import gradio as gr
import os, gc, copy, torch
from datetime import datetime
from huggingface_hub import hf_hub_download
from transformers import CLIPVisionModel
import torch.nn as nn
import torch.nn.functional as F

ctx_limit = 3500
title = "rwkv1b5-vitl336p14-577token_mix665k_rwkv"

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster)

from rwkv.model import RWKV
model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=f"{title}.pth")
model = RWKV(model=model_path, strategy='cpu fp32')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "rwkv_vocab_v20230424")


class VisualRWKV(nn.Module):
    def __init__(self, args):
        super().__init__()
        self.args = args
        self.vit = CLIPVisionModel.from_pretrained(args.vision_tower_name)
        self.proj = nn.Linear(self.vit.config.hidden_size, args.n_embd, bias=False)

    def encode_images(self, images):
        B, N, C, H, W = images.shape
        images = images.view(B*N, C, H, W)
        image_features = self.vit(images).last_hidden_state
        L, D = image_features.shape[1], image_features.shape[2]
        # rerange [B*N, L, D] -> [B, N, L, D]
        image_features = image_features.view(B, N, L, D)[:, 0, :, :]
        image_features = self.grid_pooling(image_features)
        return self.proj(image_features)
    
    def grid_pooling(self, image_features):
        if self.args.grid_size == -1: # no grid pooling
            return image_features
        if self.args.grid_size == 0: # take cls token
            return image_features[:, 0:1, :]
        if self.args.grid_size == 1: # global avg pooling
            return image_features.mean(dim=1, keepdim=True)
        cls_features = image_features[:, 0:1, :]
        image_features = image_features[:, 1:, :] #drop cls token
        B, L, D = image_features.shape
        H_or_W = int(L**0.5)
        image_features = image_features.view(B, H_or_W, H_or_W, D)
        grid_stride = H_or_W // self.args.grid_size
        image_features = F.avg_pool2d(image_features.permute(0, 3, 1, 2), 
                                      padding=0,
                                      kernel_size=grid_stride, 
                                      stride=grid_stride)
        image_features = image_features.permute(0, 2, 3, 1).view(B, -1, D)
        return torch.cat((cls_features, image_features), dim=1)


##########################################################################


def generate_prompt(instruction, input=""):
    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
    if input:
        return f"""Instruction: {instruction}

Input: {input}

Response:"""
    else:
        return f"""User: hi

Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.

User: {instruction}

Assistant:"""

def evaluate(
    ctx,
    token_count=200,
    temperature=1.0,
    top_p=0.7,
    presencePenalty = 0.1,
    countPenalty = 0.1,
):
    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                     alpha_frequency = countPenalty,
                     alpha_presence = presencePenalty,
                     token_ban = [], # ban the generation of some tokens
                     token_stop = [0]) # stop generation whenever you see any token here
    ctx = ctx.strip()
    all_tokens = []
    out_last = 0
    out_str = ''
    occurrence = {}
    state = None
    for i in range(int(token_count)):
        out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        if token in args.token_stop:
            break
        all_tokens += [token]
        for xxx in occurrence:
            occurrence[xxx] *= 0.996        
        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1
        
        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            out_str += tmp
            yield out_str.strip()
            out_last = i + 1

    del out
    del state
    gc.collect()
    yield out_str.strip()

import gradio as gr
import os, gc
from datetime import datetime
from huggingface_hub import hf_hub_download

ctx_limit = 3500
title = "rwkv1b5-vitl336p14-577token_mix665k_rwkv"

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster)

from rwkv.model import RWKV
model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=f"{title}.pth")
model = RWKV(model=model_path, strategy='cpu fp32')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "rwkv_vocab_v20230424")

##########################################################################
from model import VisualEncoder, EmbeddingMixer, VisualEncoderConfig
emb_mixer = EmbeddingMixer(model.w["emb.weight"], num_image_embeddings=4096)
config = VisualEncoderConfig(n_embd=model.args.n_embd, 
                             vision_tower_name='openai/clip-vit-large-patch14-336', 
                             grid_size=-1)
visual_encoder = VisualEncoder(config)
##########################################################################
def generate_prompt(instruction, input=""):
    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
    if input:
        return f"""Instruction: {instruction}

Input: {input}

Response:"""
    else:
        return f"""User: hi

Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.

User: {instruction}

Assistant:"""

def evaluate(
    ctx,
    token_count=200,
    temperature=1.0,
    top_p=0.7,
    presencePenalty = 0.1,
    countPenalty = 0.1,
):
    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                     alpha_frequency = countPenalty,
                     alpha_presence = presencePenalty,
                     token_ban = [], # ban the generation of some tokens
                     token_stop = [0]) # stop generation whenever you see any token here
    ctx = ctx.strip()
    all_tokens = []
    out_last = 0
    out_str = ''
    occurrence = {}
    state = None
    for i in range(int(token_count)):
        out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        if token in args.token_stop:
            break
        all_tokens += [token]
        for xxx in occurrence:
            occurrence[xxx] *= 0.996        
        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1
        
        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            out_str += tmp
            yield out_str.strip()
            out_last = i + 1

    del out
    del state
    gc.collect()
    yield out_str.strip()


##########################################################################
examples = [
    [
        "./extreme_ironing.jpg",
        "What is unusual about this image?",
    ],
    [
        "./waterview.jpg",
        "What are the things I should be cautious about when I visit here?",
    ]
]
def test(image, question):
    return question
demo = gr.Interface(fn=test, 
                    inputs=["image", "text"], 
                    outputs="text", 
                    examples=examples, 
                    title=title, 
                    description="VisualRWKV-v5.0")

demo.queue(concurrency_count=1, max_size=10)
demo.launch(share=False)