# What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
Deprecate models. # What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
# coding=utf-8
# Copyright 2022 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 CLIPSeg
"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class CLIPSegProcessor(ProcessorMixin):
    r"""
    Constructs a CLIPSeg processor which wraps a CLIPSeg image processor and a CLIP tokenizer into a single processor.

    [`CLIPSegProcessor`] offers all the functionalities of [`ViTImageProcessor`] and [`CLIPTokenizerFast`]. See the
    [`~CLIPSegProcessor.__call__`] and [`~CLIPSegProcessor.decode`] for more information.

    Args:
        image_processor ([`ViTImageProcessor`]):
            The image processor is a required input.
        tokenizer ([`CLIPTokenizerFast`]):
            The tokenizer is a required input.
    """
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "ViTImageProcessor"
    tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")

    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
        feature_extractor = None
        if "feature_extractor" in kwargs:
            warnings.warn(
                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                " instead.",
                FutureWarning,
            )
            feature_extractor = kwargs.pop("feature_extractor")

        image_processor = image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("You need to specify an `image_processor`.")
        if tokenizer is None:
            raise ValueError("You need to specify a `tokenizer`.")

        super().__init__(image_processor, tokenizer)

    def __call__(self, text=None, images=None, visual_prompt=None, return_tensors=None, **kwargs):
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
        ViTImageProcessor's [`~ViTImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring of
        the above two methods for more information.

        Args:
            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).
            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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                number of channels, H and W are image height and width.
            visual_prompt (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The visual prompt image or batch of images to be prepared. Each visual prompt image can be a PIL image,
                NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape
                (C, H, W), where C is a number of channels, H and W are image height and width.

            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.

        Returns:
            [`BatchEncoding`]: A [`BatchEncoding`] 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`.
        """
        if text is None and visual_prompt is None and images is None:
            raise ValueError("You have to specify either text, visual prompt or images.")

        if text is not None and visual_prompt is not None:
            raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

        if text is not None:
            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)

        if visual_prompt is not None:
            prompt_features = self.image_processor(visual_prompt, return_tensors=return_tensors, **kwargs)

        if images is not None:
            image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)

        if visual_prompt is not None and images is not None:
            encoding = {
                "pixel_values": image_features.pixel_values,
                "conditional_pixel_values": prompt_features.pixel_values,
            }
            return encoding
        elif text is not None and images is not None:
            encoding["pixel_values"] = image_features.pixel_values
            return encoding
        elif text is not None:
            return encoding
        elif visual_prompt is not None:
            encoding = {
                "conditional_pixel_values": prompt_features.pixel_values,
            }
            return encoding
        else:
            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)

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

    @property
    def feature_extractor_class(self):
        warnings.warn(
            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
            FutureWarning,
        )
        return self.image_processor_class

    @property
    def feature_extractor(self):
        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
            FutureWarning,
        )
        return self.image_processor

# coding=utf-8
# Copyright 2022 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 CLIPSeg
"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class CLIPSegProcessor(ProcessorMixin):
    r"""
    Constructs a CLIPSeg processor which wraps a CLIPSeg image processor and a CLIP tokenizer into a single processor.

    [`CLIPSegProcessor`] offers all the functionalities of [`ViTImageProcessor`] and [`CLIPTokenizerFast`]. See the
    [`~CLIPSegProcessor.__call__`] and [`~CLIPSegProcessor.decode`] for more information.

    Args:
        image_processor ([`ViTImageProcessor`]):
            The image processor is a required input.
        tokenizer ([`CLIPTokenizerFast`]):
            The tokenizer is a required input.
    """
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "ViTImageProcessor"
    tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")

    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
        feature_extractor = None
        if "feature_extractor" in kwargs:
            warnings.warn(
                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                " instead.",
                FutureWarning,
            )
            feature_extractor = kwargs.pop("feature_extractor")

        image_processor = image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("You need to specify an `image_processor`.")
        if tokenizer is None:
            raise ValueError("You need to specify a `tokenizer`.")

        super().__init__(image_processor, tokenizer)

    def __call__(self, text=None, images=None, visual_prompt=None, return_tensors=None, **kwargs):
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
        ViTImageProcessor's [`~ViTImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring of
        the above two methods for more information.

        Args:
            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).
            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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                number of channels, H and W are image height and width.
            visual_prompt (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The visual prompt image or batch of images to be prepared. Each visual prompt image can be a PIL image,
                NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape
                (C, H, W), where C is a number of channels, H and W are image height and width.

            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.

        Returns:
            [`BatchEncoding`]: A [`BatchEncoding`] 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`.
        """
        if text is None and visual_prompt is None and images is None:
            raise ValueError("You have to specify either text, visual prompt or images.")

        if text is not None and visual_prompt is not None:
            raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

        if text is not None:
            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)

        if visual_prompt is not None:
            prompt_features = self.image_processor(visual_prompt, return_tensors=return_tensors, **kwargs)

        if images is not None:
            image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)

        if visual_prompt is not None and images is not None:
            encoding = {
                "pixel_values": image_features.pixel_values,
                "conditional_pixel_values": prompt_features.pixel_values,
            }
            return encoding
        elif text is not None and images is not None:
            encoding["pixel_values"] = image_features.pixel_values
            return encoding
        elif text is not None:
            return encoding
        elif visual_prompt is not None:
            encoding = {
                "conditional_pixel_values": prompt_features.pixel_values,
            }
            return encoding
        else:
            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)

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

    @property
    def feature_extractor_class(self):
        warnings.warn(
            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
            FutureWarning,
        )
        return self.image_processor_class

    @property
    def feature_extractor(self):
        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
            FutureWarning,
        )
        return self.image_processor
