from transformers import FlaxAutoModelForSeq2SeqLM
from transformers import AutoTokenizer

MODEL_NAME_OR_PATH = "flax-community/t5-recipe-generation"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME_OR_PATH)

prefix = "items: "
# generation_kwargs = {
#     "max_length": 512,
#     "min_length": 64,
#     "no_repeat_ngram_size": 3,
#     "early_stopping": True,
#     "num_beams": 5,
#     "length_penalty": 1.5,
# }
generation_kwargs = {
    "max_length": 512,
    "min_length": 64,
    "no_repeat_ngram_size": 3,
    "do_sample": True,
    "top_k": 60,
    "top_p": 0.95
}


special_tokens = tokenizer.all_special_tokens
tokens_map = {
    "<sep>": "--",
    "<section>": "\n"
}
def skip_special_tokens(text, special_tokens):
    for token in special_tokens:
        text = text.replace(token, "")

    return text

def target_postprocessing(texts, special_tokens):
    if not isinstance(texts, list):
        texts = [texts]
    
    new_texts = []
    for text in texts:
        text = skip_special_tokens(text, special_tokens)

        for k, v in tokens_map.items():
            text = text.replace(k, v)

        new_texts.append(text)

    return new_texts

def generation_function(texts):
    _inputs = texts if isinstance(texts, list) else [texts]
    inputs = [prefix + inp for inp in _inputs]
    inputs = tokenizer(
        inputs, 
        max_length=256, 
        padding="max_length", 
        truncation=True, 
        return_tensors="jax"
    )

    input_ids = inputs.input_ids
    attention_mask = inputs.attention_mask

    output_ids = model.generate(
        input_ids=input_ids, 
        attention_mask=attention_mask,
        **generation_kwargs
    )
    generated = output_ids.sequences
    generated_recipe = target_postprocessing(
        tokenizer.batch_decode(generated, skip_special_tokens=False),
        special_tokens
    )
    return generated_recipe

items = [
    "chicken", "salt", "cumin"
]

generated = generation_function(items)
for text in generated:
    sections = text.split("\n")
    for section in sections:
        section = section.strip()
        if section.startswith("title:"):
            section = section.replace("title:", "")
            headline = "TITLE"
        elif section.startswith("ingredients:"):
            section = section.replace("ingredients:", "")
            headline = "INGREDIENTS"
        elif section.startswith("directions:"):
            section = section.replace("directions:", "")
            headline = "DIRECTIONS"
        
        if headline == "TITLE":
            print(f"[{headline}]: {section.strip().capitalize()}")
        else:
            section_info = [f"  - {i+1}: {info.strip().capitalize()}" for i, info in enumerate(section.split("--"))]
            print(f"[{headline}]:")
            print("\n".join(section_info))

    print("-" * 130)