)

cc_library(
    name = "host_context_util",
    srcs = ["utils/host_context.cc"],
    hdrs = ["utils/host_context.h"],
    deps = [
        "//tensorflow/core/platform:logging",
        "@com_google_absl//absl/base:core_headers",
        "@tf_runtime//:hostcontext",
    ],
)

cc_library(
    name = "function",
    srcs = [
        "function/function.cc",
    ],
    hdrs = [

    licenses = ["notice"],
)

td_library(
    name = "lift_as_function_call_td_files",
    srcs = [
        "lift_as_function_call.td",
    ],
    compatible_with = get_compatible_with_portable(),
    deps = [
        "@llvm-project//mlir:FuncTdFiles",
    ],
)

cc_library(
    name = "lift_as_function_call",
    srcs = ["lift_as_function_call.cc"],
    hdrs = ["lift_as_function_call.h"],

cc_library(
    name = "posix_filesystem_helper",
    srcs = ["posix_filesystem_helper.cc"],
    hdrs = ["posix_filesystem_helper.h"],
    deps = [":copy_file"],
)

# On Linux, we can copy files faster using `sendfile`. But not elsewhere.
# Hence, this private library to select which implementation to use.
cc_library(
    name = "copy_file",
    srcs = select({
        "//tensorflow:linux_x86_64": ["copy_file_linux.cc"],

)

cc_library(
    name = "array_grad",
    srcs = ["array_grad.cc"],
    hdrs = [
        "array_grad.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        "//tensorflow/c/eager:abstract_context",
        "//tensorflow/c/eager:gradients_internal",
    ],
)

cc_library(
    name = "math_grad",
    srcs = ["math_grad.cc"],
    hdrs = [
        "math_grad.h",

    ],
    cmd = "$(location gen_quantized_function_library) --output_file $(RULEDIR)/passes/quantized_function_library.h --src '$(SRCS)'",
    compatible_with = get_compatible_with_portable(),
    tools = ["gen_quantized_function_library"],
)

cc_library(
    name = "manipulate_model_attr",
    srcs = [
        "passes/manipulate_model_attr.cc",
    ],
    hdrs = [
        "passes/manipulate_model_attr.h",
    ],

    default_visibility = [
        ":internal_visibility_allowlist_package",
        "//tensorflow:__pkg__",
    ],
    licenses = ["notice"],
)

pytype_strict_library(
    name = "quantization",
    srcs = ["quantization.py"],
    visibility = ["//visibility:public"],
    deps = [
        ":pywrap_quantization",
        "//tensorflow/compiler/mlir/quantization/stablehlo:quantization_config_proto_py",

    ],
)

cc_library(
    name = "gcs_helper",
    srcs = ["gcs_helper.cc"],
    hdrs = ["gcs_helper.h"],
    linkstatic = 1,
    deps = [
        "//tensorflow/c:env",
    ],
)

cc_library(
    name = "cleanup",
    hdrs = ["cleanup.h"],
)

cc_library(
    name = "ram_file_block_cache",
    srcs = ["ram_file_block_cache.cc"],
    hdrs = ["ram_file_block_cache.h"],
    deps = [

    srcs = ["import_restore_v1.py"],
    deps = [
        ":common_v1",
        "//tensorflow:tensorflow_py",
    ],
)

py_strict_binary(
    name = "shapes_for_arguments",
    srcs = ["shapes_for_arguments.py"],
    deps = [
        ":common",
        "//tensorflow:tensorflow_py",
    ],
)

py_strict_binary(
    name = "control_flow_upgrade_legacy_v1",

    ],
)

cc_library(
    name = "size_utils",
    srcs = [
        "utils/size_utils.cc",
    ],
    hdrs = [
        "utils/size_utils.h",
    ],
    deps = [
        "@llvm-project//mlir:IR",
    ],
)

tf_cc_test(
    name = "size_utils_test",
    size = "small",
    srcs = ["utils/size_utils_test.cc"],
    deps = [
        ":size_utils",

				drawPaletted(dst0, r, src, sp, false)
				return
			}
		}
	case *image.NRGBA:
		if op == Src && mask == nil {
			if src0, ok := src.(*image.NRGBA); ok {
				d0 := dst0.PixOffset(r.Min.X, r.Min.Y)
				s0 := src0.PixOffset(sp.X, sp.Y)
				drawCopySrc(
					dst0.Pix[d0:], dst0.Stride, r, src0.Pix[s0:], src0.Stride, sp, 4*r.Dx())
				return
			}
		}
	case *image.NRGBA64: