




import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       "\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n"

SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       "\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n"

SCREAMING_SNAKE_CASE__     :     Tuple		    =       "\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"precision\": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'precision@10': 1.0}\n\n"

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			return float((preds == labels).mean() )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						simple_accuracy(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						float(fa_score(y_true=SCREAMING_SNAKE_CASE       ,    y_pred=SCREAMING_SNAKE_CASE ) )
			return {
			    "accuracy": acc,
			    "f1": fa,
			}

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						np.array(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						np.array(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						en_sentvecs.shape[0]

			# mean centering
			SCREAMING_SNAKE_CASE_								=						en_sentvecs - np.mean(SCREAMING_SNAKE_CASE       ,    axis=0 )
			SCREAMING_SNAKE_CASE_								=						in_sentvecs - np.mean(SCREAMING_SNAKE_CASE       ,    axis=0 )

			SCREAMING_SNAKE_CASE_								=						cdist(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    'cosine' )
			SCREAMING_SNAKE_CASE_								=						np.array(range(SCREAMING_SNAKE_CASE ) )
			SCREAMING_SNAKE_CASE_								=						sim.argsort(axis=1 )[:, :10]
			SCREAMING_SNAKE_CASE_								=						np.any(preds == actual[:, None]       ,    axis=1 )
			return float(matches.mean() )



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





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				if self.config_name not in [
				    "wnli",
				    "copa",
				    "sna",
				    "csqa",
				    "wstp",
				    "inltkh",
				    "bbca",
				    "cvit-mkb-clsr",
				    "iitp-mr",
				    "iitp-pr",
				    "actsa-sc",
				    "md",
				    "wiki-ner",
				]:
							raise KeyError(
							    'You should supply a configuration name selected in '
							    '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
							    '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
							    '"wiki-ner"]'      )
				return datasets.MetricInfo(
				    description=_DESCRIPTION      ,     citation=_CITATION      ,     inputs_description=_KWARGS_DESCRIPTION      ,     features=datasets.Features(
				        {
				            'predictions': datasets.Value('int64'      )
				            if self.config_name != 'cvit-mkb-clsr'
				            else datasets.Sequence(datasets.Value('float32'      )      ),
				            'references': datasets.Value('int64'      )
				            if self.config_name != 'cvit-mkb-clsr'
				            else datasets.Sequence(datasets.Value('float32'      )      ),
				        }      )      ,     codebase_urls=[]      ,     reference_urls=[]      ,     format='numpy' if self.config_name != 'cvit-mkb-clsr' else None      ,     )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				if self.config_name == "cvit-mkb-clsr":
							return {"precision@10": precision_at_aa(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )}
				elif self.config_name in ["wiki-ner"]:
							return acc_and_fa(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
				elif self.config_name in [
				    "wnli",
				    "copa",
				    "sna",
				    "csqa",
				    "wstp",
				    "inltkh",
				    "bbca",
				    "iitp-mr",
				    "iitp-pr",
				    "actsa-sc",
				    "md",
				]:
							return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )}
				else:
							raise KeyError(
							    'You should supply a configuration name selected in '
							    '["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
							    '"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
							    '"wiki-ner"]'      )






import qiskit

def lowercase    (  SCREAMING_SNAKE_CASE = 2 )			->							qiskit.result.counts.Counts:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						qubits

			# Using Aer's simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.Aer.get_backend('aer_simulator' )

			# Creating a Quantum Circuit acting on the q register
			SCREAMING_SNAKE_CASE_								=						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Adding a H gate on qubit 0 (now q0 in superposition)
			circuit.h(0 )

			for i in range(1       ,    SCREAMING_SNAKE_CASE ):
						# Adding CX (CNOT) gate
						circuit.cx(i - 1       ,    SCREAMING_SNAKE_CASE )

			# Mapping the quantum measurement to the classical bits
			circuit.measure(list(range(SCREAMING_SNAKE_CASE ) )       ,    list(range(SCREAMING_SNAKE_CASE ) ) )

			# Now measuring any one qubit would affect other qubits to collapse
			# their super position and have same state as the measured one.

			# Executing the circuit on the simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.execute(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    shots=10_00 )

			return job.result().get_counts(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				print(f"""Total count for various states are: {quantum_entanglement(3)}""")
