import json
import numpy
import asyncio
import triton_python_backend_utils as pb_utils

class TritonPythonModel:
    def initialize(self, args): self.target_dtype = pb_utils.triton_string_to_numpy(pb_utils.get_output_config_by_name(json.loads(args['model_config']), 'OUTPUT_TEXT')['data_type'])
    async def execute(self, requests): return [pb_utils.InferenceResponse(output_tensors=[pb_utils.Tensor('OUTPUT_TEXT', numpy.array([[pb_utils.get_output_tensor_by_name(result, 'OUTPUT_SENT').as_numpy()[0, 0].decode('utf-8')] for result in (await asyncio.gather(*awaits))], dtype=self.target_dtype))]) for awaits in [[pb_utils.InferenceRequest(model_name=f"himangy-{input_language_id[0].decode('utf-8')}-{output_language_id[0].decode('utf-8')}", requested_output_names=['OUTPUT_SENT'], inputs=[pb_utils.Tensor('INPUT_SENT_TOKENIZED', numpy.array([[input_text_tokenized[0].decode('utf-8')]], dtype='object'))]).async_exec() for input_text_tokenized, input_language_id, output_language_id in zip(pb_utils.get_input_tensor_by_name(request, 'INPUT_TEXT_TOKENIZED').as_numpy(), pb_utils.get_input_tensor_by_name(request, 'INPUT_LANGUAGE_ID').as_numpy(), pb_utils.get_input_tensor_by_name(request, 'OUTPUT_LANGUAGE_ID').as_numpy())] for request in requests]]
    def finalize(self): pass