




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)}""")






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

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


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}' )
			torch.save(model.state_dict()       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
