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# Source code for torch_tensorrt.ts._compiler

    from typing import List, Dict, Any
    import torch
    from torch import nn

    import torch_tensorrt._C.ts as _C
    from torch_tensorrt import _enums
    from torch_tensorrt.ts._compile_spec import _parse_compile_spec, _parse_device
    from torch_tensorrt._Device import Device
    from types import FunctionType

    def compile(
        module: torch.jit.ScriptModule,
        inputs=[],
        input_signature=None,
        device=Device._current_device(),
        disable_tf32=False,
        sparse_weights=False,
        enabled_precisions=set(),
        refit=False,
        debug=False,
        capability=_enums.EngineCapability.default,
        num_avg_timing_iters=1,
        workspace_size=0,
        dla_sram_size=1048576,
        dla_local_dram_size=1073741824,
        dla_global_dram_size=536870912,
        calibrator=None,
        truncate_long_and_double=False,
        require_full_compilation=False,
        min_block_size=3,
        torch_executed_ops=[],
        torch_executed_modules=[],
    ) -> torch.jit.ScriptModule:
        """Compile a TorchScript module for NVIDIA GPUs using TensorRT

        Takes a existing TorchScript module and a set of settings to configure the compiler
        and will convert methods to JIT Graphs which call equivalent TensorRT engines

        Converts specifically the forward method of a TorchScript Module

        Arguments:
            module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch
                ``torch.nn.Module``

        Keyword Arguments:
            inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
                torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
                to select device type. ::

                    input=[
                        torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
                        torch_tensorrt.Input(
                            min_shape=(1, 224, 224, 3),
                            opt_shape=(1, 512, 512, 3),
                            max_shape=(1, 1024, 1024, 3),
                            dtype=torch.int32
                            format=torch.channel_last
                        ), # Dynamic input shape for input #2
                        torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
                    ]

            input_signature Union(List, Tuple, torch_tensorrt.Input, torch.Tensor): A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
                torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. **This API should be considered beta-level stable and may change in the future** ::

                    input_signature=([
                        torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
                        torch_tensorrt.Input(
                            min_shape=(1, 224, 224, 3),
                            opt_shape=(1, 512, 512, 3),
                            max_shape=(1, 1024, 1024, 3),
                            dtype=torch.int32
                            format=torch.channel_last
                        ), # Dynamic input shape for input #2
                    ], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3
            device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::

                device=torch_tensorrt.Device("dla:1", allow_gpu_fallback=True)

            disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas
            sparse_weights (bool): Enable sparsity for convolution and fully connected layers.
            enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels
            refit (bool): Enable refitting
            debug (bool): Enable debuggable engine
            capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels
            num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels
            workspace_size (int): Maximum size of workspace given to TensorRT
            dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.
            dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations
            dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution
            truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32
            calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration
            require_full_compilation (bool): Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch
            min_block_size (int): The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT
            torch_executed_ops (List[str]): List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True
            torch_executed_modules (List[str]): List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True

        Returns:
            torch.jit.ScriptModule: Compiled TorchScript Module, when run it will execute via TensorRT
        """

        if isinstance(module, torch.jit.ScriptFunction):
            raise TypeError(
                "torch.jit.ScriptFunction currently is not directly supported, wrap the function in a module to compile"
            )

        if require_full_compilation and (
            len(torch_executed_modules) > 0 or len(torch_executed_ops) > 0
        ):
            raise ValueError(
                f"require_full_compilation is enabled however the list of modules and ops to run in torch is not empty. Found: torch_executed_ops: {torch_executed_ops}, torch_executed_modules: {torch_executed_modules}"
            )

        spec = {
            "inputs": inputs,
            "input_signature": input_signature,
            "device": device,
            "disable_tf32": disable_tf32,  # Force FP32 layers to use traditional as FP32 format
            "sparse_weights": sparse_weights,  # Enable sparsity for convolution and fully connected layers.
            "enabled_precisions": enabled_precisions,  # Enabling FP16 kernels
            "refit": refit,  # enable refit
            "debug": debug,  # enable debuggable engine
            "capability": capability,  # Restrict kernel selection to safe gpu kernels or safe dla kernels
            "num_avg_timing_iters": num_avg_timing_iters,  # Number of averaging timing iterations used to select kernels
            "workspace_size": workspace_size,  # Maximum size of workspace given to TensorRT
            "calibrator": calibrator,
            "truncate_long_and_double": truncate_long_and_double,
            "torch_fallback": {
                "enabled": not require_full_compilation,
                "forced_fallback_ops": torch_executed_ops,
                "forced_fallback_modules": torch_executed_modules,
                "min_block_size": min_block_size,
            },
        }

        compiled_cpp_mod = _C.compile_graph(module._c, _parse_compile_spec(spec))
        compiled_module = torch.jit._recursive.wrap_cpp_module(compiled_cpp_mod)
        return compiled_module

    def convert_method_to_trt_engine(
        module: torch.jit.ScriptModule,
        method_name: str,
        inputs=[],
        device=Device._current_device(),
        disable_tf32=False,
        sparse_weights=False,
        enabled_precisions=set(),
        refit=False,
        debug=False,
        capability=_enums.EngineCapability.default,
        num_avg_timing_iters=1,
        workspace_size=0,
        dla_sram_size=1048576,
        dla_local_dram_size=1073741824,
        dla_global_dram_size=536870912,
        truncate_long_and_double=False,
        calibrator=None,
    ) -> str:
        """Convert a TorchScript module method to a serialized TensorRT engine

        Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings

        Arguments:
            module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch
                ``torch.nn.Module``
            method_name (str): Name of method to convert

        Keyword Args:
            inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
                torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
                to select device type. ::

                    input=[
                        torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
                        torch_tensorrt.Input(
                            min_shape=(1, 224, 224, 3),
                            opt_shape=(1, 512, 512, 3),
                            max_shape=(1, 1024, 1024, 3),
                            dtype=torch.int32
                            format=torch.channel_last
                        ), # Dynamic input shape for input #2
                        torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
                    ]

            input_signature Union(List, Tuple, torch_tensorrt.Input, torch.Tensor): A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
                torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. **This API should be considered beta-level stable and may change in the future** ::

                    input_signature=([
                        torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
                        torch_tensorrt.Input(
                            min_shape=(1, 224, 224, 3),
                            opt_shape=(1, 512, 512, 3),
                            max_shape=(1, 1024, 1024, 3),
                            dtype=torch.int32
                            format=torch.channel_last
                        ), # Dynamic input shape for input #2
                    ], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3

            device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::

                device=torch_tensorrt.Device("dla:1", allow_gpu_fallback=True)

            disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas
            sparse_weights (bool): Enable sparsity for convolution and fully connected layers.
            enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels
            refit (bool): Enable refitting
            debug (bool): Enable debuggable engine
            capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels
            num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels
            workspace_size (int): Maximum size of workspace given to TensorRT
            dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.
            dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations
            dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution
            truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32
            calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration

        Returns:
            bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs
        """
        if isinstance(module, torch.jit.ScriptFunction):
            raise TypeError(
                "torch.jit.ScriptFunctions currently are not directly supported, wrap the function in a module to compile"
            )

        compile_spec = {
            "inputs": inputs,
            "device": device,
            "disable_tf32": disable_tf32,  # Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas
            "sparse_weights": sparse_weights,  # Enable sparsity for convolution and fully connected layers.
            "enabled_precisions": enabled_precisions,  # Enabling FP16 kernels
            "refit": refit,  # enable refit
            "debug": debug,  # enable debuggable engine
            "capability": capability,  # Restrict kernel selection to safe gpu kernels or safe dla kernels
            "num_avg_timing_iters": num_avg_timing_iters,  # Number of averaging timing iterations used to select kernels
            "workspace_size": workspace_size,  # Maximum size of workspace given to TensorRT
            "calibrator": calibrator,
            "truncate_long_and_double": truncate_long_and_double,
        }

        return _C.convert_graph_to_trt_engine(
            module._c, method_name, _parse_compile_spec(compile_spec)
        )

    def embed_engine_in_new_module(
        serialized_engine: bytes, device=Device._current_device()
    ) -> torch.jit.ScriptModule:
        """Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module

        Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.
        Registers the forward method to execute the TensorRT engine with the function signature:

            forward(Tensor[]) -> Tensor[]

        TensorRT bindings must have names with the following format:
          - [symbol].[index in input / output array]
          ex.
          - [x.0, x.1, x.2] -> [y.0]

        Module can be save with engine embedded with torch.jit.save and moved / loaded according to torch_tensorrt portability rules

        Arguments:
            serialized_engine (bytes): Serialized TensorRT engine from either torch_tensorrt or TensorRT APIs

        Keyword Arguments:
            device (Union(torch_tensorrt.Device, torch.device, dict)): Target device to run engine on. Must be compatible with engine provided. Default: Current active device

        Returns:
            torch.jit.ScriptModule: New TorchScript module with engine embedded
        """
        cpp_mod = _C.embed_engine_in_new_module(serialized_engine, _parse_device(device))
        return torch.jit._recursive.wrap_cpp_module(cpp_mod)

    def check_method_op_support(module: torch.jit.ScriptModule, method_name: str) -> bool:
        """Checks to see if a method is fully supported by torch_tensorrt

        Checks if a method of a TorchScript module can be compiled by torch_tensorrt, if not, a list of operators
        that are not supported are printed out and the function returns false, else true.

        Arguments:
            module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch
                ``torch.nn.Module``
            method_name (str): Name of method to check

        Returns:
            bool: True if supported Method
        """
        return _C.check_method_op_support(module._c, method_name)

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