Build uploaded using `kernels`.

build/torch210-cxx11-cu126-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import w8_a16_gemm, w8_a16_gemm_, preprocess_weights, quant_weights
-
-__all__ = ["w8_a16_gemm", "w8_a16_gemm_", "preprocess_weights", "quant_weights"]

build/torch210-cxx11-cu126-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_eetq_2019ec2
-ops = torch.ops._quantization_eetq_2019ec2
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_eetq_2019ec2::{op_name}"

build/torch210-cxx11-cu126-x86_64-linux/_quantization_eetq_2019ec2.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7ddd95d6bd5fa005afb8ca1cca98f8e666888437a7b21faa044d7e3180aa0902
-size 39060392

build/torch210-cxx11-cu126-x86_64-linux/custom_ops.py

@@ -1,36 +0,0 @@
-from typing import List
-import torch
-
-from ._ops import ops
-
-
-def w8_a16_gemm(
-    input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor
-) -> torch.Tensor:
-    return ops.w8_a16_gemm(input, weight, scale)
-
-
-def w8_a16_gemm_(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    output: torch.Tensor,
-    m: int,
-    n: int,
-    k: int,
-) -> torch.Tensor:
-    return ops.w8_a16_gemm_(input, weight, scale, output, m, n, k)
-
-
-def preprocess_weights(origin_weight: torch.Tensor, is_int4: bool) -> torch.Tensor:
-    return ops.preprocess_weights(origin_weight, is_int4)
-
-
-def quant_weights(
-    origin_weight: torch.Tensor,
-    quant_type: torch.dtype,
-    return_unprocessed_quantized_tensor: bool,
-) -> List[torch.Tensor]:
-    return ops.quant_weights(
-        origin_weight, quant_type, return_unprocessed_quantized_tensor
-    )

build/torch210-cxx11-cu126-x86_64-linux/metadata.json

@@ -1 +0,0 @@
-{"python-depends":[]}

build/torch210-cxx11-cu126-x86_64-linux/quantization_eetq/__init__.py

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-
-import importlib
-from pathlib import Path
-from types import ModuleType
-
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-
-
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch210-cxx11-cu128-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import w8_a16_gemm, w8_a16_gemm_, preprocess_weights, quant_weights
-
-__all__ = ["w8_a16_gemm", "w8_a16_gemm_", "preprocess_weights", "quant_weights"]

build/torch210-cxx11-cu128-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_eetq_2019ec2
-ops = torch.ops._quantization_eetq_2019ec2
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_eetq_2019ec2::{op_name}"

build/torch210-cxx11-cu128-x86_64-linux/_quantization_eetq_2019ec2.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6026ada25ff3b5a4eb532b0e8a6825ef353d4c507f36817020557c68b22db2f8
-size 45393976

build/torch210-cxx11-cu128-x86_64-linux/custom_ops.py

@@ -1,36 +0,0 @@
-from typing import List
-import torch
-
-from ._ops import ops
-
-
-def w8_a16_gemm(
-    input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor
-) -> torch.Tensor:
-    return ops.w8_a16_gemm(input, weight, scale)
-
-
-def w8_a16_gemm_(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    output: torch.Tensor,
-    m: int,
-    n: int,
-    k: int,
-) -> torch.Tensor:
-    return ops.w8_a16_gemm_(input, weight, scale, output, m, n, k)
-
-
-def preprocess_weights(origin_weight: torch.Tensor, is_int4: bool) -> torch.Tensor:
-    return ops.preprocess_weights(origin_weight, is_int4)
-
-
-def quant_weights(
-    origin_weight: torch.Tensor,
-    quant_type: torch.dtype,
-    return_unprocessed_quantized_tensor: bool,
-) -> List[torch.Tensor]:
-    return ops.quant_weights(
-        origin_weight, quant_type, return_unprocessed_quantized_tensor
-    )

build/torch210-cxx11-cu128-x86_64-linux/metadata.json

@@ -1 +0,0 @@
-{"python-depends":[]}

build/torch210-cxx11-cu128-x86_64-linux/quantization_eetq/__init__.py

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-
-import importlib
-from pathlib import Path
-from types import ModuleType
-
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-
-
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch28-cxx11-cu126-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import w8_a16_gemm, w8_a16_gemm_, preprocess_weights, quant_weights
-
-__all__ = ["w8_a16_gemm", "w8_a16_gemm_", "preprocess_weights", "quant_weights"]

build/torch28-cxx11-cu126-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_eetq_2019ec2
-ops = torch.ops._quantization_eetq_2019ec2
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_eetq_2019ec2::{op_name}"

build/torch28-cxx11-cu126-x86_64-linux/_quantization_eetq_2019ec2.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a97b47d38cf622b58973de106d9b36fa9f41400c0de74a98c67b39d916d10410
-size 39045544

build/torch28-cxx11-cu126-x86_64-linux/custom_ops.py

@@ -1,36 +0,0 @@
-from typing import List
-import torch
-
-from ._ops import ops
-
-
-def w8_a16_gemm(
-    input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor
-) -> torch.Tensor:
-    return ops.w8_a16_gemm(input, weight, scale)
-
-
-def w8_a16_gemm_(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    output: torch.Tensor,
-    m: int,
-    n: int,
-    k: int,
-) -> torch.Tensor:
-    return ops.w8_a16_gemm_(input, weight, scale, output, m, n, k)
-
-
-def preprocess_weights(origin_weight: torch.Tensor, is_int4: bool) -> torch.Tensor:
-    return ops.preprocess_weights(origin_weight, is_int4)
-
-
-def quant_weights(
-    origin_weight: torch.Tensor,
-    quant_type: torch.dtype,
-    return_unprocessed_quantized_tensor: bool,
-) -> List[torch.Tensor]:
-    return ops.quant_weights(
-        origin_weight, quant_type, return_unprocessed_quantized_tensor
-    )

build/torch28-cxx11-cu126-x86_64-linux/metadata.json

@@ -1 +0,0 @@
-{"python-depends":[]}

build/torch28-cxx11-cu126-x86_64-linux/quantization_eetq/__init__.py

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-
-import importlib
-from pathlib import Path
-from types import ModuleType
-
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-
-
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch28-cxx11-cu128-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import w8_a16_gemm, w8_a16_gemm_, preprocess_weights, quant_weights
-
-__all__ = ["w8_a16_gemm", "w8_a16_gemm_", "preprocess_weights", "quant_weights"]

build/torch28-cxx11-cu128-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_eetq_2019ec2
-ops = torch.ops._quantization_eetq_2019ec2
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_eetq_2019ec2::{op_name}"

build/torch28-cxx11-cu128-x86_64-linux/_quantization_eetq_2019ec2.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fdfe703aa8a570bb3589ec89aeca00af0a983b968700933127f2762f50c47686
-size 45378576

build/torch28-cxx11-cu128-x86_64-linux/custom_ops.py

@@ -1,36 +0,0 @@
-from typing import List
-import torch
-
-from ._ops import ops
-
-
-def w8_a16_gemm(
-    input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor
-) -> torch.Tensor:
-    return ops.w8_a16_gemm(input, weight, scale)
-
-
-def w8_a16_gemm_(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    output: torch.Tensor,
-    m: int,
-    n: int,
-    k: int,
-) -> torch.Tensor:
-    return ops.w8_a16_gemm_(input, weight, scale, output, m, n, k)
-
-
-def preprocess_weights(origin_weight: torch.Tensor, is_int4: bool) -> torch.Tensor:
-    return ops.preprocess_weights(origin_weight, is_int4)
-
-
-def quant_weights(
-    origin_weight: torch.Tensor,
-    quant_type: torch.dtype,
-    return_unprocessed_quantized_tensor: bool,
-) -> List[torch.Tensor]:
-    return ops.quant_weights(
-        origin_weight, quant_type, return_unprocessed_quantized_tensor
-    )

build/torch28-cxx11-cu128-x86_64-linux/metadata.json

@@ -1 +0,0 @@
-{"python-depends":[]}

build/torch28-cxx11-cu128-x86_64-linux/quantization_eetq/__init__.py

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-
-import importlib
-from pathlib import Path
-from types import ModuleType
-
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-
-
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch28-cxx11-cu129-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import w8_a16_gemm, w8_a16_gemm_, preprocess_weights, quant_weights
-
-__all__ = ["w8_a16_gemm", "w8_a16_gemm_", "preprocess_weights", "quant_weights"]

build/torch28-cxx11-cu129-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_eetq_2019ec2
-ops = torch.ops._quantization_eetq_2019ec2
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_eetq_2019ec2::{op_name}"

build/torch28-cxx11-cu129-x86_64-linux/_quantization_eetq_2019ec2.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ea1e527ad52b776e108df3ad077a84ec8db233a67630fa8389fa03a641b4a8c8
-size 46492944

build/torch28-cxx11-cu129-x86_64-linux/custom_ops.py

@@ -1,36 +0,0 @@
-from typing import List
-import torch
-
-from ._ops import ops
-
-
-def w8_a16_gemm(
-    input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor
-) -> torch.Tensor:
-    return ops.w8_a16_gemm(input, weight, scale)
-
-
-def w8_a16_gemm_(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    output: torch.Tensor,
-    m: int,
-    n: int,
-    k: int,
-) -> torch.Tensor:
-    return ops.w8_a16_gemm_(input, weight, scale, output, m, n, k)
-
-
-def preprocess_weights(origin_weight: torch.Tensor, is_int4: bool) -> torch.Tensor:
-    return ops.preprocess_weights(origin_weight, is_int4)
-
-
-def quant_weights(
-    origin_weight: torch.Tensor,
-    quant_type: torch.dtype,
-    return_unprocessed_quantized_tensor: bool,
-) -> List[torch.Tensor]:
-    return ops.quant_weights(
-        origin_weight, quant_type, return_unprocessed_quantized_tensor
-    )

build/torch28-cxx11-cu129-x86_64-linux/metadata.json

@@ -1 +0,0 @@
-{"python-depends":[]}

build/torch28-cxx11-cu129-x86_64-linux/quantization_eetq/__init__.py

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-
-import importlib
-from pathlib import Path
-from types import ModuleType
-
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-
-
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch29-cxx11-cu126-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import w8_a16_gemm, w8_a16_gemm_, preprocess_weights, quant_weights
-
-__all__ = ["w8_a16_gemm", "w8_a16_gemm_", "preprocess_weights", "quant_weights"]

build/torch29-cxx11-cu126-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_eetq_2019ec2
-ops = torch.ops._quantization_eetq_2019ec2
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_eetq_2019ec2::{op_name}"

build/torch29-cxx11-cu126-x86_64-linux/_quantization_eetq_2019ec2.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:232a741853f62f20c6d4e418644918b2f8c2b11c8ee97bf396d0c9e382656edf
-size 39049616

build/torch29-cxx11-cu126-x86_64-linux/custom_ops.py

@@ -1,36 +0,0 @@
-from typing import List
-import torch
-
-from ._ops import ops
-
-
-def w8_a16_gemm(
-    input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor
-) -> torch.Tensor:
-    return ops.w8_a16_gemm(input, weight, scale)
-
-
-def w8_a16_gemm_(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    output: torch.Tensor,
-    m: int,
-    n: int,
-    k: int,
-) -> torch.Tensor:
-    return ops.w8_a16_gemm_(input, weight, scale, output, m, n, k)
-
-
-def preprocess_weights(origin_weight: torch.Tensor, is_int4: bool) -> torch.Tensor:
-    return ops.preprocess_weights(origin_weight, is_int4)
-
-
-def quant_weights(
-    origin_weight: torch.Tensor,
-    quant_type: torch.dtype,
-    return_unprocessed_quantized_tensor: bool,
-) -> List[torch.Tensor]:
-    return ops.quant_weights(
-        origin_weight, quant_type, return_unprocessed_quantized_tensor
-    )

build/torch29-cxx11-cu126-x86_64-linux/metadata.json

@@ -1 +0,0 @@
-{"python-depends":[]}

build/torch29-cxx11-cu126-x86_64-linux/quantization_eetq/__init__.py

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-
-import importlib
-from pathlib import Path
-from types import ModuleType
-
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-
-
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch29-cxx11-cu128-x86_64-linux/__init__.py

@@ -1,3 +0,0 @@
-from .custom_ops import w8_a16_gemm, w8_a16_gemm_, preprocess_weights, quant_weights
-
-__all__ = ["w8_a16_gemm", "w8_a16_gemm_", "preprocess_weights", "quant_weights"]

build/torch29-cxx11-cu128-x86_64-linux/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _quantization_eetq_2019ec2
-ops = torch.ops._quantization_eetq_2019ec2
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_quantization_eetq_2019ec2::{op_name}"

build/torch29-cxx11-cu128-x86_64-linux/_quantization_eetq_2019ec2.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:46ea0f0690cda2b0143842ccfdf0f3beff510ce8666c7c3b802856a4d28197cf
-size 45378736

build/torch29-cxx11-cu128-x86_64-linux/custom_ops.py

@@ -1,36 +0,0 @@
-from typing import List
-import torch
-
-from ._ops import ops
-
-
-def w8_a16_gemm(
-    input: torch.Tensor, weight: torch.Tensor, scale: torch.Tensor
-) -> torch.Tensor:
-    return ops.w8_a16_gemm(input, weight, scale)
-
-
-def w8_a16_gemm_(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    scale: torch.Tensor,
-    output: torch.Tensor,
-    m: int,
-    n: int,
-    k: int,
-) -> torch.Tensor:
-    return ops.w8_a16_gemm_(input, weight, scale, output, m, n, k)
-
-
-def preprocess_weights(origin_weight: torch.Tensor, is_int4: bool) -> torch.Tensor:
-    return ops.preprocess_weights(origin_weight, is_int4)
-
-
-def quant_weights(
-    origin_weight: torch.Tensor,
-    quant_type: torch.dtype,
-    return_unprocessed_quantized_tensor: bool,
-) -> List[torch.Tensor]:
-    return ops.quant_weights(
-        origin_weight, quant_type, return_unprocessed_quantized_tensor
-    )

build/torch29-cxx11-cu128-x86_64-linux/metadata.json

@@ -1 +0,0 @@
-{"python-depends":[]}

build/torch29-cxx11-cu128-x86_64-linux/quantization_eetq/__init__.py

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-
-import importlib
-from pathlib import Path
-from types import ModuleType
-
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-
-
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))