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import numpy as np
import json

# triton_python_backend_utils is available in every Triton Python model. You
# need to use this module to create inference requests and responses. It also
# contains some utility functions for extracting information from model_config
# and converting Triton input/output types to numpy types.
import triton_python_backend_utils as pb_utils


class TritonPythonModel:
    """Your Python model must use the same class name. Every Python model
    that is created must have "TritonPythonModel" as the class name.
    """

    def initialize(self, args):
        """`initialize` is called only once when the model is being loaded.
        Implementing `initialize` function is optional. This function allows
        the model to intialize any state associated with this model.

        Parameters
        ----------
        args : dict
          Both keys and values are strings. The dictionary keys and values are:
          * model_config: A JSON string containing the model configuration
          * model_instance_kind: A string containing model instance kind
          * model_instance_device_id: A string containing model instance device ID
          * model_repository: Model repository path
          * model_version: Model version
          * model_name: Model name
        """
        import re
        import string
        import tensorflow as tf
        from tensorflow.keras import layers

        print("tf: {}".format(tf.__version__))

        def custom_standardization(input_data):
            lowercase = tf.strings.lower(input_data)
            stripped_html = tf.strings.regex_replace(lowercase, "<br />", " ")
            return tf.strings.regex_replace(
                stripped_html, "[%s]" % re.escape(string.punctuation), ""
            )

        max_features = 10000
        sequence_length = 250

        vectorize_layer = layers.TextVectorization(
            standardize=custom_standardization,
            max_tokens=max_features,
            output_mode="int",
            output_sequence_length=sequence_length,
        )

        custom_objects = {"vectorize_layer": vectorize_layer,
                          "custom_standardization": custom_standardization}
        with tf.keras.utils.custom_object_scope(custom_objects):
            self.model = tf.keras.models.load_model(
                "/text_model"
            )

        # You must parse model_config. JSON string is not parsed here
        self.model_config = model_config = json.loads(args['model_config'])

        # Get output configuration
        pred_config = pb_utils.get_output_config_by_name(model_config, "pred")

        # Convert Triton types to numpy types
        self.pred_dtype = pb_utils.triton_string_to_numpy(pred_config['data_type'])

    def execute(self, requests):
        """`execute` MUST be implemented in every Python model. `execute`
        function receives a list of pb_utils.InferenceRequest as the only
        argument. This function is called when an inference request is made
        for this model. Depending on the batching configuration (e.g. Dynamic
        Batching) used, `requests` may contain multiple requests. Every
        Python model, must create one pb_utils.InferenceResponse for every
        pb_utils.InferenceRequest in `requests`. If there is an error, you can
        set the error argument when creating a pb_utils.InferenceResponse

        Parameters
        ----------
        requests : list
          A list of pb_utils.InferenceRequest

        Returns
        -------
        list
          A list of pb_utils.InferenceResponse. The length of this list must
          be the same as `requests`
        """

        pred_dtype = self.pred_dtype

        responses = []

        # Every Python backend must iterate over everyone of the requests
        # and create a pb_utils.InferenceResponse for each of them.
        for request in requests:
            # Get input numpy
            sentence_input = pb_utils.get_input_tensor_by_name(request, "sentence")
            sentences = list(sentence_input.as_numpy())
            sentences = np.squeeze(sentences).tolist()
            sentences = [s.decode('utf-8') for s in sentences]

            pred = self.model.predict(sentences)

            # Create output tensors. You need pb_utils.Tensor
            # objects to create pb_utils.InferenceResponse.
            pred_tensor = pb_utils.Tensor("pred", pred.astype(pred_dtype))

            # Create InferenceResponse. You can set an error here in case
            # there was a problem with handling this inference request.
            # Below is an example of how you can set errors in inference
            # response:
            #
            # pb_utils.InferenceResponse(
            #    output_tensors=..., TritonError("An error occured"))
            inference_response = pb_utils.InferenceResponse(output_tensors=[pred_tensor])
            responses.append(inference_response)

        # You should return a list of pb_utils.InferenceResponse. Length
        # of this list must match the length of `requests` list.
        return responses

    def finalize(self):
        """`finalize` is called only once when the model is being unloaded.
        Implementing `finalize` function is OPTIONAL. This function allows
        the model to perform any necessary clean ups before exit.
        """
        print('Cleaning up...')
