
"""simple docstring"""




import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def lowercase_  (   _snake_case = 3						):
							if isinstance(_snake_case   ,_snake_case						):
														raise TypeError("""number of qubits must be a integer."""						)
							if number_of_qubits <= 0:
														raise ValueError("""number of qubits must be > 0."""						)
							if math.floor(_snake_case						) != number_of_qubits:
														raise ValueError("""number of qubits must be exact integer."""						)
							if number_of_qubits > 10:
														raise ValueError("""number of qubits too large to simulate(>10)."""						)

							SCREAMING_SNAKE_CASE__						:       str           =   QuantumRegister(_snake_case   ,"""qr"""						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   ClassicalRegister(_snake_case   ,"""cr"""						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   QuantumCircuit(_snake_case   ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:       int           =   number_of_qubits

							for i in range(_snake_case						):
														quantum_circuit.h(number_of_qubits - i - 1						)
														counter -= 1
														for j in range(_snake_case						):
																					quantum_circuit.cp(np.pi / 2 ** (counter - j)   ,_snake_case   ,_snake_case						)

							for k in range(number_of_qubits // 2						):
														quantum_circuit.swap(_snake_case   ,number_of_qubits - k - 1						)

							# measure all the qubits
							quantum_circuit.measure(_snake_case   ,_snake_case						)
							# simulate with 10000 shots
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   Aer.get_backend("""qasm_simulator"""						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   execute(_snake_case   ,_snake_case   ,shots=10_000						)

							return job.result().get_counts(_snake_case						)


if __name__ == "__main__":
					print(
					    f"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
					)






"""simple docstring"""




import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase__      :    List[str]    							= '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
UpperCAmelCase__      :    List[Any]    							= [
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   SavedModel()
							SCREAMING_SNAKE_CASE__						:       Dict           =   []

							with open(os.path.join(_snake_case   ,"""utils"""   ,"""tf_ops"""   ,"""onnx.json"""						)						) as f:
														SCREAMING_SNAKE_CASE__						:       Any           =   json.load(_snake_case						)["""opsets"""]

							for i in range(1   ,opset + 1						):
														onnx_ops.extend(onnx_opsets[str(_snake_case						)]						)

							with open(_snake_case   ,"""rb"""						) as f:
														saved_model.ParseFromString(f.read()						)

							SCREAMING_SNAKE_CASE__						:       List[str]           =   set()

							# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
							for meta_graph in saved_model.meta_graphs:
														# Add operations in the graph definition
														model_op_names.update(node.op for node in meta_graph.graph_def.node						)

														# Go through the functions in the graph definition
														for func in meta_graph.graph_def.library.function:
																					# Add operations in each function
																					model_op_names.update(node.op for node in func.node_def						)

    # Convert to list, sorted if you want
							SCREAMING_SNAKE_CASE__						:       int           =   sorted(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   []

							for op in model_op_names:
														if op not in onnx_ops and op not in INTERNAL_OPS:
																					incompatible_ops.append(_snake_case						)

							if strict and len(_snake_case						) > 0:
														raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
							elif len(_snake_case						) > 0:
														print(f'''Found the following incompatible ops for the opset {opset}:'''						)
														print(*_snake_case   ,sep="""\n"""						)
							else:
														print(f'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
					UpperCAmelCase__      :    Optional[int]    							= argparse.ArgumentParser()
					parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
					parser.add_argument(
					    '--opset', default=1_2, type=int, help='The ONNX opset against which the model has to be tested.'
					)
					parser.add_argument(
					    '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
					)
					parser.add_argument(
					    '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
					)
					UpperCAmelCase__      :    Dict    							= parser.parse_args()

					if args.framework == "onnx":
										onnx_compliancy(args.saved_model_path, args.strict, args.opset)




