


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_lowercase       :      List[str]	=	logging.get_logger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple						) ->   Optional[int]:





	"""simple docstring"""







	A								=				r"""\w+[.]\d+"""
	A								=				re.findall(UpperCamelCase__							,					UpperCamelCase__						)
	for pat in pats:
		A								=				key.replace(UpperCamelCase__							,					"""_""".join(pat.split("""."""						)						)						)
	return key





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     str							,					UpperCamelCase__:     List[Any]						) ->   Tuple:





	"""simple docstring"""







	A								=				pt_tuple_key[:-1] + ("""scale""",)
	if (
	    any("""norm""" in str_ for str_ in pt_tuple_key						)
	    and (pt_tuple_key[-1] == "bias")
	    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
	    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
	):
		A								=				pt_tuple_key[:-1] + ("""scale""",)
		return renamed_pt_tuple_key, pt_tensor
	elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
		A								=				pt_tuple_key[:-1] + ("""scale""",)
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
		A								=				pt_tuple_key[:-1] + ("""embedding""",)
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	A								=				pt_tuple_key[:-1] + ("""kernel""",)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
		A								=				pt_tensor.transpose(2							,					3							,					1							,					0						)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	A								=				pt_tuple_key[:-1] + ("""kernel""",)
	if pt_tuple_key[-1] == "weight":
		A								=				pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm weight
	A								=				pt_tuple_key[:-1] + ("""weight""",)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	A								=				pt_tuple_key[:-1] + ("""bias""",)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Optional[int]=42						) ->   Optional[Any]:





	"""simple docstring"""







	A								=				{k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	A								=				flax_model.init_weights(PRNGKey(UpperCamelCase__						)						)

	A								=				flatten_dict(UpperCamelCase__						)
	A								=				{}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		A								=				rename_key(UpperCamelCase__						)
		A								=				tuple(renamed_pt_key.split("""."""						)						)

		# Correctly rename weight parameters
		A					,			A								=				rename_key_and_reshape_tensor(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)

		if flax_key in random_flax_state_dict:
			if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
				raise ValueError(
				    f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
				    f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'						)

        # also add unexpected weight so that warning is thrown
		A								=				jnp.asarray(UpperCamelCase__						)

	return unflatten_dict(UpperCamelCase__						)







import qiskit





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 2						) ->   qiskit.result.counts.Counts:





	"""simple docstring"""







	A								=				qubits

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

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

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

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

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

	# 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
	A								=				qiskit.execute(UpperCamelCase__							,					UpperCamelCase__							,					shots=10_00						)

	return job.result().get_counts(UpperCamelCase__						)


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



