# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# 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,
# 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.
import argparse
import json
import os

import torch
from transformers import BloomTokenizerFast

import xtrt_llm
from xtrt_llm.runtime import ModelConfig, SamplingConfig
import numpy as np

from build import get_engine_name  # isort:skip

EOS_TOKEN = 2
PAD_TOKEN = 3


def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--max_output_len', type=int, required=True)
    parser.add_argument('--log_level', type=str, default='error')
    parser.add_argument('--engine_dir', type=str, default='bloom_outputs')
    parser.add_argument('--tokenizer_dir',
                        type=str,
                        default=".",
                        help="Directory containing the tokenizer.model.")
    parser.add_argument('--input_text',
                        type=str,
                        default='Born in north-east France, Soyer trained as a')
    parser.add_argument(
        '--performance_test_scale',
        type=str,
        help=
        "Scale for performance test. e.g., 8x1024x64 (batch_size, input_text_length, max_output_length)",
        default="")
    return parser.parse_args()


if __name__ == '__main__':
    args = parse_arguments()
    xtrt_llm.logger.set_level(args.log_level)

    config_path = os.path.join(args.engine_dir, 'config.json')
    with open(config_path, 'r') as f:
        config = json.load(f)
    use_gpt_attention_plugin = config['plugin_config']['gpt_attention_plugin']
    dtype = config['builder_config']['precision']
    world_size = config['builder_config']['tensor_parallel']
    assert world_size == xtrt_llm.mpi_world_size(), \
        f'Engine world size ({world_size}) != Runtime world size ({xtrt_llm.mpi_world_size()})'
    num_heads = config['builder_config']['num_heads'] // world_size
    hidden_size = config['builder_config']['hidden_size'] // world_size
    vocab_size = config['builder_config']['vocab_size']
    num_layers = config['builder_config']['num_layers']

    runtime_rank = xtrt_llm.mpi_rank()
    if world_size > 1:
        os.environ["XCCL_GROUP_ID"] = str(runtime_rank // world_size)
        os.environ["XCCL_NRANKS"] = str(world_size)
        os.environ["XCCL_CUR_RANK"] = str(runtime_rank % world_size)
        os.environ["XCCL_DEVICE_ID"] = str(runtime_rank)
        os.environ["MP_RUN"] = str(1)
    runtime_mapping = xtrt_llm.Mapping(world_size,
                                           runtime_rank,
                                           tp_size=world_size)
    torch.cuda.set_device(runtime_rank % runtime_mapping.gpus_per_node)

    engine_name = get_engine_name('bloom', dtype, world_size, runtime_rank)
    serialize_path = os.path.join(args.engine_dir, engine_name)

    tokenizer = BloomTokenizerFast.from_pretrained(args.tokenizer_dir)
    input_ids = torch.tensor(tokenizer.encode(args.input_text),
                             dtype=torch.int32).cuda().unsqueeze(0)

    model_config = ModelConfig(num_heads=num_heads,
                               num_kv_heads=num_heads,
                               hidden_size=hidden_size,
                               vocab_size=vocab_size,
                               num_layers=num_layers,
                               gpt_attention_plugin=use_gpt_attention_plugin,
                               dtype=dtype)
    sampling_config = SamplingConfig(end_id=EOS_TOKEN, pad_id=PAD_TOKEN)
    input_lengths = torch.tensor(
        [input_ids.size(1) for _ in range(input_ids.size(0))]).int().cuda()

    # with open(serialize_path, 'rb') as f:
    #     engine_buffer = f.read()
    decoder = xtrt_llm.runtime.GenerationSession(model_config,
                                                     serialize_path,
                                                     runtime_mapping)
    if args.performance_test_scale != "":
        performance_test_scale_list = args.performance_test_scale.split("E")
        for scale in performance_test_scale_list:
            xtrt_llm.logger.info(f"Running performance test with scale {scale}")
            bs, seqlen, _max_output_len = [int(x) for x in scale.split("x")]
            _input_ids = torch.from_numpy(
                np.zeros((bs, seqlen)).astype("int32")).cuda()
            _input_lengths = torch.from_numpy(
                np.full((bs, ), seqlen).astype("int32")).cuda()
            _max_input_length = torch.max(_input_lengths).item()
            decoder.setup(_input_lengths.size(0), _max_input_length,
                          _max_output_len)
            _output_gen_ids = decoder.decode(_input_ids,
                                             _input_lengths,
                                             sampling_config)
    decoder.setup(input_ids.size(0),
                  max_context_length=input_ids.size(1),
                  max_new_tokens=args.max_output_len)
    output_ids = decoder.decode(input_ids, input_lengths, sampling_config)
    torch.cuda.synchronize()

    output_ids = output_ids.tolist()[0][0][input_ids.size(1):]
    output_text = tokenizer.decode(output_ids)
    print(f'Input: \"{args.input_text}\"')
    print(f'Output Ids: \"{output_ids}\"')
    print(f'Output: \"{output_text}\"')