import os

import argparse
import logging

import numpy as np
import torch
import datetime
import gradio as gr

from transformers import (
    CTRLLMHeadModel,
    CTRLTokenizer,
    GPT2LMHeadModel,
    GPT2Tokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO,
)
logger = logging.getLogger(__name__)

MAX_LENGTH = int(10000)  # Hardcoded max length to avoid infinite loop

MODEL_CLASSES = {
    "gpt2": (GPT2LMHeadModel, GPT2Tokenizer),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
}

def set_seed(args):
    rd = np.random.randint(100000)
    print('seed =', rd)
    np.random.seed(rd)
    torch.manual_seed(rd)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(rd)

#
# Functions to prepare models' input
#


def prepare_ctrl_input(args, _, tokenizer, prompt_text):
    if args.temperature > 0.7:
        logger.info("CTRL typically works better with lower temperatures (and lower top_k).")

    encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False)
    if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()):
        logger.info("WARNING! You are not starting your generation from a control code so you won't get good results")
    return prompt_text


def prepare_xlm_input(args, model, tokenizer, prompt_text):
    # kwargs = {"language": None, "mask_token_id": None}

    # Set the language
    use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb
    if hasattr(model.config, "lang2id") and use_lang_emb:
        available_languages = model.config.lang2id.keys()
        if args.xlm_language in available_languages:
            language = args.xlm_language
        else:
            language = None
            while language not in available_languages:
                language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ")

        model.config.lang_id = model.config.lang2id[language]
        # kwargs["language"] = tokenizer.lang2id[language]

    # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
    # XLM masked-language modeling (MLM) models need masked token
    # is_xlm_mlm = "mlm" in args.model_name_or_path
    # if is_xlm_mlm:
    #     kwargs["mask_token_id"] = tokenizer.mask_token_id

    return prompt_text


def prepare_xlnet_input(args, _, tokenizer, prompt_text):
    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
    return prompt_text


def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
    prompt_text = (args.padding_text if args.padding_text else PADDING_TEXT) + prompt_text
    return prompt_text


PREPROCESSING_FUNCTIONS = {
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}


def adjust_length_to_model(length, max_sequence_length):
    if length < 0 and max_sequence_length > 0:
        length = max_sequence_length
    elif 0 < max_sequence_length < length:
        length = max_sequence_length  # No generation bigger than model size
    elif length < 0:
        length = MAX_LENGTH  # avoid infinite loop
    return length


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_type",
        default=None,
        type=str,
        required=True,
        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
    )
    parser.add_argument(
        "--model_name_or_path",
        default=None,
        type=str,
        required=True,
        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
    )

    parser.add_argument("--prompt", type=str, default="")
    parser.add_argument("--length", type=int, default=20)
    parser.add_argument("--stop_token", type=str, default="</s>", help="Token at which lyrics generation is stopped")

    parser.add_argument(
        "--temperature",
        type=float,
        default=1.0,
        help="temperature of 1.0 has no effect, lower tend toward greedy sampling",
    )
    parser.add_argument(
        "--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2"
    )
    parser.add_argument("--k", type=int, default=0)
    parser.add_argument("--p", type=float, default=0.9)

    parser.add_argument("--padding_text", type=str, default="", help="Padding lyrics for Transfo-XL and XLNet.")
    parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.")

    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
    parser.add_argument("--num_return_sequences", type=int, default=1, help="The number of samples to generate.")
    args = parser.parse_args()

    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
    args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()

    # Initialize the model and tokenizer
    try:
        args.model_type = args.model_type.lower()
        model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
    except KeyError:
        raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)")

    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
    model = model_class.from_pretrained(args.model_name_or_path)
    model.to(args.device)

    args.length = adjust_length_to_model(args.length, max_sequence_length=model.config.max_position_embeddings)
    logger.info(args)
    generated_sequences = []
    prompt_text = ""
    while prompt_text != "stop":
        set_seed(args)
        while not len(prompt_text):
            prompt_text = args.prompt if args.prompt else input("Context >>> ")

        # Different models need different input formatting and/or extra arguments
        requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys()
        if requires_preprocessing:
            prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
            preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text)
            encoded_prompt = tokenizer.encode(
                preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", add_space_before_punct_symbol=True
            )
        else:
            encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False, return_tensors="pt")
        encoded_prompt = encoded_prompt.to(args.device)

        output_sequences = model.generate(
            input_ids=encoded_prompt,
            max_length=args.length + len(encoded_prompt[0]),
            temperature=args.temperature,
            top_k=args.k,
            top_p=args.p,
            repetition_penalty=args.repetition_penalty,
            do_sample=True,
            num_return_sequences=args.num_return_sequences,
        )

        # Remove the batch dimension when returning multiple sequences
        if len(output_sequences.shape) > 2:
            output_sequences.squeeze_()

        now = datetime.datetime.now()
        date_time = now.strftime('%Y%m%d_%H%M%S%f')

        for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
            print("ruGPT:".format(generated_sequence_idx + 1))
            generated_sequence = generated_sequence.tolist()

            # Decode lyrics
            text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)

            # Remove all lyrics after the stop token
            text = text[: text.find(args.stop_token) if args.stop_token else None]

            # Add the prompt at the beginning of the sequence. Remove the excess lyrics that was used for pre-processing
            total_sequence = (
                prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
            )

            generated_sequences.append(total_sequence)
            # os.system('clear')
            print(total_sequence)

        prompt_text = ""
        if args.prompt:
            break

    return generated_sequences

title = "ruGPT3 Song Writer"
description = "Generate russian songs via fine-tuned ruGPT3"

gr.Interface(
    process, 
    gr.inputs.Textbox(lines=1, label="Input text", examples="Как дела? Как дела? Это новый кадиллак"),
    gr.outputs.Textbox(lines=20, label="Output text"),
    title=title,
    description=description,
    ).launch(enable_queue=True,cache_examples=True)