#!/usr/bin/env python3
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# SPDX-License-Identifier: Apache-2.0
#
# 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,
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# See the License for the specific language governing permissions and
# limitations under the License.

import argparse as _arg
from pathlib import Path

import run
from build_engines_utils import init_model_spec_module

init_model_spec_module()
import os

import model_spec

import tensorrt_llm.bindings as _tb


def generate_output(engine: str,
                    num_beams: int,
                    model_spec_obj: model_spec.ModelSpec,
                    max_output_len: int = 4):

    tp_size = 1
    pp_size = 1
    cp_size = 1
    model = 'gpt-j-6b'
    resources_dir = Path(__file__).parent.resolve().parent
    models_dir = resources_dir / 'models'
    hf_dir = models_dir / model
    tp_pp_cp_dir = 'tp' + str(tp_size) + '-pp' + str(pp_size) + '-cp' + str(
        cp_size) + '-gpu/'
    engine_dir = models_dir / 'rt_engine' / model / engine / tp_pp_cp_dir

    data_dir = resources_dir / 'data'
    input_file = data_dir / 'input_tokens.npy'
    model_data_dir = data_dir / model
    if num_beams <= 1:
        output_dir = model_data_dir / 'sampling'
    else:
        output_dir = model_data_dir / ('beam_search_' + str(num_beams))

    base_output_name = os.path.splitext(model_spec_obj.get_results_file())[0]

    args = run.parse_arguments([
        '--engine_dir',
        str(engine_dir), '--input_file',
        str(input_file), '--tokenizer_dir',
        str(hf_dir), '--output_npy',
        str(output_dir / (base_output_name + '.npy')), '--output_csv',
        str(output_dir / (base_output_name + '.csv')), '--max_output_len',
        str(max_output_len), '--num_beams',
        str(num_beams), '--use_py_session'
    ])
    run.main(args)


def generate_outputs(only_fp8, num_beams):
    input_file = 'input_tokens.npy'
    if only_fp8 and num_beams == 1:
        model_spec_obj = model_spec.ModelSpec(input_file, _tb.DataType.FP8)
        model_spec_obj.use_gpt_plugin()
        model_spec_obj.set_kv_cache_type(_tb.KVCacheType.PAGED)
        model_spec_obj.use_packed_input()

        print('Generating GPT-J FP8-kv-cache outputs')
        generate_output(engine=model_spec_obj.get_model_path(),
                        num_beams=num_beams,
                        model_spec_obj=model_spec_obj)
    elif not only_fp8:
        print('Generating GPT-J FP16 outputs')
        model_spec_obj = model_spec.ModelSpec(input_file, _tb.DataType.HALF)
        model_spec_obj.use_gpt_plugin()
        model_spec_obj.set_kv_cache_type(_tb.KVCacheType.CONTINUOUS)
        generate_output(engine=model_spec_obj.get_model_path(),
                        num_beams=num_beams,
                        model_spec_obj=model_spec_obj)

        model_spec_obj.use_packed_input()
        generate_output(engine=model_spec_obj.get_model_path(),
                        num_beams=num_beams,
                        model_spec_obj=model_spec_obj)

        model_spec_obj.set_kv_cache_type(_tb.KVCacheType.PAGED)
        generate_output(engine=model_spec_obj.get_model_path(),
                        num_beams=num_beams,
                        model_spec_obj=model_spec_obj)


if __name__ == '__main__':
    parser = _arg.ArgumentParser()
    parser.add_argument(
        "--only_fp8",
        action="store_true",
        help="Generate data for only FP8 tests. Implemented for H100 runners.")

    generate_outputs(**vars(parser.parse_args()), num_beams=1)
    generate_outputs(**vars(parser.parse_args()), num_beams=2)