import types
from enum import Enum
from typing import Any, Callable, Dict, Set, Type, TypeVar, Union

from pydantic import BaseModel

DecoratedCallable = TypeVar("DecoratedCallable", bound=Callable[..., Any])
UnionType = getattr(types, "UnionType", Union)
ModelNameMap = Dict[Union[Type[BaseModel], Type[Enum]], str]

    .SetIsStateful()
    .SetShapeFn([](shape_inference::InferenceContext* c) {
      c->set_output(0, c->Scalar());
      DataType t;
      TF_RETURN_IF_ERROR(c->GetAttr("dtype", &t));
      PartialTensorShape p;
      TF_RETURN_IF_ERROR(c->GetAttr("shape", &p));
      shape_inference::ShapeHandle s;
      TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(p, &s));
      c->set_output_handle_shapes_and_types(

from typing import Any, List, Union

import peewee
from pydantic import BaseModel
from pydantic.utils import GetterDict


class PeeweeGetterDict(GetterDict):
    def get(self, key: Any, default: Any = None):
        res = getattr(self._obj, key, default)
        if isinstance(res, peewee.ModelSelect):
            return list(res)
        return res


class ItemBase(BaseModel):
    title: str
    description: Union[str, None] = None

from starlette.status import HTTP_422_UNPROCESSABLE_ENTITY, WS_1008_POLICY_VIOLATION


async def http_exception_handler(request: Request, exc: HTTPException) -> Response:
    headers = getattr(exc, "headers", None)
    if not is_body_allowed_for_status_code(exc.status_code):
        return Response(status_code=exc.status_code, headers=headers)
    return JSONResponse(

      return;
    }

    const auto [min_value, max_value] = *min_max_values;
    mlir::OpBuilder builder(aggregator_op);
    aggregator_op->setAttr("min", builder.getF32FloatAttr(min_value));
    aggregator_op->setAttr("max", builder.getF32FloatAttr(max_value));
  });
  return status;
}

bool IsCalibrationRequired(mlir::ModuleOp module_op) {
  bool calibration_required = false;

    input_ = tf.raw_ops.Concat(
        concat_dim=i, values=[left_padding, input_, right_padding])
  return input_


@tf.RegisterGradient('NewMirrorPad')
def _mirror_pad_grad(op, grad):
  mode = op.get_attr('mode')
  return [gen_array_ops.mirror_pad_grad(grad, op.inputs[1], mode=mode), None]


@Composite(
    'NewMirrorPadGrad',
    inputs=['input_: T', 'paddings: Tpaddings'],

  auto spec = std::make_unique<OpQuantSpec>();
  if (auto call_op = dyn_cast<TF::PartitionedCallOp>(op)) {
    StringRef function_name =
        mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue();
    if (!function_name.starts_with("composite_")) {
      return spec;
    }
    if (function_name.contains("depthwise_conv2d")) {
      spec->coeff_op_quant_dim[1] = 3;

namespace mlir {
namespace TFL {
namespace tac {

// Cost attribute string on the TFL dialect.
constexpr char kCost[] = "tac.cost";

inline void UpdateCost(Operation* op, float cost, OpBuilder* builder) {
  op->setAttr(kCost, builder->getF32FloatAttr(cost));
}

// Get the cost annotated with kCost.
inline bool GetCostOnOp(Operation* op, float* cost) {
  auto cost_type = op->getAttrOfType<FloatAttr>(kCost);
  if (cost_type == nullptr) {

  auto func = builder.create<func::FuncOp>(module->getLoc(),
                                           kSessionInitFuncName, func_type);
  func->setAttr(kTfSavedModelExportedNamesAttr,
                builder.getStrArrayAttr({kSessionInitFuncName}));
  func->setAttr(kTfSavedModelInitializerTypeAttr,
                builder.getStringAttr(kTfSavedModelInitializerRestoreType));
  func.setVisibility(mlir::func::FuncOp::Visibility::Public);

    const std::string new_inputs_attr_str =
        llvm::join(std::move(inputs_attrs), /*Separator=*/",");

    named_attr.setValue(StringAttr::get(ctx, new_inputs_attr_str));
  }

  func_op->setAttr(kTfEntryFunctionAttr,
                   DictionaryAttr::get(ctx, entry_func_attrs));
}
}  // namespace quant