# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Llava.
"""


from typing import List, Optional, Union

from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.tokenization_utils_base import (
    PaddingStrategy,
    PreTokenizedInput,
    TextInput,
    TruncationStrategy,
)
from transformers.utils import TensorType
import torch
from open_clip.transform import PreprocessCfg, image_transform_v2
from modeling_llava import LlavaForConditionalGeneration
from PIL import Image
import math


class OpenCLIPImageProcessor:
    def __init__(self, config, crop_size=384, max_tokens=100):
        cfg = PreprocessCfg(**config)
        transform = image_transform_v2(cfg=cfg, is_train=False)
        self.transform = transform
        self.crop_size = crop_size
        self.max_tokens = max_tokens

    def __call__(self, image: Image.Image):
        output = self.transform_func(image)
        return {
            "pixel_values": output,
        }

    def transform_func(self, image: Image.Image):
        outputs = []
        outputs.append(self.transform(image))
        width, height = image.size
        crop_size = self.crop_size
        if width <= crop_size and height <= crop_size:
            outputs = torch.stack(outputs, dim=0)
            return outputs
        total_tokens = math.inf
        while total_tokens > self.max_tokens:
            total_tokens = math.floor(
                (2 * width - crop_size)
                / crop_size
                * (2 * height - crop_size)
                / crop_size
            )
            if total_tokens > self.max_tokens:
                crop_size += 10
        stride = crop_size // 2
        x_steps = int(round((2 * width - crop_size) / crop_size))
        if x_steps < 1:
            x_steps = 1
        y_steps = int(round((2 * height - crop_size) / crop_size))
        if y_steps < 1:
            y_steps = 1
        x_coords = []
        y_coords = []
        for i in range(x_steps):
            x_coords.append([i * stride, i * stride + crop_size])
        if x_coords[-1][1] != width:
            x_coords[-1][1] = width
        for i in range(y_steps):
            y_coords.append([i * stride, i * stride + crop_size])
        if y_coords[-1][1] != height:
            y_coords[-1][1] = height
        image_parts = []
        for i in range(len(x_coords)):
            for j in range(len(y_coords)):
                image_parts.append(
                    image.crop(
                        (x_coords[i][0], y_coords[j][0], x_coords[i][1], y_coords[j][1])
                    )
                )
        for image_part in image_parts:
            outputs.append(self.transform(image_part))
        outputs = torch.stack(outputs, dim=0)
        return outputs

    @property
    def model_input_names(self):
        return ["pixel_values"]


class LlavaProcessor:
    def __init__(self, image_processor: OpenCLIPImageProcessor, tokenizer):
        self.image_processor = image_processor
        self.tokenizer = tokenizer

    def __call__(
        self,
        text: Union[
            TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
        ] = None,
        images: ImageInput = None,
        model: LlavaForConditionalGeneration = None,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length=None,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
    ) -> BatchFeature:
        if images is not None:
            pixel_values = self.image_processor(images)[
                "pixel_values"
            ]
            pixel_values = pixel_values.to(model.device).to(model.dtype)
            image_outputs = model.vision_model(pixel_values)
            image_features = model.multi_modal_projector(image_outputs)
            image_features = image_features.unsqueeze(0)
        else:
            image_features = None
        text_inputs = self.tokenizer(
            text,
            return_tensors=return_tensors,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
        )

        return BatchFeature(data={**text_inputs, "image_features": image_features})

    def batch_decode(self, *args, **kwargs):
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        return self.tokenizer.decode(*args, **kwargs)

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))