# This code is part of Qiskit.
#
# (C) Copyright IBM 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Gradient of probabilities with parameter shift
"""

from __future__ import annotations

from collections import defaultdict
from collections.abc import Sequence

from qiskit.circuit import Parameter, QuantumCircuit

from .base_sampler_gradient import BaseSamplerGradient
from .sampler_gradient_result import SamplerGradientResult
from .utils import _make_param_shift_parameter_values

from ..exceptions import AlgorithmError


class ParamShiftSamplerGradient(BaseSamplerGradient):
    """
    Compute the gradients of the sampling probability by the parameter shift rule [1].

    **Reference:**
    [1] Schuld, M., Bergholm, V., Gogolin, C., Izaac, J., and Killoran, N. Evaluating analytic
    gradients on quantum hardware, `DOI <https://doi.org/10.1103/PhysRevA.99.032331>`_
    """

    SUPPORTED_GATES = [
        "x",
        "y",
        "z",
        "h",
        "rx",
        "ry",
        "rz",
        "p",
        "cx",
        "cy",
        "cz",
        "ryy",
        "rxx",
        "rzz",
        "rzx",
    ]

    def _run(
        self,
        circuits: Sequence[QuantumCircuit],
        parameter_values: Sequence[Sequence[float]],
        parameters: Sequence[Sequence[Parameter]],
        **options,
    ) -> SamplerGradientResult:
        """Compute the estimator gradients on the given circuits."""
        g_circuits, g_parameter_values, g_parameters = self._preprocess(
            circuits, parameter_values, parameters, self.SUPPORTED_GATES
        )
        results = self._run_unique(g_circuits, g_parameter_values, g_parameters, **options)
        return self._postprocess(results, circuits, parameter_values, parameters)

    def _run_unique(
        self,
        circuits: Sequence[QuantumCircuit],
        parameter_values: Sequence[Sequence[float]],
        parameters: Sequence[Sequence[Parameter]],
        **options,
    ) -> SamplerGradientResult:
        """Compute the sampler gradients on the given circuits."""
        job_circuits, job_param_values, metadata = [], [], []
        all_n = []
        for circuit, parameter_values_, parameters_ in zip(circuits, parameter_values, parameters):
            metadata.append({"parameters": parameters_})
            # Make parameter values for the parameter shift rule.
            param_shift_parameter_values = _make_param_shift_parameter_values(
                circuit, parameter_values_, parameters_
            )
            # Combine inputs into a single job to reduce overhead.
            n = len(param_shift_parameter_values)
            job_circuits.extend([circuit] * n)
            job_param_values.extend(param_shift_parameter_values)
            all_n.append(n)

        # Run the single job with all circuits.
        job = self._sampler.run(job_circuits, job_param_values, **options)
        try:
            results = job.result()
        except Exception as exc:
            raise AlgorithmError("Estimator job failed.") from exc

        # Compute the gradients.
        gradients = []
        partial_sum_n = 0
        for n in all_n:
            gradient = []
            result = results.quasi_dists[partial_sum_n : partial_sum_n + n]
            for dist_plus, dist_minus in zip(result[: n // 2], result[n // 2 :]):
                grad_dist: dict[int, float] = defaultdict(float)
                for key, val in dist_plus.items():
                    grad_dist[key] += val / 2
                for key, val in dist_minus.items():
                    grad_dist[key] -= val / 2
                gradient.append(dict(grad_dist))
            gradients.append(gradient)
            partial_sum_n += n

        opt = self._get_local_options(options)
        return SamplerGradientResult(gradients=gradients, metadata=metadata, options=opt)