import enum
import math
from dataclasses import dataclass, field
from typing import Optional, Union

import torch

from ..utils import PeftType, PromptLearningConfig


class PromptTuningInit(str, enum.Enum):
    TEXT = "TEXT"
    RANDOM = "RANDOM"


@dataclass
class PromptTuningConfig(PromptLearningConfig):
    """
    This is the configuration class to store the configuration of a [`~peft.PromptEmbedding`].

    Args:
        prompt_tuning_init (Union[[`PromptTuningInit`], `str`]): The initialization of the prompt embedding.
        prompt_tuning_init_text ( Optional[`str`]): The text to initialize the prompt embedding.
            Only used if `prompt_tuning_init` is `TEXT`
        tokenizer_name_or_path ( Optional[`str`]): The name or path of the tokenizer.
            Only used if `prompt_tuning_init` is `TEXT`
    """

    prompt_tuning_init: Union[PromptTuningInit, str] = field(
        default=PromptTuningInit.RANDOM,
        metadata={"help": "How to initialize the prompt tuning parameters"},
    )
    prompt_tuning_init_text: Optional[str] = field(
        default=None,
        metadata={
            "help": "The text to use for prompt tuning initialization. Only used if prompt_tuning_init is `TEXT`"
        },
    )
    tokenizer_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "The tokenizer to use for prompt tuning initialization. Only used if prompt_tuning_init is `TEXT`"
        },
    )

    def __post_init__(self):
        self.peft_type = PeftType.PROMPT_TUNING


class PromptEmbedding(torch.nn.Module):
    """
    The model to encode virtual tokens into prompt embeddings.

    Args:
        config ([`PromptTuningConfig`]): The configuration of the prompt embedding.
        word_embeddings (`torch.nn.Module`): The word embeddings of the base transformer model.

    **Attributes**:
        **embedding** (`torch.nn.Embedding`) -- The embedding layer of the prompt embedding.

    Example::

        >>> from peft import PromptEmbedding, PromptTuningConfig >>> config = PromptTuningConfig(
                peft_type="PROMPT_TUNING", task_type="SEQ_2_SEQ_LM", num_virtual_tokens=20, token_dim=768,
                num_transformer_submodules=1, num_attention_heads=12, num_layers=12, prompt_tuning_init="TEXT",
                prompt_tuning_init_text="Predict if sentiment of this review is positive, negative or neutral",
                tokenizer_name_or_path="t5-base",
            )
        >>> # t5_model.shared is the word embeddings of the base model >>> prompt_embedding = PromptEmbedding(config,
        t5_model.shared)


    Input Shape: (batch_size, total_virtual_tokens)

    Output Shape: (batch_size, total_virtual_tokens, token_dim)
    """

    def __init__(self, config, word_embeddings):
        super().__init__()

        total_virtual_tokens = config.num_virtual_tokens * config.num_transformer_submodules
        self.embedding = torch.nn.Embedding(total_virtual_tokens, config.token_dim)
        if config.prompt_tuning_init == PromptTuningInit.TEXT:
            from transformers import AutoTokenizer

            tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name_or_path)
            init_text = config.prompt_tuning_init_text
            init_token_ids = tokenizer(init_text)["input_ids"]
            # Trim or iterate until num_text_tokens matches total_virtual_tokens
            num_text_tokens = len(init_token_ids)
            if num_text_tokens > total_virtual_tokens:
                init_token_ids = init_token_ids[:total_virtual_tokens]
            elif num_text_tokens < total_virtual_tokens:
                num_reps = math.ceil(total_virtual_tokens / num_text_tokens)
                init_token_ids = init_token_ids * num_reps
            init_token_ids = init_token_ids[:total_virtual_tokens]

            word_embedding_weights = word_embeddings(torch.LongTensor(init_token_ids)).detach().clone()
            word_embedding_weights = word_embedding_weights.to(torch.float32)
            self.embedding.weight = torch.nn.Parameter(word_embedding_weights)

    def forward(self, indices):
        # Just get embeddings
        prompt_embeddings = self.embedding(indices)
        return prompt_embeddings
