# 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 Aero.
"""

import math
import os
from typing import List, Optional, Union

import numpy as np
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.video_utils import VideoInput
from transformers.models.auto import AutoFeatureExtractor
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import logging

logger = logging.get_logger(__name__)


class AeroProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "padding": False,
        },
        "audio_kwargs": {
        },
    }


class AeroProcessor(ProcessorMixin):
    attributes = ["tokenizer", "audio_processor"]
    valid_kwargs = [
        "chat_template",
        "audio_token",
    ]
    tokenizer_class = "AutoTokenizer"
    audio_processor_class = "AutoFeatureExtractor"

    def __init__(
        self,
        tokenizer=None,
        audio_processor=None,
        chat_template=None,
        audio_token="<|AUDIO|>",
        **kwargs,
    ):
        self.audio_token = (
            tokenizer.audio_token if hasattr(tokenizer, "audio_token") else audio_token
        )
        if chat_template is None:
            chat_template = self.default_chat_template
        super().__init__(
            tokenizer,
            audio_processor,
            chat_template=chat_template,
        )

    def __call__(
        self,
        text: Union[
            TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
        ] = None,
        audios: Union[np.ndarray, List[np.ndarray]] = None,
        videos: VideoInput = None,
        images: ImageInput = None,
        sampling_rate: Optional[int] = None,
        **kwargs: Unpack[AeroProcessorKwargs],
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
        LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
        of the above two methods for more information.

        Args:
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. Both channels-first and channels-last formats are supported.
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch

        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:

            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
            - **pixel_values_videos** -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
            - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
        """

        output_kwargs = self._merge_kwargs(
            AeroProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        # Make sure no duplicate padding
        if "padding" in output_kwargs["audio_kwargs"]:
            output_kwargs["audio_kwargs"].pop("padding")

        if isinstance(text, str):
            text = [text]
        elif not isinstance(text, list) and not isinstance(text[0], str):
            raise ValueError(
                "Invalid input text. Please provide a string, or a list of strings"
            )

        audio_inputs = {}

        if audios is not None:
            audio_inputs = self.audio_processor(
                audios,
                sampling_rate=sampling_rate,
                return_attention_mask=True,
                padding="max_length",
                **output_kwargs["audio_kwargs"],
            )
            audio_inputs["audio_attention_mask"] = audio_inputs.pop(
                "attention_mask"
            )  # rename attention_mask to prevent conflicts later on
            audio_inputs["audio_values"] = audio_inputs.pop(
                "input_features"
            )  # rename input_features to audio_features for clarification
            # Computes the output length of the convolutional layers and the output length of the audio encoder
            input_lengths = (audio_inputs["audio_attention_mask"].sum(-1) - 1) // 2 + 1
            num_audio_tokens = (input_lengths - 2) // 2 + 1
            text = self.expand_audio_tokens(text, num_audio_tokens, self.audio_token)

        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
        return BatchFeature(data={**text_inputs, **audio_inputs})

    def expand_audio_tokens(
        self,
        text: List[TextInput],
        num_audio_tokens: List[int],
        special_token: str,
    ):
        prompt_strings = []
        current_audio_idx = 0
        for sample in text:
            while special_token in sample:
                num_audio_token = num_audio_tokens[current_audio_idx]
                sample = sample.replace(
                    special_token, "<placeholder>" * num_audio_token, 1
                )
                current_audio_idx += 1
            prompt_strings.append(sample)
        text = [
            sample.replace("<placeholder>", special_token) for sample in prompt_strings
        ]
        return text

    # 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.
        """
        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.
        """
        return self.tokenizer.decode(*args, **kwargs)

    def batch_encode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_encode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_encode(*args, **kwargs)

    def encode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.encode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.encode(*args, **kwargs)

    @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))

    @property
    def default_chat_template(self):
        # fmt: off
        return (
            "{% set audio_count = namespace(value=0) %}"
            "{% for message in messages %}"
                "{% if loop.first and message['role'] != 'system' %}"
                    "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
                "{% endif %}"
                "<|im_start|>{{ message['role'] }}\n"
                "{% if message['content'] is string %}"
                    "{{ message['content'] }}<|im_end|>\n"
                "{% else %}"
                    "{% for content in message['content'] %}"
                        "{% if 'audio' in content or 'audio_url' in content or content['type'] == 'audio'%}"
                            "{% set audio_count.value = audio_count.value + 1 %}"
                            "<|AUDIO|>\n"
                        "{% elif 'text' in content %}"
                            "{{ content['text'] }}"
                        "{% endif %}"
                    "{% endfor %}"
                    "<|im_end|>\n"
                "{% endif %}"
            "{% endfor %}"
            "{% if add_generation_prompt %}"
                "<|im_start|>assistant\n"
            "{% endif %}"
        )
        # fmt: on