# coding=utf-8
# Copyright 2024 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
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"""
Processor class for MiniCPMV.
"""

from typing import List, Optional, Union
import torch
import re

from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from transformers.utils import TensorType

from .image_processing_minicpmv import MiniCPMVBatchFeature


class MiniCPMVProcessor(ProcessorMixin):
    r"""
    Constructs a MiniCPMV processor which wraps a MiniCPMV image processor and a MiniCPMV tokenizer into a single processor.

    [`MiniCPMVProcessor`] offers all the functionalities of [`MiniCPMVImageProcessor`] and [`LlamaTokenizerWrapper`]. See the
    [`~MiniCPMVProcessor.__call__`] and [`~MiniCPMVProcessor.decode`] for more information.

    Args:
        image_processor ([`MiniCPMVImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`LlamaTokenizerWrapper`], *optional*):
            The tokenizer is a required input.
    """
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "AutoImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(self, image_processor=None, tokenizer=None):
        super().__init__(image_processor, tokenizer)
    
    def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
        images: ImageInput = None,
        padding: Union[bool, str, PaddingStrategy] = False,
        max_length: Optional[int] = None,
        do_pad: Optional[bool] = True,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
    ) -> MiniCPMVBatchFeature:
        if images is not None:
            image_inputs = self.image_processor(images, do_pad=do_pad, return_tensors=return_tensors)
        return self._convert_images_texts_to_inputs(image_inputs, text, max_length=max_length, return_tensors=return_tensors)
    
    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        output_ids = args[0]
        result_text = []
        for result in output_ids:
            result = result[result != 0]
            if result[0] == self.tokenizer.bos_id:
                result = result[1:]
            if result[-1] == self.tokenizer.eos_id:
                result = result[:-1]
            result_text.append(self.tokenizer.decode(result, *args[1:], **kwargs).strip())
        return result_text
        # return self.tokenizer.batch_decode(*args, **kwargs)
    
    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        result = args[0]
        result = result[result != 0]
        if result[0] == self.tokenizer.bos_id:
            result = result[1:]
        if result[-1] == self.tokenizer.eos_id:
            result = result[:-1]
        return self.tokenizer.decode(result, *args[1:], **kwargs).strip()

    def _convert(
        self, input_str, max_inp_length: Optional[int] = None
    ):
        if self.tokenizer.add_bos_token:
            input_ids = self.tokenizer.encode(input_str)
        else:
            input_ids = [self.tokenizer.bos_id] + self.tokenizer.encode(input_str)
        if max_inp_length is not None:
            input_ids = input_ids[:max_inp_length]
        input_ids = torch.tensor(input_ids, dtype=torch.int32)

        image_start_tokens = torch.where(input_ids == self.tokenizer.im_start_id)[0]
        image_start_tokens += 1
        image_end_tokens = torch.where(input_ids == self.tokenizer.im_end_id)[0]
        valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
        image_bounds = torch.hstack(
            [
                image_start_tokens[:valid_image_nums].unsqueeze(-1),
                image_end_tokens[:valid_image_nums].unsqueeze(-1),
            ]
        )
        return input_ids.unsqueeze(0), image_bounds

    def _convert_images_texts_to_inputs(self, images, texts, do_pad=False, truncation=None, max_length=None, return_tensors=None):
        if not len(images):
            model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=do_pad, truncation=truncation, max_length=max_length)
            return MiniCPMVBatchFeature(data={**model_inputs})
        
        pattern = "(<image>./</image>)"
        images, image_sizes = images["pixel_values"], images["image_sizes"]

        image_tags = re.findall(pattern, texts)
        assert len(image_tags) == len(image_sizes[0])
        text_chunks = texts.split(pattern)
        final_texts = ""
        for i in range(len(image_tags)):
            final_texts = final_texts + text_chunks[i] + self.image_processor.get_slice_image_placeholder(image_sizes[0][i])
        final_texts += text_chunks[-1]
        input_ids, image_bounds = self._convert(final_texts, max_length)
        
        return MiniCPMVBatchFeature(data={
            "input_ids": input_ids,
            "pixel_values": images,
            "image_sizes": image_sizes,
            "image_bounds": [image_bounds]
        }, tensor_type=return_tensors)

    @property
    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
    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))