx(1				,					0 )
    quantum_circuit.h(1 )

    # measure the circuit
    quantum_circuit.measure(snake_case__				,					snake_case__ )

    A_								=					Aer.get_backend("aer_simulator" )
    A_								=					execute(snake_case__				,					snake_case__				,					shots=1000 )

    return job.result().get_counts(snake_case__ )


if __name__ == "__main__":
     print(f"""Counts for classical state send: {superdense_coding(1,1)}""")






