# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities for collecting TFLite metrics."""

import collections
import enum
import functools
from typing import Text

from tensorflow.lite.python.metrics import converter_error_data_pb2
from tensorflow.lite.python.metrics import metrics


class Component(enum.Enum):
  """Enum class defining name of the converter components."""
  # Validate the given input and prepare and optimize TensorFlow Model.
  PREPARE_TF_MODEL = "PREPARE_TF_MODEL"

  # Convert to TFLite model format.
  CONVERT_TF_TO_TFLITE_MODEL = "CONVERT_TF_TO_TFLITE_MODEL"

  # RUN quantization and sparsification.
  OPTIMIZE_TFLITE_MODEL = "OPTIMIZE_TFLITE_MODEL"


SubComponentItem = collections.namedtuple("SubComponentItem",
                                          ["name", "component"])


class SubComponent(SubComponentItem, enum.Enum):
  """Enum class defining name of the converter subcomponents.

  This enum only defines the subcomponents in Python, there might be more
  subcomponents defined in C++.
  """

  def __str__(self):
    return self.value.name

  @property
  def name(self):
    return self.value.name

  @property
  def component(self):
    return self.value.component

  # The subcomponent name is unspecified.
  UNSPECIFIED = SubComponentItem("UNSPECIFIED", None)

  # Valid the given input and parameters.
  VALIDATE_INPUTS = SubComponentItem("VALIDATE_INPUTS",
                                     Component.PREPARE_TF_MODEL)

  # Load GraphDef from SavedModel.
  LOAD_SAVED_MODEL = SubComponentItem("LOAD_SAVED_MODEL",
                                      Component.PREPARE_TF_MODEL)

  # Convert a SavedModel to frozen graph.
  FREEZE_SAVED_MODEL = SubComponentItem("FREEZE_SAVED_MODEL",
                                        Component.PREPARE_TF_MODEL)

  # Save a Keras model to SavedModel.
  CONVERT_KERAS_TO_SAVED_MODEL = SubComponentItem(
      "CONVERT_KERAS_TO_SAVED_MODEL", Component.PREPARE_TF_MODEL)

  # Save Concrete functions to SavedModel.
  CONVERT_CONCRETE_FUNCTIONS_TO_SAVED_MODEL = SubComponentItem(
      "CONVERT_CONCRETE_FUNCTIONS_TO_SAVED_MODEL", Component.PREPARE_TF_MODEL)

  # Convert a Keras model to a frozen graph.
  FREEZE_KERAS_MODEL = SubComponentItem("FREEZE_KERAS_MODEL",
                                        Component.PREPARE_TF_MODEL)

  # Replace all the variables with constants in a ConcreteFunction.
  FREEZE_CONCRETE_FUNCTION = SubComponentItem("FREEZE_CONCRETE_FUNCTION",
                                              Component.PREPARE_TF_MODEL)

  # Run grappler optimization.
  OPTIMIZE_TF_MODEL = SubComponentItem("OPTIMIZE_TF_MODEL",
                                       Component.PREPARE_TF_MODEL)

  # Convert using the old TOCO converter.
  CONVERT_GRAPHDEF_USING_DEPRECATED_CONVERTER = SubComponentItem(
      "CONVERT_GRAPHDEF_USING_DEPRECATED_CONVERTER",
      Component.CONVERT_TF_TO_TFLITE_MODEL)

  # Convert a GraphDef to TFLite model.
  CONVERT_GRAPHDEF = SubComponentItem("CONVERT_GRAPHDEF",
                                      Component.CONVERT_TF_TO_TFLITE_MODEL)

  # Convert a SavedModel to TFLite model.
  CONVERT_SAVED_MODEL = SubComponentItem("CONVERT_SAVED_MODEL",
                                         Component.CONVERT_TF_TO_TFLITE_MODEL)

  # Convert a Jax HLO to TFLite model.
  CONVERT_JAX_HLO = SubComponentItem("CONVERT_JAX_HLO",
                                     Component.CONVERT_TF_TO_TFLITE_MODEL)

  # Do quantization by the deprecated quantizer.
  QUANTIZE_USING_DEPRECATED_QUANTIZER = SubComponentItem(
      "QUANTIZE_USING_DEPRECATED_QUANTIZER", Component.OPTIMIZE_TFLITE_MODEL)

  # Do calibration.
  CALIBRATE = SubComponentItem("CALIBRATE", Component.OPTIMIZE_TFLITE_MODEL)

  # Do quantization by MLIR.
  QUANTIZE = SubComponentItem("QUANTIZE", Component.OPTIMIZE_TFLITE_MODEL)

  # Do sparsification by MLIR.
  SPARSIFY = SubComponentItem("SPARSIFY", Component.OPTIMIZE_TFLITE_MODEL)


class ConverterError(Exception):
  """Raised when an error occurs during model conversion."""

  def __init__(self, message):
    super(ConverterError, self).__init__(message)
    self.errors = []
    self._parse_error_message(message)

  def append_error(self,
                   error_data: converter_error_data_pb2.ConverterErrorData):
    self.errors.append(error_data)

  def _parse_error_message(self, message):
    """If the message matches a pattern, assigns the associated error code.

    It is difficult to assign an error code to some errrors in MLIR side, Ex:
    errors thrown by other components than TFLite or not using mlir::emitError.
    This function try to detect them by the error message and assign the
    corresponding error code.

    Args:
      message: The error message of this exception.
    """
    error_code_mapping = {
        "Failed to functionalize Control Flow V1 ops. Consider using Control "
        "Flow V2 ops instead. See https://www.tensorflow.org/api_docs/python/"
        "tf/compat/v1/enable_control_flow_v2.":
            converter_error_data_pb2.ConverterErrorData
            .ERROR_UNSUPPORTED_CONTROL_FLOW_V1,
    }
    for pattern, error_code in error_code_mapping.items():
      if pattern in message:
        error_data = converter_error_data_pb2.ConverterErrorData()
        error_data.error_message = message
        error_data.error_code = error_code
        self.append_error(error_data)
        return


def convert_phase(component, subcomponent=SubComponent.UNSPECIFIED):
  """The decorator to identify converter component and subcomponent.

  Args:
    component: Converter component name.
    subcomponent: Converter subcomponent name.

  Returns:
    Forward the result from the wrapped function.

  Raises:
    ValueError: if component and subcomponent name is not valid.
  """
  if component not in Component:
    raise ValueError("Given component name not found")
  if subcomponent not in SubComponent:
    raise ValueError("Given subcomponent name not found")
  if (subcomponent != SubComponent.UNSPECIFIED and
      subcomponent.component != component):
    raise ValueError("component and subcomponent name don't match")

  def report_error(error_data: converter_error_data_pb2.ConverterErrorData):
    # Always overwrites the component information, but only overwrites the
    # subcomponent if it is not available.
    error_data.component = component.value
    if not error_data.subcomponent:
      error_data.subcomponent = subcomponent.name
    tflite_metrics = metrics.TFLiteConverterMetrics()
    tflite_metrics.set_converter_error(error_data)

  def report_error_message(error_message: Text):
    error_data = converter_error_data_pb2.ConverterErrorData()
    error_data.error_message = error_message
    report_error(error_data)

  def actual_decorator(func):

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
      try:
        return func(*args, **kwargs)
      except ConverterError as converter_error:
        if converter_error.errors:
          for error_data in converter_error.errors:
            report_error(error_data)
        else:
          report_error_message(str(converter_error))
        raise converter_error from None  # Re-throws the exception.
      except Exception as error:
        report_error_message(str(error))
        raise error from None  # Re-throws the exception.

    return wrapper

  return actual_decorator