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#           This file was automatically generated from src/transformers/models/deepseek_vl/modular_deepseek_vl.py.
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#                          modular_deepseek_vl.py file directly. One of our CI enforces this.
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# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
#
# 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.


from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import auto_docstring


class DeepseekVLProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {"padding": False},
        "common_kwargs": {"return_tensors": "pt"},
    }


@auto_docstring
class DeepseekVLProcessor(ProcessorMixin):
    def __init__(
        self,
        image_processor,
        tokenizer,
        chat_template=None,
        num_image_tokens=576,
    ):
        r"""
        num_image_tokens (`int`, *optional*, defaults to 576):
            The number of special image tokens used as placeholders for visual content in text sequences.
        """
        self.image_token = tokenizer.image_token
        self.num_image_tokens = num_image_tokens

        super().__init__(image_processor, tokenizer, chat_template=chat_template)

    @auto_docstring
    def __call__(
        self,
        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
        images: ImageInput | None = None,
        **kwargs: Unpack[DeepseekVLProcessorKwargs],
    ) -> BatchFeature:
        r"""
        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`.
        """
        output_kwargs = self._merge_kwargs(
            DeepseekVLProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs
        )
        if text is None and images is None:
            raise ValueError("You must specify either text or images.")

        if text is not None:
            if isinstance(text, str):
                text = [text]
            elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
                raise ValueError("Invalid input text. Please provide a string, or a list of strings")

        prompt_strings = []
        one_img_tokens = self.image_token * self.num_image_tokens
        for prompt in text:
            prompt = prompt.replace(self.image_token, one_img_tokens)
            prompt_strings.append(prompt)

        data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])

        # process images if pixel_values are provided
        if images is not None:
            data["pixel_values"] = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]

        return BatchFeature(data=data)

    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)

    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)

    @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))


__all__ = ["DeepseekVLProcessor"]