from typing import List, Optional, Tuple

import pytest
import torch
from transformers import AutoModelForCausalLM

from vllm import LLM, SamplingParams
from vllm.transformers_utils.tokenizer import get_tokenizer

_TEST_PROMPTS = [
    # pylint: disable=line-too-long
    "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.",
    "Briefly describe the major milestones in the development of artificial intelligence from 1950 to 2020.",
    "Compare and contrast artificial intelligence with human intelligence in terms of processing information.",
    "Describe the basic components of a neural network and how it can be trained.",
    "Write a short story about a robot that dreams for the first time.",
    "Analyze the impact of the COVID-19 pandemic on global economic structures and future business models.",
    "Explain the cultural significance of the Mona Lisa painting, and how its perception might vary in Western versus Eastern societies.",
    "Translate the following English sentence into Japanese, French, and Swahili: 'The early bird catches the worm.'",
]


@pytest.fixture
def example_prompts() -> List[str]:
    return _TEST_PROMPTS


_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.half,
    "bfloat16": torch.bfloat16,
    "float": torch.float,
}


class HfRunner:

    def __init__(
        self,
        model_name: str,
        tokenizer_name: Optional[str] = None,
        dtype: str = "half",
    ) -> None:
        assert dtype in _STR_DTYPE_TO_TORCH_DTYPE
        torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch_dtype,
            trust_remote_code=True,
        ).cuda()
        if tokenizer_name is None:
            tokenizer_name = model_name
        self.tokenizer = get_tokenizer(tokenizer_name, trust_remote_code=True)

    def generate(
        self,
        prompts: List[str],
        **kwargs,
    ) -> List[Tuple[List[int], str]]:
        outputs: List[Tuple[List[int], str]] = []
        for prompt in prompts:
            input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
            output_ids = self.model.generate(
                input_ids.cuda(),
                use_cache=True,
                **kwargs,
            )
            output_str = self.tokenizer.batch_decode(
                output_ids,
                skip_special_tokens=True,
                clean_up_tokenization_spaces=False,
            )
            output_ids = output_ids.cpu().tolist()
            outputs.append((output_ids, output_str))
        return outputs

    def generate_greedy(
        self,
        prompts: List[str],
        max_tokens: int,
    ) -> List[Tuple[List[int], str]]:
        outputs = self.generate(prompts,
                                do_sample=False,
                                max_new_tokens=max_tokens)
        for i in range(len(outputs)):
            output_ids, output_str = outputs[i]
            outputs[i] = (output_ids[0], output_str[0])
        return outputs

    def generate_beam_search(
        self,
        prompts: List[str],
        beam_width: int,
        max_tokens: int,
    ) -> List[Tuple[List[int], str]]:
        outputs = self.generate(prompts,
                                do_sample=False,
                                max_new_tokens=max_tokens,
                                num_beams=beam_width,
                                num_return_sequences=beam_width)
        for i in range(len(outputs)):
            output_ids, output_str = outputs[i]
            for j in range(len(output_ids)):
                output_ids[j] = [
                    x for x in output_ids[j]
                    if x != self.tokenizer.pad_token_id
                ]
            outputs[i] = (output_ids, output_str)
        return outputs

    def generate_greedy_logprobs(
        self,
        prompts: List[str],
        max_tokens: int,
    ) -> List[List[torch.Tensor]]:
        all_logprobs = []
        for prompt in prompts:
            input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
            output = self.model.generate(
                input_ids.cuda(),
                use_cache=True,
                do_sample=False,
                max_new_tokens=max_tokens,
                output_hidden_states=True,
                return_dict_in_generate=True,
            )
            seq_logprobs = []
            for hidden_states in output.hidden_states:
                last_hidden_states = hidden_states[-1][0]
                logits = torch.matmul(
                    last_hidden_states,
                    self.model.get_output_embeddings().weight.t(),
                )
                if self.model.get_output_embeddings().bias is not None:
                    logits += self.model.get_output_embeddings(
                    ).bias.unsqueeze(0)
                logprobs = torch.nn.functional.log_softmax(logits,
                                                           dim=-1,
                                                           dtype=torch.float32)
                seq_logprobs.append(logprobs)
            all_logprobs.append(seq_logprobs)
        return all_logprobs


@pytest.fixture
def hf_runner():
    return HfRunner


class VllmRunner:

    def __init__(
        self,
        model_name: str,
        tokenizer_name: Optional[str] = None,
        dtype: str = "half",
    ) -> None:
        self.model = LLM(
            model=model_name,
            tokenizer=tokenizer_name,
            trust_remote_code=True,
            dtype=dtype,
            swap_space=0,
        )

    def generate(
        self,
        prompts: List[str],
        sampling_params: SamplingParams,
    ) -> List[Tuple[List[int], str]]:
        req_outputs = self.model.generate(prompts,
                                          sampling_params=sampling_params)
        outputs = []
        for req_output in req_outputs:
            prompt_str = req_output.prompt
            prompt_ids = req_output.prompt_token_ids
            req_sample_output_ids = []
            req_sample_output_strs = []
            for sample in req_output.outputs:
                output_str = sample.text
                output_ids = sample.token_ids
                req_sample_output_ids.append(prompt_ids + output_ids)
                req_sample_output_strs.append(prompt_str + output_str)
            outputs.append((req_sample_output_ids, req_sample_output_strs))
        return outputs

    def generate_greedy(
        self,
        prompts: List[str],
        max_tokens: int,
    ) -> List[Tuple[List[int], str]]:
        greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
        outputs = self.generate(prompts, greedy_params)
        return [(output_ids[0], output_str[0])
                for output_ids, output_str in outputs]

    def generate_beam_search(
        self,
        prompts: List[str],
        beam_width: int,
        max_tokens: int,
    ) -> List[Tuple[List[int], str]]:
        beam_search_params = SamplingParams(n=beam_width,
                                            use_beam_search=True,
                                            temperature=0.0,
                                            max_tokens=max_tokens)
        outputs = self.generate(prompts, beam_search_params)
        return outputs


@pytest.fixture
def vllm_runner():
    return VllmRunner
