
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






@contextmanager
def     lowerCamelCase__				(   ) ->  Dict:
   def filter_log(a ):
      return False if "This IS expected if you are initializing" in record.msg else True

   _A:    Optional[int]						     =			datasets.utils.logging.get_logger('''transformers.modeling_utils''' )
   logger.addFilter(a )
   try:
      yield
   finally:
      logger.removeFilter(a )


UpperCAmelCase__						:	Dict	     = '\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

UpperCAmelCase__						:	List[Any]	     = '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

UpperCAmelCase__						:	Optional[Any]	     = '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,				_KWARGS_DESCRIPTION  )
class UpperCAmelCase  (		datasets.Metric  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				str	):

      """simple docstring"""
      return datasets.MetricInfo(
          description=_DESCRIPTION     ,     citation=_CITATION     ,     homepage='''https://github.com/Tiiiger/bert_score'''     ,     inputs_description=_KWARGS_DESCRIPTION     ,     features=datasets.Features(
              {
                  '''predictions''': datasets.Value('''string'''     ,     id='''sequence'''	),
                  '''references''': datasets.Sequence(datasets.Value('''string'''     ,     id='''sequence'''	)     ,     id='''references'''	),
              }	)     ,     codebase_urls=['''https://github.com/Tiiiger/bert_score''']     ,     reference_urls=[
              '''https://github.com/Tiiiger/bert_score''',
              '''https://arxiv.org/abs/1904.09675''',
          ]     ,     )


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				List[Any]     ,     lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				Optional[int]=None     ,     lowerCAmelCase_	:				str=None     ,     lowerCAmelCase_	:				Dict=None     ,     lowerCAmelCase_	:				Optional[Any]=False     ,     lowerCAmelCase_	:				List[Any]=False     ,     lowerCAmelCase_	:				Optional[int]=None     ,     lowerCAmelCase_	:				Optional[Any]=6_4     ,     lowerCAmelCase_	:				int=4     ,     lowerCAmelCase_	:				Tuple=False     ,     lowerCAmelCase_	:				List[Any]=False     ,     lowerCAmelCase_	:				Optional[Any]=None     ,     lowerCAmelCase_	:				str=False     ,     ):

      """simple docstring"""
      _A:    Optional[int]						     =			bert_score.utils.get_hash
      _A:    Optional[int]						     =			bert_score.BERTScorer

      if version.parse(bert_score.__version__	) >= version.parse('''0.3.10'''	):
         _A:    Tuple						     =			functools.partial(lowerCAmelCase_     ,     use_fast_tokenizer=lowerCAmelCase_	)
         _A:    List[Any]						     =			functools.partial(lowerCAmelCase_     ,     use_fast_tokenizer=lowerCAmelCase_	)
      elif use_fast_tokenizer:
         raise ImportWarning(
             '''To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'''
             '''You can install it with `pip install "bert-score>=0.3.10"`.'''	)

      if model_type is None:
         assert lang is not None, "either lang or model_type should be specified"
         _A:    Union[str, Any]						     =			bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
         _A:    Optional[Any]						     =			bert_score.utils.modelalayers[model_type]

      _A:    Optional[Any]						     =			get_hash(
          model=lowerCAmelCase_     ,     num_layers=lowerCAmelCase_     ,     idf=lowerCAmelCase_     ,     rescale_with_baseline=lowerCAmelCase_     ,     use_custom_baseline=baseline_path is not None     ,     )

      with filter_logging_context():
         if not hasattr(self     ,     '''cached_bertscorer'''	) or self.cached_bertscorer.hash != hashcode:
            _A:    Optional[Any]						     =			scorer(
                model_type=lowerCAmelCase_     ,     num_layers=lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     nthreads=lowerCAmelCase_     ,     all_layers=lowerCAmelCase_     ,     idf=lowerCAmelCase_     ,     device=lowerCAmelCase_     ,     lang=lowerCAmelCase_     ,     rescale_with_baseline=lowerCAmelCase_     ,     baseline_path=lowerCAmelCase_     ,     )

      ((_A)     ,    (_A)     ,    (_A)):    List[str]						     =			self.cached_bertscorer.score(
          cands=lowerCAmelCase_     ,     refs=lowerCAmelCase_     ,     verbose=lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     )
      _A:    int						     =			{
          '''precision''': P.tolist(),
          '''recall''': R.tolist(),
          '''f1''': F.tolist(),
          '''hashcode''': hashcode,
      }
      return output_dict


   def 					__magic_name__							(   self	:				List[str]     ,     lowerCAmelCase_	:				Optional[int]=None     ,     lowerCAmelCase_	:				Any=None     ,     **lowerCAmelCase_	:				List[Any]	):

      """simple docstring"""
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if references is not None:
         _A:    Optional[Any]						     =			[[ref] if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else ref for ref in references]
      super().add_batch(predictions=lowerCAmelCase_     ,     references=lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				Optional[int]=None     ,     lowerCAmelCase_	:				List[str]=None     ,     **lowerCAmelCase_	:				Optional[Any]	):

      """simple docstring"""
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	):
         _A:    int						     =			[reference]
      super().add(prediction=lowerCAmelCase_     ,     reference=lowerCAmelCase_     ,     **lowerCAmelCase_	)





import numpy as np
import qiskit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    List[Any]						     =			qiskit.QuantumCircuit(num_qubits + 1 )

   # First, let's deal with the case in which oracle is balanced
   if case == "balanced":
      # First generate a random number that tells us which CNOTs to
      # wrap in X-gates:
      _A:    Any						     =			np.random.randint(1						,						2**num_qubits )
      # Next, format 'b' as a binary string of length 'n', padded with zeros:
      _A:    int						     =			format(a						,						f"""0{num_qubits}b""" )
      # Next, we place the first X-gates. Each digit in our binary string
      # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
      # we apply an X-gate to that qubit:
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
      for index in range(a ):
         oracle_qc.cx(a						,						a )
      # Next, place the final X-gates
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )

    # Case in which oracle is constant
   if case == "constant":
      # First decide what the fixed output of the oracle will be
      # (either always 0 or always 1)
      _A:    int						     =			np.random.randint(2 )
      if output == 1:
         oracle_qc.x(a )

   _A:    int						     =			oracle_qc.to_gate()
   _A:    str						     =			'''Oracle'''  # To show when we display the circuit
   return oracle_gate






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    Optional[int]						     =			qiskit.QuantumCircuit(num_qubits + 1						,						a )
   # Set up the output qubit:
   dj_circuit.x(a )
   dj_circuit.h(a )
   # And set up the input register:
   for qubit in range(a ):
      dj_circuit.h(a )
   # Let's append the oracle gate to our circuit:
   dj_circuit.append(a						,						range(num_qubits + 1 ) )
   # Finally, perform the H-gates again and measure:
   for qubit in range(a ):
      dj_circuit.h(a )

   for i in range(a ):
      dj_circuit.measure(a						,						a )

   return dj_circuit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.result.counts.Counts:
   _A:    int						     =			qiskit.Aer.get_backend('''aer_simulator''' )

   _A:    Optional[Any]						     =			dj_oracle(a						,						a )
   _A:    int						     =			dj_algorithm(a						,						a )

   # Execute the circuit on the simulator
   _A:    int						     =			qiskit.execute(a						,						a						,						shots=10_00 )

   # Return the histogram data of the results of the experiment.
   return job.result().get_counts(a )


if __name__ == "__main__":
   print(F"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}""")
   print(F"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}""")



