# 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.
"""
Image/Text processor class for GIT
"""

import re
from typing import Optional, Union

import numpy as np

from ...image_utils import ImageInput
from ...processing_utils import (
    MultiModalData,
    ProcessingKwargs,
    ProcessorMixin,
    Unpack,
)
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import is_torch_available, logging, requires_backends
from ...utils.import_utils import requires


if is_torch_available():
    from .image_processing_fuyu import FuyuBatchFeature


logger = logging.get_logger(__name__)


if is_torch_available():
    import torch


TEXT_REPR_BBOX_OPEN = "<box>"
TEXT_REPR_BBOX_CLOSE = "</box>"
TEXT_REPR_POINT_OPEN = "<point>"
TEXT_REPR_POINT_CLOSE = "</point>"

TOKEN_BBOX_OPEN_STRING = "<0x00>"  # <bbox>
TOKEN_BBOX_CLOSE_STRING = "<0x01>"  # </bbox>
TOKEN_POINT_OPEN_STRING = "<0x02>"  # <point>
TOKEN_POINT_CLOSE_STRING = "<0x03>"  # </point>
BEGINNING_OF_ANSWER_STRING = "<0x04>"  # <boa>


class FuyuProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "add_special_tokens": True,
            "padding": False,
            "stride": 0,
            "return_attention_mask": True,
            "return_overflowing_tokens": False,
            "return_special_tokens_mask": False,
            "return_offsets_mapping": False,
            "return_token_type_ids": False,
            "return_length": False,
            "verbose": True,
            "return_mm_token_type_ids": False,
        },
    }


def full_unpacked_stream_to_tensor(
    all_bi_tokens_to_place: list[int],
    full_unpacked_stream: list["torch.Tensor"],
    fill_value: int,
    batch_size: int,
    new_seq_len: int,
    offset: int,
) -> "torch.Tensor":
    """Takes an unpacked stream of tokens (i.e. a list of tensors, one for each item in the batch) and does
    the required padding to create a single tensor for the batch of shape batch_size x new_seq_len.
    """

    assert len(all_bi_tokens_to_place) == batch_size
    assert len(full_unpacked_stream) == batch_size

    # Create padded tensors for the full batch.
    new_padded_tensor = torch.full(
        [batch_size, new_seq_len],
        fill_value=fill_value,
        dtype=full_unpacked_stream[0].dtype,
        device=full_unpacked_stream[0].device,
    )

    # Place each batch entry into the batch tensor.
    for bi in range(batch_size):
        tokens_to_place = all_bi_tokens_to_place[bi]
        new_padded_tensor[bi, :tokens_to_place] = full_unpacked_stream[bi][offset : tokens_to_place + offset]

    return new_padded_tensor


def construct_full_unpacked_stream(
    num_real_text_tokens: Union[list[list[int]], "torch.Tensor"],
    input_stream: "torch.Tensor",
    image_tokens: list[list["torch.Tensor"]],
    batch_size: int,
    num_sub_sequences: int,
) -> list["torch.Tensor"]:
    """Takes an input_stream tensor of shape B x S x ?. For each subsequence, adds any required
    padding to account for images and then unpacks the subsequences to create a single sequence per item in the batch.
    Returns a list of tensors, one for each item in the batch."""

    all_bi_stream = []

    for batch_index in range(batch_size):
        all_si_stream = []

        # First, construct full token stream (including image placeholder tokens) and loss mask for each subsequence
        # and append to lists. We use lists rather than tensors because each subsequence is variable-sized.
        # TODO Remove this logic in a subsequent release since subsequences are not supported.
        image_adjustment = image_tokens[batch_index][0]
        subsequence_stream = torch.cat([image_adjustment, input_stream[batch_index, 0]], dim=0)
        num_real_tokens = image_adjustment.shape[0] + num_real_text_tokens[batch_index][0]
        all_si_stream.append(subsequence_stream[:num_real_tokens])
        all_bi_stream.append(torch.cat(all_si_stream, dim=0))

    return all_bi_stream


def _replace_string_repr_with_token_tags(prompt: str) -> str:
    prompt = prompt.replace(TEXT_REPR_POINT_OPEN, TOKEN_POINT_OPEN_STRING)
    prompt = prompt.replace(TEXT_REPR_POINT_CLOSE, TOKEN_POINT_CLOSE_STRING)
    prompt = prompt.replace(TEXT_REPR_BBOX_OPEN, TOKEN_BBOX_OPEN_STRING)
    prompt = prompt.replace(TEXT_REPR_BBOX_CLOSE, TOKEN_BBOX_CLOSE_STRING)
    return prompt


def _segment_prompt_into_text_token_conversions(prompt: str) -> list:
    """
    Given a string prompt, converts the prompt into a list of TextTokenConversions.
    """
    # Wherever, we notice the [TOKEN_OPEN_STRING, TOKEN_CLOSE_STRING], we split the prompt
    prompt_text_list: list = []
    regex_pattern = re.compile(
        f"({TOKEN_BBOX_OPEN_STRING}|{TOKEN_BBOX_CLOSE_STRING}|{TOKEN_POINT_OPEN_STRING}|{TOKEN_POINT_CLOSE_STRING})"
    )
    # Split by the regex pattern
    prompt_split = regex_pattern.split(prompt)
    for i, elem in enumerate(prompt_split):
        if len(elem) == 0 or elem in [
            TOKEN_BBOX_OPEN_STRING,
            TOKEN_BBOX_CLOSE_STRING,
            TOKEN_POINT_OPEN_STRING,
            TOKEN_POINT_CLOSE_STRING,
        ]:
            continue
        prompt_text_list.append(
            (elem, i > 1 and prompt_split[i - 1] in [TOKEN_BBOX_OPEN_STRING, TOKEN_POINT_OPEN_STRING])
        )
    return prompt_text_list


def _transform_coordinates_and_tokenize(prompt: str, scale_factor: float, tokenizer) -> list[int]:
    """
    This function transforms the prompt in the following fashion:
    - <box> <point> and </box> </point> to their respective token mappings
    - extract the coordinates from the tag
    - transform the coordinates into the transformed image space
    - return the prompt tokens with the transformed coordinates and new tags

    Bounding boxes and points MUST be in the following format: <box>y1, x1, y2, x2</box> <point>x, y</point> The spaces
    and punctuation added above are NOT optional.
    """
    # Make a namedtuple that stores "text" and "is_bbox"

    # We want to do the following: Tokenize the code normally -> when we see a point or box, tokenize using the tokenize_within_tag function
    # When point or box close tag, continue tokenizing normally
    # First, we replace the point and box tags with their respective tokens
    prompt = _replace_string_repr_with_token_tags(prompt)
    # Tokenize the prompt
    # Convert prompt into a list split
    prompt_text_list = _segment_prompt_into_text_token_conversions(prompt)
    transformed_prompt_tokens: list[int] = []
    for elem in prompt_text_list:
        if elem[1]:
            # This is a location, we need to tokenize it
            within_tag_tokenized = _transform_within_tags(elem[0], scale_factor, tokenizer)
            # Surround the text with the open and close tags
            transformed_prompt_tokens.extend(within_tag_tokenized)
        else:
            transformed_prompt_tokens.extend(tokenizer(elem[0], add_special_tokens=False).input_ids)
    return transformed_prompt_tokens


def _transform_within_tags(text: str, scale_factor: float, tokenizer) -> list[int]:
    """
    Given a bounding box of the fashion <box>1, 2, 3, 4</box> | <point>1, 2</point> This function is responsible for
    converting 1, 2, 3, 4 into tokens of 1 2 3 4 without any commas.
    """
    # Convert the text into a list of strings.
    num_int_strs = text.split(",")
    if len(num_int_strs) == 2:
        # If there are any open or close tags, remove them.
        token_space_open_string = tokenizer.vocab[TOKEN_POINT_OPEN_STRING]
        token_space_close_string = tokenizer.vocab[TOKEN_POINT_CLOSE_STRING]
    else:
        token_space_open_string = tokenizer.vocab[TOKEN_BBOX_OPEN_STRING]
        token_space_close_string = tokenizer.vocab[TOKEN_BBOX_CLOSE_STRING]

    # Remove all spaces from num_ints
    num_ints = [float(num.strip()) for num in num_int_strs]
    # scale to transformed image size
    if len(num_ints) == 2:
        num_ints_translated = scale_point_to_transformed_image(x=num_ints[0], y=num_ints[1], scale_factor=scale_factor)
    elif len(num_ints) == 4:
        num_ints_translated = scale_bbox_to_transformed_image(
            top=num_ints[0],
            left=num_ints[1],
            bottom=num_ints[2],
            right=num_ints[3],
            scale_factor=scale_factor,
        )
    else:
        raise ValueError(f"Invalid number of ints: {len(num_ints)}")
    # Tokenize the text, skipping the
    tokens = [tokenizer.vocab[str(num)] for num in num_ints_translated]
    return [token_space_open_string] + tokens + [token_space_close_string]


def _tokenize_prompts_with_image_and_batch(
    tokenizer,
    prompts: list[list[str]],
    scale_factors: Optional[list[list["torch.Tensor"]]],
    max_tokens_to_generate: int,
    max_position_embeddings: int,
    add_BOS: bool,  # Same issue with types as above
    add_beginning_of_answer_token: bool,
) -> tuple["torch.Tensor", "torch.Tensor"]:
    """
    Given a set of prompts and number of tokens to generate:
    - tokenize prompts
    - set the sequence length to be the max of length of prompts plus the number of tokens we would like to generate
    - pad all the sequences to this length so we can convert them into a 3D tensor.
    """

    # If not tool use, transform the coordinates while tokenizing
    if scale_factors is not None:
        transformed_prompt_tokens = []
        for prompt_seq, scale_factor_seq in zip(prompts, scale_factors):
            transformed_prompt_tokens.append(
                [
                    _transform_coordinates_and_tokenize(prompt, scale_factor.item(), tokenizer)
                    for prompt, scale_factor in zip(prompt_seq, scale_factor_seq)
                ]
            )
    else:
        transformed_prompt_tokens = [[tokenizer.tokenize(prompt) for prompt in prompt_seq] for prompt_seq in prompts]

    prompts_tokens = transformed_prompt_tokens

    if add_BOS:
        bos_token = tokenizer.vocab["<s>"]
    else:
        bos_token = tokenizer.vocab["|ENDOFTEXT|"]
    prompts_tokens = [[[bos_token] + x for x in prompt_seq] for prompt_seq in prompts_tokens]
    if add_beginning_of_answer_token:
        beginning_of_answer = tokenizer.vocab[BEGINNING_OF_ANSWER_STRING]
        # Only add bbox open token to the last subsequence since that is what will be completed
        for token_seq in prompts_tokens:
            token_seq[-1].append(beginning_of_answer)

    # Now we have a list of list of tokens which each list has a different
    # size. We want to extend this list to:
    #   - incorporate the tokens that need to be generated
    #   - make all the sequences equal length.
    # Get the prompts length.

    prompts_length = [[len(x) for x in prompts_tokens_seq] for prompts_tokens_seq in prompts_tokens]
    # Get the max prompts length.
    max_prompt_len: int = np.max(prompts_length)
    # Number of tokens in the each sample of the batch.
    samples_length = min(max_prompt_len + max_tokens_to_generate, max_position_embeddings)
    if max_prompt_len + max_tokens_to_generate > max_position_embeddings:
        logger.warning(
            f"Max subsequence prompt length of {max_prompt_len} + max tokens to generate {max_tokens_to_generate}",
            f"exceeds context length of {max_position_embeddings}. Will generate as many tokens as possible.",
        )
    # Now update the list of list to be of the same size: samples_length.
    for prompt_tokens_seq, prompts_length_seq in zip(prompts_tokens, prompts_length):
        for prompt_tokens, prompt_length in zip(prompt_tokens_seq, prompts_length_seq):
            if len(prompt_tokens) > samples_length:
                raise ValueError("Length of subsequence prompt exceeds sequence length.")
            padding_size = samples_length - prompt_length
            prompt_tokens.extend([tokenizer.vocab["|ENDOFTEXT|"]] * padding_size)

    # Now we are in a structured format, we can convert to tensors.
    prompts_tokens_tensor = torch.tensor(prompts_tokens, dtype=torch.int64)
    prompts_length_tensor = torch.tensor(prompts_length, dtype=torch.int64)

    return prompts_tokens_tensor, prompts_length_tensor


# Simplified assuming self.crop_top = self.padding_top = 0
def original_to_transformed_h_coords(original_coords, scale_h):
    return np.round(original_coords * scale_h).astype(np.int32)


# Simplified assuming self.crop_left = self.padding_left = 0
def original_to_transformed_w_coords(original_coords, scale_w):
    return np.round(original_coords * scale_w).astype(np.int32)


def scale_point_to_transformed_image(x: float, y: float, scale_factor: float) -> list[int]:
    x_scaled = original_to_transformed_w_coords(np.array([x / 2]), scale_factor)[0]
    y_scaled = original_to_transformed_h_coords(np.array([y / 2]), scale_factor)[0]
    return [x_scaled, y_scaled]


def scale_bbox_to_transformed_image(
    top: float, left: float, bottom: float, right: float, scale_factor: float
) -> list[int]:
    top_scaled = original_to_transformed_w_coords(np.array([top / 2]), scale_factor)[0]
    left_scaled = original_to_transformed_h_coords(np.array([left / 2]), scale_factor)[0]
    bottom_scaled = original_to_transformed_w_coords(np.array([bottom / 2]), scale_factor)[0]
    right_scaled = original_to_transformed_h_coords(np.array([right / 2]), scale_factor)[0]
    return [top_scaled, left_scaled, bottom_scaled, right_scaled]


@requires(backends=("vision",))
class FuyuProcessor(ProcessorMixin):
    r"""
    Constructs a Fuyu processor which wraps a Fuyu image processor and a Llama tokenizer into a single processor.

    [`FuyuProcessor`] offers all the functionalities of [`FuyuImageProcessor`] and [`LlamaTokenizerFast`]. See the
    [`~FuyuProcessor.__call__`] and [`~FuyuProcessor.decode`] for more information.

    Args:
        image_processor ([`FuyuImageProcessor`]):
            The image processor is a required input.
        tokenizer ([`LlamaTokenizerFast`]):
            The tokenizer is a required input.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "FuyuImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(self, image_processor, tokenizer, **kwargs):
        super().__init__(image_processor=image_processor, tokenizer=tokenizer)
        self.image_processor = image_processor
        self.tokenizer = tokenizer
        self.max_tokens_to_generate = 10
        self.max_position_embeddings = 16384  # TODO Can't derive this from model files: where to set it?
        self.pad_token_id = 0
        self.dummy_image_index = -1
        self.image_token_id = tokenizer.encode("|SPEAKER|", add_special_tokens=False)[1]
        self.image_newline_id = tokenizer.encode("|NEWLINE|", add_special_tokens=False)[1]

    def _left_pad_inputs_with_attention_mask(self, model_inputs: list[dict], return_attention_mask: bool):
        max_length_input_ids = max(entry["input_ids"].shape[1] for entry in model_inputs)
        max_length_image_patch_indices = max(entry["image_patches_indices"].shape[1] for entry in model_inputs)

        batched_inputs = {"input_ids": [], "image_patches": [], "image_patches_indices": [], "attention_mask": []}

        for entry in model_inputs:
            for key, tensor in entry.items():
                if key == "input_ids":
                    num_padding_tokens = max_length_input_ids - tensor.shape[1]
                    padded_input_ids = torch.cat(
                        [
                            torch.full((tensor.shape[0], num_padding_tokens), self.pad_token_id, dtype=torch.long),
                            tensor,
                        ],
                        dim=1,
                    )
                    batched_inputs[key].append(padded_input_ids)

                    attention_mask = torch.cat(
                        [torch.zeros(tensor.shape[0], num_padding_tokens, dtype=torch.long), torch.ones_like(tensor)],
                        dim=1,
                    )
                    batched_inputs["attention_mask"].append(attention_mask)

                elif key == "image_patches":
                    # For image_patches, we don't pad but just append them to the list.
                    batched_inputs[key].append(tensor)

                else:  # for image_patches_indices
                    num_padding_indices = max_length_image_patch_indices - tensor.shape[1]
                    padded_indices = torch.cat(
                        [
                            torch.full(
                                (tensor.shape[0], num_padding_indices), self.dummy_image_index, dtype=torch.long
                            ),
                            tensor,
                        ],
                        dim=1,
                    )
                    batched_inputs[key].append(padded_indices)
        batched_keys = ["input_ids", "image_patches_indices"]
        if return_attention_mask:
            batched_keys.append("attention_mask")
        for key in batched_keys:
            batched_inputs[key] = torch.cat(batched_inputs[key], dim=0)

        # Cast images to tensor as well, if only one image passed and no padding needed
        # NOTE: vLLM expects all processor outputs to be a tensor
        if len(batched_inputs["image_patches"]) == 1:
            batched_inputs["image_patches"] = torch.cat(batched_inputs["image_patches"], dim=0)

        return batched_inputs

    def get_sample_encoding(
        self,
        prompts,
        scale_factors,
        image_unpadded_heights,
        image_unpadded_widths,
        image_placeholder_id,
        image_newline_id,
        tensor_batch_images,
    ):
        image_present = torch.ones(1, 1, 1)
        model_image_input = self.image_processor.preprocess_with_tokenizer_info(
            image_input=tensor_batch_images,
            image_present=image_present,
            image_unpadded_h=image_unpadded_heights,
            image_unpadded_w=image_unpadded_widths,
            image_placeholder_id=image_placeholder_id,
            image_newline_id=image_newline_id,
            variable_sized=True,
        )
        # FIXME max_tokens_to_generate is embedded into this processor's call.
        prompt_tokens, prompts_length = _tokenize_prompts_with_image_and_batch(
            tokenizer=self.tokenizer,
            prompts=prompts,
            scale_factors=scale_factors,
            max_tokens_to_generate=self.max_tokens_to_generate,
            max_position_embeddings=self.max_position_embeddings,
            add_BOS=True,
            add_beginning_of_answer_token=True,
        )
        image_padded_unpacked_tokens = construct_full_unpacked_stream(
            num_real_text_tokens=prompts_length,
            input_stream=prompt_tokens,
            image_tokens=model_image_input["image_input_ids"],
            batch_size=1,
            num_sub_sequences=self.subsequence_length,
        )
        # Construct inputs for image patch indices.
        unpacked_image_patch_indices_per_batch = construct_full_unpacked_stream(
            num_real_text_tokens=prompts_length,
            input_stream=torch.full_like(prompt_tokens, -1),
            image_tokens=model_image_input["image_patch_indices_per_batch"],
            batch_size=1,
            num_sub_sequences=self.subsequence_length,
        )
        max_prompt_length = max(x.shape[-1] for x in image_padded_unpacked_tokens)
        max_seq_len_batch = min(max_prompt_length + self.max_tokens_to_generate, self.max_position_embeddings)
        tokens_to_place = min(max_seq_len_batch, max(0, image_padded_unpacked_tokens[0].shape[0]))

        # Use same packing logic for the image patch indices.
        image_patch_input_indices = full_unpacked_stream_to_tensor(
            all_bi_tokens_to_place=[tokens_to_place],
            full_unpacked_stream=unpacked_image_patch_indices_per_batch,
            fill_value=-1,
            batch_size=1,
            new_seq_len=max_seq_len_batch,
            offset=0,
        )
        image_patches_tensor = torch.stack([img[0] for img in model_image_input["image_patches"]])
        batch_encoding = {
            "input_ids": image_padded_unpacked_tokens[0].unsqueeze(0),
            "image_patches": image_patches_tensor,
            "image_patches_indices": image_patch_input_indices,
        }
        return batch_encoding

    def __call__(
        self,
        images: Optional[ImageInput] = None,
        text: Optional[Union[str, list[str], TextInput, PreTokenizedInput]] = None,
        **kwargs: Unpack[FuyuProcessorKwargs],
    ) -> "FuyuBatchFeature":
        """
        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 `kwargs` arguments to
        FuyuImageProcessor's [`~FuyuImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
        of the above two methods for more information.

        Args:
            images (`PIL.Image.Image`, `list[PIL.Image.Image]`):
                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]`):
                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).

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

            - **input_ids** -- Tensor of token ids to be fed to a model. Returned when `text` is not `None`.
            - **image_patches** -- List of Tensor of image patches. Returned when `images` is not `None`.
            - **image_patches_indices** -- Tensor of indices where patch embeddings have to be inserted by the model.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model when
              `return_attention_mask=True`.
        """
        requires_backends(self, ["torch"])

        # --- Check input validity ---
        if text is None and images is None:
            raise ValueError("You have to specify either text or images. Both cannot be None.")

        output_kwargs = self._merge_kwargs(
            FuyuProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)

        if not output_kwargs["text_kwargs"].setdefault("return_attention_mask", True):
            raise ValueError("`return_attention_mask=False` is not supported for this model.")

        if text is not None and images is None:
            logger.warning("You are processing a text with no associated image. Make sure it is intended.")
            text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
            return text_encoding

        if text is None and images is not None:
            logger.warning("You are processing an image with no associated text. Make sure it is intended.")
            prompts = [[""]]
        if text is not None and images is not None:
            if isinstance(text, str):
                prompts = [[text]]
            elif isinstance(text, list):
                prompts = [[text_seq] for text_seq in text]

        # --- Preprocess images using self.image_processor ---

        # FIXME - We hard code "pt" here because the rest of the processing assumes torch tensors
        output_kwargs["images_kwargs"]["return_tensors"] = "pt"
        image_encoding = self.image_processor.preprocess(images, **output_kwargs["images_kwargs"])
        batch_images = image_encoding["images"]
        image_unpadded_heights = image_encoding["image_unpadded_heights"]
        image_unpadded_widths = image_encoding["image_unpadded_widths"]
        scale_factors = image_encoding["image_scale_factors"]
        self.subsequence_length = 1  # Each batch contains only one sequence.
        self.batch_size = len(batch_images)

        # --- Use self.tokenizer to get the ids of special tokens to insert into image ids ---

        tensor_batch_images = torch.stack([img[0] for img in batch_images if img]).unsqueeze(1)

        # --- Use self.image_processor again to obtain the full token ids and batch inputs ---
        all_encodings = []

        for prompt, scale_factor, image_unpadded_height, image_unpadded_width, tensor_batch_image in zip(
            prompts, scale_factors, image_unpadded_heights, image_unpadded_widths, tensor_batch_images
        ):
            sample_encoding = self.get_sample_encoding(
                prompts=[prompt],
                scale_factors=[scale_factor],
                image_unpadded_heights=torch.tensor([image_unpadded_height]),
                image_unpadded_widths=torch.tensor([image_unpadded_width]),
                image_placeholder_id=self.image_token_id,
                image_newline_id=self.image_newline_id,
                tensor_batch_images=tensor_batch_image.unsqueeze(0),
            )
            all_encodings.append(sample_encoding)

        batch_encoding = self._left_pad_inputs_with_attention_mask(
            model_inputs=all_encodings, return_attention_mask=True
        )
        if return_mm_token_type_ids:
            input_ids = batch_encoding["input_ids"]
            mm_token_type_ids = torch.zeros_like(input_ids)
            mm_token_type_ids[input_ids == self.image_token_id] = 1
            mm_token_type_ids[input_ids == self.image_newline_id] = 1
            batch_encoding["mm_token_type_ids"] = mm_token_type_ids

        return FuyuBatchFeature(data=batch_encoding)

    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
        """
        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.

        Args:
            image_sizes (`list[list[int]]`, *optional*):
                The input sizes formatted as (height, width) per each image.

        Returns:
            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
            input modalities, along with other useful data.
        """

        vision_data = {}
        if image_sizes is not None:
            size = kwargs.get("size") or self.image_processor.size
            padded_height, padded_width = size["height"], size["width"]

            num_image_tokens = []
            num_image_patches = [1] * len(image_sizes)
            for image_size in image_sizes:
                height_scale_factor = padded_height / image_size[0]
                width_scale_factor = padded_width / image_size[1]
                optimal_scale_factor = min(height_scale_factor, width_scale_factor)

                image_unpadded_h = min(int(image_size[0] * optimal_scale_factor), image_size[0])
                image_unpadded_w = min(int(image_size[0] * optimal_scale_factor), image_size[0])

                # We can use torch here because Fuyu processor has hard dependency on torch. NOTE: Fuyu can't do multi-image
                # thus the below (1, 1, 1) is hardcoded. Same as when calling the processor
                model_image_input = self.image_processor.preprocess_with_tokenizer_info(
                    image_input=torch.zeros(1, 1, 3, padded_height, padded_width),
                    image_present=torch.ones(1, 1, 1),
                    image_unpadded_h=torch.tensor([[image_unpadded_h]]),
                    image_unpadded_w=torch.tensor([[image_unpadded_w]]),
                    image_placeholder_id=0,  # dummy ids, we can be sure `id=0` is never out-of-range
                    image_newline_id=0,
                    variable_sized=True,
                )
                num_image_tokens.append(model_image_input["image_input_ids"][0][0].shape[-1])
            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
        return MultiModalData(**vision_data)

    def post_process_box_coordinates(self, outputs, target_sizes=None):
        """
        Transforms raw coordinates detected by [`FuyuForCausalLM`] to the original images' coordinate space.
        Coordinates will be returned in "box" format, with the following pattern:
            `<box>top, left, bottom, right</box>`

        Point coordinates are not supported yet.

        Args:
            outputs ([`GenerateOutput`]):
                Raw outputs from `generate`.
            target_sizes (`torch.Tensor`, *optional*):
                Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
                the batch. If set, found coordinates in the output sequence are rescaled to the target sizes. If left
                to None, coordinates will not be rescaled.

        Returns:
            `GenerateOutput`: Same output type returned by `generate`, with output token ids replaced with
                boxed and possible rescaled coordinates.
        """

        def scale_factor_to_fit(original_size, target_size=None):
            height, width = original_size
            if target_size is None:
                max_height = self.image_processor.size["height"]
                max_width = self.image_processor.size["width"]
            else:
                max_height, max_width = target_size
            if width <= max_width and height <= max_height:
                return 1.0
            return min(max_height / height, max_width / width)

        def find_delimiters_pair(tokens, start_token, end_token):
            start_id = self.tokenizer.convert_tokens_to_ids(start_token)
            end_id = self.tokenizer.convert_tokens_to_ids(end_token)

            starting_positions = (tokens == start_id).nonzero(as_tuple=True)[0]
            ending_positions = (tokens == end_id).nonzero(as_tuple=True)[0]

            if torch.any(starting_positions) and torch.any(ending_positions):
                return (starting_positions[0], ending_positions[0])
            return (None, None)

        def tokens_to_boxes(tokens, original_size):
            while (pair := find_delimiters_pair(tokens, TOKEN_BBOX_OPEN_STRING, TOKEN_BBOX_CLOSE_STRING)) != (
                None,
                None,
            ):
                start, end = pair
                if end != start + 5:
                    continue

                # Retrieve transformed coordinates from tokens
                coords = self.tokenizer.convert_ids_to_tokens(tokens[start + 1 : end])

                # Scale back to original image size and multiply by 2
                scale = scale_factor_to_fit(original_size)
                top, left, bottom, right = [2 * int(float(c) / scale) for c in coords]

                # Replace the IDs so they get detokenized right
                replacement = f" {TEXT_REPR_BBOX_OPEN}{top}, {left}, {bottom}, {right}{TEXT_REPR_BBOX_CLOSE}"
                replacement = self.tokenizer.tokenize(replacement)[1:]
                replacement = self.tokenizer.convert_tokens_to_ids(replacement)
                replacement = torch.tensor(replacement).to(tokens)

                tokens = torch.cat([tokens[:start], replacement, tokens[end + 1 :]], 0)
            return tokens

        def tokens_to_points(tokens, original_size):
            while (pair := find_delimiters_pair(tokens, TOKEN_POINT_OPEN_STRING, TOKEN_POINT_CLOSE_STRING)) != (
                None,
                None,
            ):
                start, end = pair
                if end != start + 3:
                    continue

                # Retrieve transformed coordinates from tokens
                coords = self.tokenizer.convert_ids_to_tokens(tokens[start + 1 : end])

                # Scale back to original image size and multiply by 2
                scale = scale_factor_to_fit(original_size)
                x, y = [2 * int(float(c) / scale) for c in coords]

                # Replace the IDs so they get detokenized right
                replacement = f" {TEXT_REPR_POINT_OPEN}{x}, {y}{TEXT_REPR_POINT_CLOSE}"
                replacement = self.tokenizer.tokenize(replacement)[1:]
                replacement = self.tokenizer.convert_tokens_to_ids(replacement)
                replacement = torch.tensor(replacement).to(tokens)

                tokens = torch.cat([tokens[:start], replacement, tokens[end + 1 :]], 0)
            return tokens

        if target_sizes is None:
            target_sizes = ((self.image_processor.size["height"], self.image_processor.size["width"]),) * len(outputs)
        elif target_sizes.shape[1] != 2:
            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

        if len(outputs) != len(target_sizes):
            raise ValueError("Make sure that you pass in as many target sizes as output sequences")

        results = []
        for seq, size in zip(outputs, target_sizes):
            seq = tokens_to_boxes(seq, size)
            seq = tokens_to_points(seq, size)
            results.append(seq)

        return results

    def post_process_image_text_to_text(self, generated_outputs, skip_special_tokens=True, **kwargs):
        """
        Post-processes the output of `FuyuForConditionalGeneration` to only return the text output.

        Args:
            generated_outputs (`torch.Tensor` or `np.ndarray`):
                The output of the model. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
                containing the token ids of the generated sequences.
            skip_special_tokens (`bool`, *optional*, defaults to `True`):
                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
            **kwargs:
                Additional arguments to be passed to the tokenizer's `batch_decode method`.

        Returns:
            `list[str]`: The decoded text output.
        """
        beginning_of_answer = self.tokenizer.convert_tokens_to_ids(BEGINNING_OF_ANSWER_STRING)
        # get boa index for each outputted sequence tensor
        # start all generated sequences from the beginning of the answer token, pad to have consistent length
        unpadded_output_sequences = [
            seq[(seq == beginning_of_answer).nonzero(as_tuple=True)[0] + 1 :] for seq in generated_outputs
        ]
        max_len = max(len(seq) for seq in unpadded_output_sequences)
        # convert to torch and pad sequences
        padded_output_sequences = torch.full((len(unpadded_output_sequences), max_len), self.pad_token_id)
        for i, seq in enumerate(unpadded_output_sequences):
            padded_output_sequences[i, : len(seq)] = torch.tensor(seq)

        return self.batch_decode(padded_output_sequences, skip_special_tokens=skip_special_tokens, **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

        # Make a copy of list when removing otherwise `self.image_processor.model_input_names` is also modified
        extra_image_inputs = [
            "image_input_ids",
            "image_patch_indices_per_subsequence",
            "images",
            "image_patch_indices_per_batch",
        ]
        image_processor_input_names = [name for name in image_processor_input_names if name not in extra_image_inputs]
        return list(tokenizer_input_names + image_processor_input_names + ["image_patches_indices"])


__all__ = ["FuyuProcessor"]
