# Path: opt_einsum/helpers.py
# @overload
# def compute_size_by_dict(indices: Iterable[int], idx_dict: List[int]) -> int:
#     ...
# 
# def flop_count(
#     idx_contraction: Collection[str],
#     inner: bool,
#     num_terms: int,
#     size_dictionary: Dict[str, int],
# ) -> int:
#     """
#     Computes the number of FLOPS in the contraction.
# 
#     Parameters
#     ----------
#     idx_contraction : iterable
#         The indices involved in the contraction
#     inner : bool
#         Does this contraction require an inner product?
#     num_terms : int
#         The number of terms in a contraction
#     size_dictionary : dict
#         The size of each of the indices in idx_contraction
# 
#     Returns
#     -------
#     flop_count : int
#         The total number of FLOPS required for the contraction.
# 
#     Examples
#     --------
# 
#     >>> flop_count('abc', False, 1, {'a': 2, 'b':3, 'c':5})
#     30
# 
#     >>> flop_count('abc', True, 2, {'a': 2, 'b':3, 'c':5})
#     60
# 
#     """
# 
#     overall_size = compute_size_by_dict(idx_contraction, size_dictionary)
#     op_factor = max(1, num_terms - 1)
#     if inner:
#         op_factor += 1
# 
#     return overall_size * op_factor
# 
# Path: opt_einsum/typing.py



import functools
import heapq
import itertools
import operator
import random
import re
import numpy as np
from collections import Counter, OrderedDict, defaultdict
from typing import Any, Callable
from typing import Counter as CounterType
from typing import Dict, FrozenSet, Generator, List, Optional, Sequence, Set, Tuple, Union
from .helpers import compute_size_by_dict, flop_count
from .typing import ArrayIndexType, PathType
    from .path_random import random_greedy_128

    **Returns:**

    - **k12** - *(frozenset)* The resulting indices of the potential tensor.
    - **cost** - *(int)* Estimated flop count of operation.
    """
    k1, k2 = inputs[i], inputs[j]
    either = k1 | k2
    shared = k1 & k2
    keep = frozenset.union(output, *map(inputs.__getitem__, remaining - {i, j}))

    k12 = either & keep
    cost = flop_count(either, bool(shared - keep), 2, size_dict)

    return k12, cost


def _compute_oversize_flops(
    inputs: Tuple[FrozenSet[str]],
    remaining: List[ArrayIndexType],
    output: ArrayIndexType,
    size_dict: Dict[str, int],
) -> int:
    """
    Compute the flop count for a contraction of all remaining arguments. This
    is used when a memory limit means that no pairwise contractions can be made.
    """
    idx_contraction = frozenset.union(*map(inputs.__getitem__, remaining))  # type: ignore
    inner = idx_contraction - output
    num_terms = len(remaining)

# Path: opt_einsum/helpers.py
# @overload
# def compute_size_by_dict(indices: Iterable[int], idx_dict: List[int]) -> int:
#     ...
# 
# def flop_count(
#     idx_contraction: Collection[str],
#     inner: bool,
#     num_terms: int,
#     size_dictionary: Dict[str, int],
# ) -> int:
#     """
#     Computes the number of FLOPS in the contraction.
# 
#     Parameters
#     ----------
#     idx_contraction : iterable
#         The indices involved in the contraction
#     inner : bool
#         Does this contraction require an inner product?
#     num_terms : int
#         The number of terms in a contraction
#     size_dictionary : dict
#         The size of each of the indices in idx_contraction
# 
#     Returns
#     -------
#     flop_count : int
#         The total number of FLOPS required for the contraction.
# 
#     Examples
#     --------
# 
#     >>> flop_count('abc', False, 1, {'a': 2, 'b':3, 'c':5})
#     30
# 
#     >>> flop_count('abc', True, 2, {'a': 2, 'b':3, 'c':5})
#     60
# 
#     """
# 
#     overall_size = compute_size_by_dict(idx_contraction, size_dictionary)
#     op_factor = max(1, num_terms - 1)
#     if inner:
#         op_factor += 1
# 
#     return overall_size * op_factor
# 
# Path: opt_einsum/typing.py



# Path: opt_einsum/paths.py
import functools
import heapq
import itertools
import operator
import random
import re
import numpy as np
from collections import Counter, OrderedDict, defaultdict
from typing import Any, Callable
from typing import Counter as CounterType
from typing import Dict, FrozenSet, Generator, List, Optional, Sequence, Set, Tuple, Union
from .helpers import compute_size_by_dict, flop_count
from .typing import ArrayIndexType, PathType
    from .path_random import random_greedy_128


    **Returns:**

    - **k12** - *(frozenset)* The resulting indices of the potential tensor.
    - **cost** - *(int)* Estimated flop count of operation.
    """
    k1, k2 = inputs[i], inputs[j]
    either = k1 | k2
    shared = k1 & k2
    keep = frozenset.union(output, *map(inputs.__getitem__, remaining - {i, j}))

    k12 = either & keep
    cost = flop_count(either, bool(shared - keep), 2, size_dict)

    return k12, cost


def _compute_oversize_flops(
    inputs: Tuple[FrozenSet[str]],
    remaining: List[ArrayIndexType],
    output: ArrayIndexType,
    size_dict: Dict[str, int],
) -> int:
    """
    Compute the flop count for a contraction of all remaining arguments. This
    is used when a memory limit means that no pairwise contractions can be made.
    """
    idx_contraction = frozenset.union(*map(inputs.__getitem__, remaining))  # type: ignore
    inner = idx_contraction - output
    num_terms = len(remaining)

    return flop_count(idx_contraction, bool(inner), num_terms, size_dict)