#   Copyright 2020 Google LLC

#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.
"""Tensor utilities to evaluate symmetry and eventually create dense storage."""

from collections import deque
from typing import Any, Deque, List, Tuple

import numpy


def build_symmetry_operations(symmetry: List[Any]) -> None:
    """Take the list of allowed permutations and build the symmetry operations
    allowed for the operation, modifying `symmetry` in place.

    Args:
        symmetry (List): A list containing all the allowed permutations.
    """
    dim = len(symmetry[0][0])
    unit = numpy.identity(dim, dtype=int)
    for permutation in symmetry:
        perm = unit[:, numpy.argsort(permutation[0])]
        permutation[0] = perm


def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) -> None:
    """Digest the allowed permutations to validate the underlying structure.

    Args:
        mat (numpy.ndarray): Matrix to confirm symmetry in.

        symmetry (List): A list containing all the information regarding \
            symmetry of the matrix. The first element can be an identity \
            element with the indices in order, a parity of 1.0 and no complex \
            conjugation.  Each entry should specify the action of the symmetry \
            on the indexes, a parity associated with the permutation and \
            whether the term should be conjugated. The first term should be \
            the unity operation.
    """
    is_unity = validate_unity(symmetry[0])
    if len(symmetry) == 1 and is_unity:
        return
    build_symmetry_operations(symmetry)
    validate_matrix_symmetry(mat, symmetry)


def index_queue(dim: int, highest_index: int) -> Deque[Tuple[int, ...]]:
    """Generate all index pointers into the matrix of interest.

    Args:
        dim (int): The size of the matrix of interest.

        highest_index (int): The maximum value allowable in the matrix.

    Returns:
        Deque[Tuple[int, ...]]: A queue containing all possible pointers \
            into the matrix.
    """
    i_queue: List[Tuple[int, ...]] = []
    if dim == 1:
        for i in range(highest_index):
            i_queue.append(tuple([i]))
    else:
        total = highest_index**dim - 1
        ticker = [0 for _ in range(dim)]
        i_queue.append(tuple(ticker))

        for _ in range(total):
            for i in reversed(range(dim)):
                ticker[i] += 1
                if ticker[i] < highest_index:
                    break
                ticker[i] = 0
            i_queue.append(tuple(ticker))

    return deque(i_queue)


def validate_matrix_symmetry(matrix: numpy.ndarray,
                             symmetry: List[Any],
                             threshold: float = 1.0e-8) -> None:
    """Go through every element of the matrix and check that the symmetry
    operations are valid up to a threshold.

    Args:
        matrix (numpy.ndarray): A matrix of interest.

        symmetry (List): Symmetry that should be validated.

        threshold (float): The limit at which a symmetry operation is valid.

    Raises:
        ValueError: If there is an error with a symmetry in a permutation.
    """
    all_index = index_queue(len(matrix.shape), matrix.shape[0])
    while all_index:
        index = all_index.popleft()
        value = matrix[index]
        for permu in symmetry[1:]:
            test_index = tuple(numpy.dot(index, permu[0]))
            test_value = matrix[test_index]

            if permu[2]:
                ref_value = permu[1] * numpy.conj(value)
            else:
                ref_value = permu[1] * value

            if numpy.abs(test_value - ref_value) > threshold:
                raise ValueError("Error with symmetry in permutation {} -> {}."
                                 " {} != {}".format(index, test_index,
                                                    ref_value, test_value))

            try:
                all_index.remove(test_index)
            except ValueError:
                pass


def validate_unity(unity_permutation: List[Any]) -> bool:
    """Checks that the inumpyut permutation is the unity permutation, i.e., an
    object s of type List[List[int], float, bool] such that

    * s[0][0] > -1,
    * s[0][i] < s[0][j] for i < j,
    * s[1] = 1.0, and
    * s[2] = False.

    Args:
        unity_permutation (list): A permutation object to compare to the unity
            operation.

    Returns:
        (bool): True if the inumpyut permutation is the unity permutation, \
            else False.

    Raises:
        ValueError: If the inumpyut permutation is invalid.
    """
    if unity_permutation[1] != 1.0:
        raise ValueError("The unity permutation does not have a phase of 1.0")

    if unity_permutation[2]:
        return False

    static = unity_permutation[0]
    lowlimit = -1

    for index in static:
        if index < lowlimit:
            raise ValueError("The first entry is not unity")
        lowlimit = index

    return True

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def build_symmetry_operations(symmetry: List[Any]) ->None:
    """Take the list of allowed permutations and build the symmetry operations
    allowed for the operation, modifying `symmetry` in place.

    Args:
        symmetry (List): A list containing all the allowed permutations.
    """
    dim = len(symmetry[0][0])
    unit = numpy.identity(dim, dtype=int)
    for permutation in symmetry:
        perm = unit[:, numpy.argsort(permutation[0])]
        permutation[0] = perm


def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) ->None:
    """Digest the allowed permutations to validate the underlying structure.

    Args:
        mat (numpy.ndarray): Matrix to confirm symmetry in.

        symmetry (List): A list containing all the information regarding             symmetry of the matrix. The first element can be an identity             element with the indices in order, a parity of 1.0 and no complex             conjugation.  Each entry should specify the action of the symmetry             on the indexes, a parity associated with the permutation and             whether the term should be conjugated. The first term should be             the unity operation.
    """
    is_unity = validate_unity(symmetry[0])
    if len(symmetry) == 1 and is_unity:
        return
    build_symmetry_operations(symmetry)
    validate_matrix_symmetry(mat, symmetry)


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def validate_matrix_symmetry(matrix: numpy.ndarray, symmetry: List[Any],
    threshold: float=1e-08) ->None:
    """Go through every element of the matrix and check that the symmetry
    operations are valid up to a threshold.

    Args:
        matrix (numpy.ndarray): A matrix of interest.

        symmetry (List): Symmetry that should be validated.

        threshold (float): The limit at which a symmetry operation is valid.

    Raises:
        ValueError: If there is an error with a symmetry in a permutation.
    """
    all_index = index_queue(len(matrix.shape), matrix.shape[0])
    while all_index:
        index = all_index.popleft()
        value = matrix[index]
        for permu in symmetry[1:]:
            test_index = tuple(numpy.dot(index, permu[0]))
            test_value = matrix[test_index]
            if permu[2]:
                ref_value = permu[1] * numpy.conj(value)
            else:
                ref_value = permu[1] * value
            if numpy.abs(test_value - ref_value) > threshold:
                raise ValueError(
                    'Error with symmetry in permutation {} -> {}. {} != {}'
                    .format(index, test_index, ref_value, test_value))
            try:
                all_index.remove(test_index)
            except ValueError:
                pass


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def build_symmetry_operations(symmetry: List[Any]) ->None:
    """Take the list of allowed permutations and build the symmetry operations
    allowed for the operation, modifying `symmetry` in place.

    Args:
        symmetry (List): A list containing all the allowed permutations.
    """
    dim = len(symmetry[0][0])
    unit = numpy.identity(dim, dtype=int)
    for permutation in symmetry:
        perm = unit[:, numpy.argsort(permutation[0])]
        permutation[0] = perm


def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) ->None:
    """Digest the allowed permutations to validate the underlying structure.

    Args:
        mat (numpy.ndarray): Matrix to confirm symmetry in.

        symmetry (List): A list containing all the information regarding             symmetry of the matrix. The first element can be an identity             element with the indices in order, a parity of 1.0 and no complex             conjugation.  Each entry should specify the action of the symmetry             on the indexes, a parity associated with the permutation and             whether the term should be conjugated. The first term should be             the unity operation.
    """
    is_unity = validate_unity(symmetry[0])
    if len(symmetry) == 1 and is_unity:
        return
    build_symmetry_operations(symmetry)
    validate_matrix_symmetry(mat, symmetry)


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def validate_matrix_symmetry(matrix: numpy.ndarray, symmetry: List[Any],
    threshold: float=1e-08) ->None:
    """Go through every element of the matrix and check that the symmetry
    operations are valid up to a threshold.

    Args:
        matrix (numpy.ndarray): A matrix of interest.

        symmetry (List): Symmetry that should be validated.

        threshold (float): The limit at which a symmetry operation is valid.

    Raises:
        ValueError: If there is an error with a symmetry in a permutation.
    """
    all_index = index_queue(len(matrix.shape), matrix.shape[0])
    while all_index:
        index = all_index.popleft()
        value = matrix[index]
        for permu in symmetry[1:]:
            test_index = tuple(numpy.dot(index, permu[0]))
            test_value = matrix[test_index]
            if permu[2]:
                ref_value = permu[1] * numpy.conj(value)
            else:
                ref_value = permu[1] * value
            if numpy.abs(test_value - ref_value) > threshold:
                raise ValueError(
                    'Error with symmetry in permutation {} -> {}. {} != {}'
                    .format(index, test_index, ref_value, test_value))
            try:
                all_index.remove(test_index)
            except ValueError:
                pass


def validate_unity(unity_permutation: List[Any]) ->bool:
    """Checks that the inumpyut permutation is the unity permutation, i.e., an
    object s of type List[List[int], float, bool] such that

    * s[0][0] > -1,
    * s[0][i] < s[0][j] for i < j,
    * s[1] = 1.0, and
    * s[2] = False.

    Args:
        unity_permutation (list): A permutation object to compare to the unity
            operation.

    Returns:
        (bool): True if the inumpyut permutation is the unity permutation,             else False.

    Raises:
        ValueError: If the inumpyut permutation is invalid.
    """
    if unity_permutation[1] != 1.0:
        raise ValueError('The unity permutation does not have a phase of 1.0')
    if unity_permutation[2]:
        return False
    static = unity_permutation[0]
    lowlimit = -1
    for index in static:
        if index < lowlimit:
            raise ValueError('The first entry is not unity')
        lowlimit = index
    return True

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def build_symmetry_operations(symmetry: List[Any]) ->None:
    """Take the list of allowed permutations and build the symmetry operations
    allowed for the operation, modifying `symmetry` in place.

    Args:
        symmetry (List): A list containing all the allowed permutations.
    """
    dim = len(symmetry[0][0])
    unit = numpy.identity(dim, dtype=int)
    for permutation in symmetry:
        perm = unit[:, numpy.argsort(permutation[0])]
        permutation[0] = perm


def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) ->None:
    """Digest the allowed permutations to validate the underlying structure.

    Args:
        mat (numpy.ndarray): Matrix to confirm symmetry in.

        symmetry (List): A list containing all the information regarding             symmetry of the matrix. The first element can be an identity             element with the indices in order, a parity of 1.0 and no complex             conjugation.  Each entry should specify the action of the symmetry             on the indexes, a parity associated with the permutation and             whether the term should be conjugated. The first term should be             the unity operation.
    """
    is_unity = validate_unity(symmetry[0])
    if len(symmetry) == 1 and is_unity:
        return
    build_symmetry_operations(symmetry)
    validate_matrix_symmetry(mat, symmetry)


def index_queue(dim: int, highest_index: int) ->Deque[Tuple[int, ...]]:
    """Generate all index pointers into the matrix of interest.

    Args:
        dim (int): The size of the matrix of interest.

        highest_index (int): The maximum value allowable in the matrix.

    Returns:
        Deque[Tuple[int, ...]]: A queue containing all possible pointers             into the matrix.
    """
    i_queue: List[Tuple[int, ...]] = []
    if dim == 1:
        for i in range(highest_index):
            i_queue.append(tuple([i]))
    else:
        total = highest_index ** dim - 1
        ticker = [(0) for _ in range(dim)]
        i_queue.append(tuple(ticker))
        for _ in range(total):
            for i in reversed(range(dim)):
                ticker[i] += 1
                if ticker[i] < highest_index:
                    break
                ticker[i] = 0
            i_queue.append(tuple(ticker))
    return deque(i_queue)


def validate_matrix_symmetry(matrix: numpy.ndarray, symmetry: List[Any],
    threshold: float=1e-08) ->None:
    """Go through every element of the matrix and check that the symmetry
    operations are valid up to a threshold.

    Args:
        matrix (numpy.ndarray): A matrix of interest.

        symmetry (List): Symmetry that should be validated.

        threshold (float): The limit at which a symmetry operation is valid.

    Raises:
        ValueError: If there is an error with a symmetry in a permutation.
    """
    all_index = index_queue(len(matrix.shape), matrix.shape[0])
    while all_index:
        index = all_index.popleft()
        value = matrix[index]
        for permu in symmetry[1:]:
            test_index = tuple(numpy.dot(index, permu[0]))
            test_value = matrix[test_index]
            if permu[2]:
                ref_value = permu[1] * numpy.conj(value)
            else:
                ref_value = permu[1] * value
            if numpy.abs(test_value - ref_value) > threshold:
                raise ValueError(
                    'Error with symmetry in permutation {} -> {}. {} != {}'
                    .format(index, test_index, ref_value, test_value))
            try:
                all_index.remove(test_index)
            except ValueError:
                pass


def validate_unity(unity_permutation: List[Any]) ->bool:
    """Checks that the inumpyut permutation is the unity permutation, i.e., an
    object s of type List[List[int], float, bool] such that

    * s[0][0] > -1,
    * s[0][i] < s[0][j] for i < j,
    * s[1] = 1.0, and
    * s[2] = False.

    Args:
        unity_permutation (list): A permutation object to compare to the unity
            operation.

    Returns:
        (bool): True if the inumpyut permutation is the unity permutation,             else False.

    Raises:
        ValueError: If the inumpyut permutation is invalid.
    """
    if unity_permutation[1] != 1.0:
        raise ValueError('The unity permutation does not have a phase of 1.0')
    if unity_permutation[2]:
        return False
    static = unity_permutation[0]
    lowlimit = -1
    for index in static:
        if index < lowlimit:
            raise ValueError('The first entry is not unity')
        lowlimit = index
    return True

<mask token>
from collections import deque
from typing import Any, Deque, List, Tuple
import numpy


def build_symmetry_operations(symmetry: List[Any]) ->None:
    """Take the list of allowed permutations and build the symmetry operations
    allowed for the operation, modifying `symmetry` in place.

    Args:
        symmetry (List): A list containing all the allowed permutations.
    """
    dim = len(symmetry[0][0])
    unit = numpy.identity(dim, dtype=int)
    for permutation in symmetry:
        perm = unit[:, numpy.argsort(permutation[0])]
        permutation[0] = perm


def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) ->None:
    """Digest the allowed permutations to validate the underlying structure.

    Args:
        mat (numpy.ndarray): Matrix to confirm symmetry in.

        symmetry (List): A list containing all the information regarding             symmetry of the matrix. The first element can be an identity             element with the indices in order, a parity of 1.0 and no complex             conjugation.  Each entry should specify the action of the symmetry             on the indexes, a parity associated with the permutation and             whether the term should be conjugated. The first term should be             the unity operation.
    """
    is_unity = validate_unity(symmetry[0])
    if len(symmetry) == 1 and is_unity:
        return
    build_symmetry_operations(symmetry)
    validate_matrix_symmetry(mat, symmetry)


def index_queue(dim: int, highest_index: int) ->Deque[Tuple[int, ...]]:
    """Generate all index pointers into the matrix of interest.

    Args:
        dim (int): The size of the matrix of interest.

        highest_index (int): The maximum value allowable in the matrix.

    Returns:
        Deque[Tuple[int, ...]]: A queue containing all possible pointers             into the matrix.
    """
    i_queue: List[Tuple[int, ...]] = []
    if dim == 1:
        for i in range(highest_index):
            i_queue.append(tuple([i]))
    else:
        total = highest_index ** dim - 1
        ticker = [(0) for _ in range(dim)]
        i_queue.append(tuple(ticker))
        for _ in range(total):
            for i in reversed(range(dim)):
                ticker[i] += 1
                if ticker[i] < highest_index:
                    break
                ticker[i] = 0
            i_queue.append(tuple(ticker))
    return deque(i_queue)


def validate_matrix_symmetry(matrix: numpy.ndarray, symmetry: List[Any],
    threshold: float=1e-08) ->None:
    """Go through every element of the matrix and check that the symmetry
    operations are valid up to a threshold.

    Args:
        matrix (numpy.ndarray): A matrix of interest.

        symmetry (List): Symmetry that should be validated.

        threshold (float): The limit at which a symmetry operation is valid.

    Raises:
        ValueError: If there is an error with a symmetry in a permutation.
    """
    all_index = index_queue(len(matrix.shape), matrix.shape[0])
    while all_index:
        index = all_index.popleft()
        value = matrix[index]
        for permu in symmetry[1:]:
            test_index = tuple(numpy.dot(index, permu[0]))
            test_value = matrix[test_index]
            if permu[2]:
                ref_value = permu[1] * numpy.conj(value)
            else:
                ref_value = permu[1] * value
            if numpy.abs(test_value - ref_value) > threshold:
                raise ValueError(
                    'Error with symmetry in permutation {} -> {}. {} != {}'
                    .format(index, test_index, ref_value, test_value))
            try:
                all_index.remove(test_index)
            except ValueError:
                pass


def validate_unity(unity_permutation: List[Any]) ->bool:
    """Checks that the inumpyut permutation is the unity permutation, i.e., an
    object s of type List[List[int], float, bool] such that

    * s[0][0] > -1,
    * s[0][i] < s[0][j] for i < j,
    * s[1] = 1.0, and
    * s[2] = False.

    Args:
        unity_permutation (list): A permutation object to compare to the unity
            operation.

    Returns:
        (bool): True if the inumpyut permutation is the unity permutation,             else False.

    Raises:
        ValueError: If the inumpyut permutation is invalid.
    """
    if unity_permutation[1] != 1.0:
        raise ValueError('The unity permutation does not have a phase of 1.0')
    if unity_permutation[2]:
        return False
    static = unity_permutation[0]
    lowlimit = -1
    for index in static:
        if index < lowlimit:
            raise ValueError('The first entry is not unity')
        lowlimit = index
    return True

#   Copyright 2020 Google LLC

#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.
"""Tensor utilities to evaluate symmetry and eventually create dense storage."""

from collections import deque
from typing import Any, Deque, List, Tuple

import numpy


def build_symmetry_operations(symmetry: List[Any]) -> None:
    """Take the list of allowed permutations and build the symmetry operations
    allowed for the operation, modifying `symmetry` in place.

    Args:
        symmetry (List): A list containing all the allowed permutations.
    """
    dim = len(symmetry[0][0])
    unit = numpy.identity(dim, dtype=int)
    for permutation in symmetry:
        perm = unit[:, numpy.argsort(permutation[0])]
        permutation[0] = perm


def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) -> None:
    """Digest the allowed permutations to validate the underlying structure.

    Args:
        mat (numpy.ndarray): Matrix to confirm symmetry in.

        symmetry (List): A list containing all the information regarding \
            symmetry of the matrix. The first element can be an identity \
            element with the indices in order, a parity of 1.0 and no complex \
            conjugation.  Each entry should specify the action of the symmetry \
            on the indexes, a parity associated with the permutation and \
            whether the term should be conjugated. The first term should be \
            the unity operation.
    """
    is_unity = validate_unity(symmetry[0])
    if len(symmetry) == 1 and is_unity:
        return
    build_symmetry_operations(symmetry)
    validate_matrix_symmetry(mat, symmetry)


def index_queue(dim: int, highest_index: int) -> Deque[Tuple[int, ...]]:
    """Generate all index pointers into the matrix of interest.

    Args:
        dim (int): The size of the matrix of interest.

        highest_index (int): The maximum value allowable in the matrix.

    Returns:
        Deque[Tuple[int, ...]]: A queue containing all possible pointers \
            into the matrix.
    """
    i_queue: List[Tuple[int, ...]] = []
    if dim == 1:
        for i in range(highest_index):
            i_queue.append(tuple([i]))
    else:
        total = highest_index**dim - 1
        ticker = [0 for _ in range(dim)]
        i_queue.append(tuple(ticker))

        for _ in range(total):
            for i in reversed(range(dim)):
                ticker[i] += 1
                if ticker[i] < highest_index:
                    break
                ticker[i] = 0
            i_queue.append(tuple(ticker))

    return deque(i_queue)


def validate_matrix_symmetry(matrix: numpy.ndarray,
                             symmetry: List[Any],
                             threshold: float = 1.0e-8) -> None:
    """Go through every element of the matrix and check that the symmetry
    operations are valid up to a threshold.

    Args:
        matrix (numpy.ndarray): A matrix of interest.

        symmetry (List): Symmetry that should be validated.

        threshold (float): The limit at which a symmetry operation is valid.

    Raises:
        ValueError: If there is an error with a symmetry in a permutation.
    """
    all_index = index_queue(len(matrix.shape), matrix.shape[0])
    while all_index:
        index = all_index.popleft()
        value = matrix[index]
        for permu in symmetry[1:]:
            test_index = tuple(numpy.dot(index, permu[0]))
            test_value = matrix[test_index]

            if permu[2]:
                ref_value = permu[1] * numpy.conj(value)
            else:
                ref_value = permu[1] * value

            if numpy.abs(test_value - ref_value) > threshold:
                raise ValueError("Error with symmetry in permutation {} -> {}."
                                 " {} != {}".format(index, test_index,
                                                    ref_value, test_value))

            try:
                all_index.remove(test_index)
            except ValueError:
                pass


def validate_unity(unity_permutation: List[Any]) -> bool:
    """Checks that the inumpyut permutation is the unity permutation, i.e., an
    object s of type List[List[int], float, bool] such that

    * s[0][0] > -1,
    * s[0][i] < s[0][j] for i < j,
    * s[1] = 1.0, and
    * s[2] = False.

    Args:
        unity_permutation (list): A permutation object to compare to the unity
            operation.

    Returns:
        (bool): True if the inumpyut permutation is the unity permutation, \
            else False.

    Raises:
        ValueError: If the inumpyut permutation is invalid.
    """
    if unity_permutation[1] != 1.0:
        raise ValueError("The unity permutation does not have a phase of 1.0")

    if unity_permutation[2]:
        return False

    static = unity_permutation[0]
    lowlimit = -1

    for index in static:
        if index < lowlimit:
            raise ValueError("The first entry is not unity")
        lowlimit = index

    return True
