import torch
import torch.nn as nn
import os
from transformers import (
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    T5EncoderModel,
    T5TokenizerFast
)
from typing import Union, List, Optional

class SD3TextEncoderWithMask(nn.Module):
    def __init__(self, model_path, torch_dtype=torch.float16):  # Default to FP16
        super().__init__()

        # Tokenizers for CLIP and T5
        self.tokenizer = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer'))
        self.tokenizer_2 = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer_2'))
        self.tokenizer_3 = T5TokenizerFast.from_pretrained(os.path.join(model_path, 'tokenizer_3'))

        # Lazy loading for memory efficiency
        self.text_encoder = None
        self.text_encoder_2 = None
        self.text_encoder_3 = None
        self.model_path = model_path
        self.torch_dtype = torch_dtype
        self.tokenizer_max_length = self.tokenizer.model_max_length

        # Freeze parameters to avoid unnecessary gradient computation
        self._freeze()

    def _freeze(self):
        """ Freeze all model parameters to avoid training overhead. """
        for param in self.parameters():
            param.requires_grad = False

    def _load_models_if_needed(self):
        """ Load models only if they haven't been loaded already. """
        if self.text_encoder is None:
            self.text_encoder = CLIPTextModelWithProjection.from_pretrained(
                os.path.join(self.model_path, 'text_encoder'), torch_dtype=self.torch_dtype
            ).to("cuda")

        if self.text_encoder_2 is None:
            self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
                os.path.join(self.model_path, 'text_encoder_2'), torch_dtype=self.torch_dtype
            ).to("cuda")

        if self.text_encoder_3 is None:
            # Load the T5 model in FP16
            self.text_encoder_3 = T5EncoderModel.from_pretrained(
                os.path.join(self.model_path, 'text_encoder_3'),  # Ensure you're using the correct path
                torch_dtype=self.torch_dtype  # Use FP16 precision
            ).to("cuda")

    def _get_t5_prompt_embeds(
        self,
        prompt: Union[str, List[str]] = None,
        num_images_per_prompt: int = 1,
        device: Optional[torch.device] = None,
        max_sequence_length: int = 128,
    ):
        """ Get embeddings from T5 model. """
        self._load_models_if_needed()  # Lazy loading
        prompt = [prompt] if isinstance(prompt, str) else prompt
        text_inputs = self.tokenizer_3(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            add_special_tokens=True,
            return_tensors="pt",
        )

        text_input_ids = text_inputs.input_ids.to(device)
        prompt_attention_mask = text_inputs.attention_mask.to(device)

        # Use the T5 model to generate embeddings in FP16
        prompt_embeds = self.text_encoder_3(text_input_ids, attention_mask=prompt_attention_mask)[0]
        prompt_embeds = prompt_embeds.to(dtype=self.torch_dtype)  # Ensure correct dtype

        # Duplicate embeddings for each image generation
        batch_size = len(prompt)
        _, seq_len, _ = prompt_embeds.shape
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1).view(batch_size * num_images_per_prompt, seq_len, -1)
        prompt_attention_mask = prompt_attention_mask.view(batch_size, -1).repeat(num_images_per_prompt, 1)

        return prompt_embeds, prompt_attention_mask

    def _get_clip_prompt_embeds(
        self,
        prompt: Union[str, List[str]],
        num_images_per_prompt: int = 1,
        device: Optional[torch.device] = None,
        clip_model_index: int = 0,
    ):
        """ Get embeddings from CLIP model. """
        self._load_models_if_needed()  # Lazy loading

        clip_tokenizers = [self.tokenizer, self.tokenizer_2]
        clip_text_encoders = [self.text_encoder, self.text_encoder_2]

        tokenizer = clip_tokenizers[clip_model_index]
        text_encoder = clip_text_encoders[clip_model_index]

        text_inputs = tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer_max_length,
            truncation=True,
            return_tensors="pt",
        )

        text_input_ids = text_inputs.input_ids.to(device)
        prompt_embeds = text_encoder(text_input_ids, output_hidden_states=True)[0]

        # Duplicate embeddings for each image generation
        batch_size = len(prompt)
        pooled_prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1).view(batch_size * num_images_per_prompt, -1)

        return pooled_prompt_embeds

    def encode_prompt(self, 
        prompt, 
        num_images_per_prompt=1, 
        device=None
    ):
        """ Encode the prompt using both CLIP and T5 models. """
        prompt = [prompt] if isinstance(prompt, str) else prompt

        # Get embeddings from both CLIP models
        pooled_prompt_embed = self._get_clip_prompt_embeds(prompt, num_images_per_prompt=num_images_per_prompt, device=device, clip_model_index=0)
        pooled_prompt_2_embed = self._get_clip_prompt_embeds(prompt, num_images_per_prompt=num_images_per_prompt, device=device, clip_model_index=1)
        pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)

        # Get T5 embeddings in FP16
        prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(prompt, num_images_per_prompt=num_images_per_prompt, device=device)

        return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds

    def forward(self, input_prompts, device):
        """ Forward pass for encoding prompts. """
        with torch.no_grad():
            prompt_embeds, prompt_attention_mask, pooled_prompt_embeds = self.encode_prompt(input_prompts, num_images_per_prompt=1, device=device)

        return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds