# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# coding=utf-8
# Copyright 2021 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.
"""
Processor class for LayoutLMv2.
"""

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class LayoutLMv2Processor(ProcessorMixin):
    r"""
    Constructs a LayoutLMv2 processor which combines a LayoutLMv2 image processor and a LayoutLMv2 tokenizer into a
    single processor.

    [`LayoutLMv2Processor`] offers all the functionalities you need to prepare data for the model.

    It first uses [`LayoutLMv2ImageProcessor`] to resize document images to a fixed size, and optionally applies OCR to
    get words and normalized bounding boxes. These are then provided to [`LayoutLMv2Tokenizer`] or
    [`LayoutLMv2TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
    `attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
    into token-level `labels` for token classification tasks (such as FUNSD, CORD).

    Args:
        image_processor (`LayoutLMv2ImageProcessor`):
            An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input.
        tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`):
            An instance of [`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]. The tokenizer is a required input.
    """
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "LayoutLMv2ImageProcessor"
    tokenizer_class = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")

    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,
        images,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> BatchEncoding:
        """
        This method first forwards the `images` argument to [`~LayoutLMv2ImageProcessor.__call__`]. In case
        [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
        bounding boxes along with the additional arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output,
        together with resized `images`. In case [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to
        `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
        arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output, together with resized `images``.

        Please refer to the docstring of the above two methods for more information.
        """
        # verify input
        if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
            )

        if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
            )

        if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")

        # first, apply the image processor
        features = self.image_processor(images=images, return_tensors=return_tensors)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(text, str):
                text = [text]  # add batch dimension (as the image processor always adds a batch dimension)
            text_pair = features["words"]

        encoded_inputs = self.tokenizer(
            text=text if text is not None else features["words"],
            text_pair=text_pair if text_pair is not None else None,
            boxes=boxes if boxes is not None else features["boxes"],
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            return_tensors=return_tensors,
            **kwargs,
        )

        # add pixel values
        images = features.pop("pixel_values")
        if return_overflowing_tokens is True:
            images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
        encoded_inputs["image"] = images

        return encoded_inputs

    def get_overflowing_images(self, images, overflow_to_sample_mapping):
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        images_with_overflow = []
        for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx])

        if len(images_with_overflow) != len(overflow_to_sample_mapping):
            raise ValueError(
                "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
            )

        return images_with_overflow

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer'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 PreTrainedTokenizer'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):
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]

    @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 2021 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.
"""
Processor class for LayoutLMv2.
"""

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class LayoutLMv2Processor(ProcessorMixin):
    r"""
    Constructs a LayoutLMv2 processor which combines a LayoutLMv2 image processor and a LayoutLMv2 tokenizer into a
    single processor.

    [`LayoutLMv2Processor`] offers all the functionalities you need to prepare data for the model.

    It first uses [`LayoutLMv2ImageProcessor`] to resize document images to a fixed size, and optionally applies OCR to
    get words and normalized bounding boxes. These are then provided to [`LayoutLMv2Tokenizer`] or
    [`LayoutLMv2TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
    `attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
    into token-level `labels` for token classification tasks (such as FUNSD, CORD).

    Args:
        image_processor (`LayoutLMv2ImageProcessor`):
            An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input.
        tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`):
            An instance of [`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]. The tokenizer is a required input.
    """
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "LayoutLMv2ImageProcessor"
    tokenizer_class = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")

    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,
        images,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> BatchEncoding:
        """
        This method first forwards the `images` argument to [`~LayoutLMv2ImageProcessor.__call__`]. In case
        [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
        bounding boxes along with the additional arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output,
        together with resized `images`. In case [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to
        `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
        arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output, together with resized `images``.

        Please refer to the docstring of the above two methods for more information.
        """
        # verify input
        if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
            )

        if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
            )

        if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")

        # first, apply the image processor
        features = self.image_processor(images=images, return_tensors=return_tensors)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(text, str):
                text = [text]  # add batch dimension (as the image processor always adds a batch dimension)
            text_pair = features["words"]

        encoded_inputs = self.tokenizer(
            text=text if text is not None else features["words"],
            text_pair=text_pair if text_pair is not None else None,
            boxes=boxes if boxes is not None else features["boxes"],
            word_labels=word_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            return_tensors=return_tensors,
            **kwargs,
        )

        # add pixel values
        images = features.pop("pixel_values")
        if return_overflowing_tokens is True:
            images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
        encoded_inputs["image"] = images

        return encoded_inputs

    def get_overflowing_images(self, images, overflow_to_sample_mapping):
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        images_with_overflow = []
        for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx])

        if len(images_with_overflow) != len(overflow_to_sample_mapping):
            raise ValueError(
                "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
            )

        return images_with_overflow

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to PreTrainedTokenizer'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 PreTrainedTokenizer'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):
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]

    @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
