# What does this PR do?
### _Automatically converts input pixels to the correct type when running image_to_text pipelines_

---

Currently, when using image_to_text pipelines with half precision, I encounter an error in the forward function when passing in the pixel data. I found that casting to the target_dtype within the forward function fixes this issue in my case.

This is my first time working with the transformers library, so I'm not sure if this is the "correct" place to fix this kind of issue, or if perhaps there's another step in the pipeline code that I've missed that should be responsible for casting the input data to the correct type.

I assume there are other similar models that may benefit from the same type of fix. However, I've constrained my fix to the models that I've been working with already, as I didn't want to contribute unvalidated code. I'm happy to make similar changes to further models if required, or if someone with more experience with this library wants to rework these changes and approach the fix differently, I'd be happy with that, and can test with the subset of models I've been using if required.

- _Possibly_ Fixes #24834

_(This issue looked similar to the issue I was encountering, however I had different additional issues using 8bit optimizations, so I've only tested my fix under float16. But I think given that issue seems to relate to the same root cause of missing casting for input data within pipelines, I think there's a good chance it may be fixed by this issue.)_

PR Checklist Notes
- I haven't been able to run tests as I ran into dependency issues on my local environment, and the CI workflows appear to use self hosted runners, which I assume won't be easy for me to set up. Given the small scope of my changes, perhaps someone can approve running my PR against CircleCI if necessary?
- I haven't added new tests for this change, I had a read through of the existing tests and I wasn't sure if this sort of low level change would usually warrant explicit test coverage. Since I don't currently have a good way of running the tests myself, I figured it was best to omit new tests for now.

Tagging @younesbelkada and @NielsRogge as the last authors of the lines I edited.
fix: cast input pixels to appropriate dtype for image_to_text pipelines. # What does this PR do?
### _Automatically converts input pixels to the correct type when running image_to_text pipelines_

---

Currently, when using image_to_text pipelines with half precision, I encounter an error in the forward function when passing in the pixel data. I found that casting to the target_dtype within the forward function fixes this issue in my case.

This is my first time working with the transformers library, so I'm not sure if this is the "correct" place to fix this kind of issue, or if perhaps there's another step in the pipeline code that I've missed that should be responsible for casting the input data to the correct type.

I assume there are other similar models that may benefit from the same type of fix. However, I've constrained my fix to the models that I've been working with already, as I didn't want to contribute unvalidated code. I'm happy to make similar changes to further models if required, or if someone with more experience with this library wants to rework these changes and approach the fix differently, I'd be happy with that, and can test with the subset of models I've been using if required.

- _Possibly_ Fixes #24834

_(This issue looked similar to the issue I was encountering, however I had different additional issues using 8bit optimizations, so I've only tested my fix under float16. But I think given that issue seems to relate to the same root cause of missing casting for input data within pipelines, I think there's a good chance it may be fixed by this issue.)_

PR Checklist Notes
- I haven't been able to run tests as I ran into dependency issues on my local environment, and the CI workflows appear to use self hosted runners, which I assume won't be easy for me to set up. Given the small scope of my changes, perhaps someone can approve running my PR against CircleCI if necessary?
- I haven't added new tests for this change, I had a read through of the existing tests and I wasn't sure if this sort of low level change would usually warrant explicit test coverage. Since I don't currently have a good way of running the tests myself, I figured it was best to omit new tests for now.

Tagging @younesbelkada and @NielsRogge as the last authors of the lines I edited.
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.

import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


logger = logging.getLogger(__name__)


@dataclass(frozen=True)
class InputExample:
    """
    A single training/test example for simple sequence classification.

    Args:
        guid: Unique id for the example.
        text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
        text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
        label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        pairID: (Optional) string. Unique identifier for the pair of sentences.
    """

    guid: str
    text_a: str
    text_b: Optional[str] = None
    label: Optional[str] = None
    pairID: Optional[str] = None


@dataclass(frozen=True)
class InputFeatures:
    """
    A single set of features of data.
    Property names are the same names as the corresponding inputs to a model.

    Args:
        input_ids: Indices of input sequence tokens in the vocabulary.
        attention_mask: Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
        token_type_ids: (Optional) Segment token indices to indicate first and second
            portions of the inputs. Only some models use them.
        label: (Optional) Label corresponding to the input. Int for classification problems,
            float for regression problems.
        pairID: (Optional) Unique identifier for the pair of sentences.
    """

    input_ids: List[int]
    attention_mask: Optional[List[int]] = None
    token_type_ids: Optional[List[int]] = None
    label: Optional[Union[int, float]] = None
    pairID: Optional[int] = None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset

    class HansDataset(Dataset):
        """
        This will be superseded by a framework-agnostic approach
        soon.
        """

        features: List[InputFeatures]

        def __init__(
            self,
            data_dir: str,
            tokenizer: PreTrainedTokenizer,
            task: str,
            max_seq_length: Optional[int] = None,
            overwrite_cache=False,
            evaluate: bool = False,
        ):
            processor = hans_processors[task]()

            cached_features_file = os.path.join(
                data_dir,
                "cached_{}_{}_{}_{}".format(
                    "dev" if evaluate else "train",
                    tokenizer.__class__.__name__,
                    str(max_seq_length),
                    task,
                ),
            )
            label_list = processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                label_list[1], label_list[2] = label_list[2], label_list[1]
            self.label_list = label_list

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            lock_path = cached_features_file + ".lock"
            with FileLock(lock_path):
                if os.path.exists(cached_features_file) and not overwrite_cache:
                    logger.info(f"Loading features from cached file {cached_features_file}")
                    self.features = torch.load(cached_features_file)
                else:
                    logger.info(f"Creating features from dataset file at {data_dir}")

                    examples = (
                        processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
                    )

                    logger.info("Training examples: %s", len(examples))
                    self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
                    logger.info("Saving features into cached file %s", cached_features_file)
                    torch.save(self.features, cached_features_file)

        def __len__(self):
            return len(self.features)

        def __getitem__(self, i) -> InputFeatures:
            return self.features[i]

        def get_labels(self):
            return self.label_list


if is_tf_available():
    import tensorflow as tf

    class TFHansDataset:
        """
        This will be superseded by a framework-agnostic approach
        soon.
        """

        features: List[InputFeatures]

        def __init__(
            self,
            data_dir: str,
            tokenizer: PreTrainedTokenizer,
            task: str,
            max_seq_length: Optional[int] = 128,
            overwrite_cache=False,
            evaluate: bool = False,
        ):
            processor = hans_processors[task]()
            label_list = processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                label_list[1], label_list[2] = label_list[2], label_list[1]
            self.label_list = label_list

            examples = processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
            self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)

            def gen():
                for ex_index, ex in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
                    if ex_index % 10000 == 0:
                        logger.info("Writing example %d of %d" % (ex_index, len(examples)))

                    yield (
                        {
                            "example_id": 0,
                            "input_ids": ex.input_ids,
                            "attention_mask": ex.attention_mask,
                            "token_type_ids": ex.token_type_ids,
                        },
                        ex.label,
                    )

            self.dataset = tf.data.Dataset.from_generator(
                gen,
                (
                    {
                        "example_id": tf.int32,
                        "input_ids": tf.int32,
                        "attention_mask": tf.int32,
                        "token_type_ids": tf.int32,
                    },
                    tf.int64,
                ),
                (
                    {
                        "example_id": tf.TensorShape([]),
                        "input_ids": tf.TensorShape([None, None]),
                        "attention_mask": tf.TensorShape([None, None]),
                        "token_type_ids": tf.TensorShape([None, None]),
                    },
                    tf.TensorShape([]),
                ),
            )

        def get_dataset(self):
            return self.dataset

        def __len__(self):
            return len(self.features)

        def __getitem__(self, i) -> InputFeatures:
            return self.features[i]

        def get_labels(self):
            return self.label_list


class HansProcessor(DataProcessor):
    """Processor for the HANS data set."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_evaluation_set.txt")), "dev")

    def get_labels(self):
        """See base class.
        Note that we follow the standard three labels for MNLI
        (see :class:`~transformers.data.processors.utils.MnliProcessor`)
        but the HANS evaluation groups `contradiction` and `neutral` into `non-entailment` (label 0) while
        `entailment` is label 1."""
        return ["contradiction", "entailment", "neutral"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for i, line in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[5]
            text_b = line[6]
            pairID = line[7][2:] if line[7].startswith("ex") else line[7]
            label = line[0]
            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, pairID=pairID))
        return examples


def hans_convert_examples_to_features(
    examples: List[InputExample],
    label_list: List[str],
    max_length: int,
    tokenizer: PreTrainedTokenizer,
):
    """
    Loads a data file into a list of ``InputFeatures``

    Args:
        examples: List of ``InputExamples`` containing the examples.
        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method.
        max_length: Maximum example length.
        tokenizer: Instance of a tokenizer that will tokenize the examples.

    Returns:
        A list of task-specific ``InputFeatures`` which can be fed to the model.

    """

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for ex_index, example in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d" % (ex_index))

        inputs = tokenizer(
            example.text_a,
            example.text_b,
            add_special_tokens=True,
            max_length=max_length,
            padding="max_length",
            truncation=True,
            return_overflowing_tokens=True,
        )

        label = label_map[example.label] if example.label in label_map else 0

        pairID = int(example.pairID)

        features.append(InputFeatures(**inputs, label=label, pairID=pairID))

    for i, example in enumerate(examples[:5]):
        logger.info("*** Example ***")
        logger.info(f"guid: {example}")
        logger.info(f"features: {features[i]}")

    return features


hans_tasks_num_labels = {
    "hans": 3,
}

hans_processors = {
    "hans": HansProcessor,
}

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.

import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


logger = logging.getLogger(__name__)


@dataclass(frozen=True)
class InputExample:
    """
    A single training/test example for simple sequence classification.

    Args:
        guid: Unique id for the example.
        text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
        text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
        label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        pairID: (Optional) string. Unique identifier for the pair of sentences.
    """

    guid: str
    text_a: str
    text_b: Optional[str] = None
    label: Optional[str] = None
    pairID: Optional[str] = None


@dataclass(frozen=True)
class InputFeatures:
    """
    A single set of features of data.
    Property names are the same names as the corresponding inputs to a model.

    Args:
        input_ids: Indices of input sequence tokens in the vocabulary.
        attention_mask: Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
        token_type_ids: (Optional) Segment token indices to indicate first and second
            portions of the inputs. Only some models use them.
        label: (Optional) Label corresponding to the input. Int for classification problems,
            float for regression problems.
        pairID: (Optional) Unique identifier for the pair of sentences.
    """

    input_ids: List[int]
    attention_mask: Optional[List[int]] = None
    token_type_ids: Optional[List[int]] = None
    label: Optional[Union[int, float]] = None
    pairID: Optional[int] = None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset

    class HansDataset(Dataset):
        """
        This will be superseded by a framework-agnostic approach
        soon.
        """

        features: List[InputFeatures]

        def __init__(
            self,
            data_dir: str,
            tokenizer: PreTrainedTokenizer,
            task: str,
            max_seq_length: Optional[int] = None,
            overwrite_cache=False,
            evaluate: bool = False,
        ):
            processor = hans_processors[task]()

            cached_features_file = os.path.join(
                data_dir,
                "cached_{}_{}_{}_{}".format(
                    "dev" if evaluate else "train",
                    tokenizer.__class__.__name__,
                    str(max_seq_length),
                    task,
                ),
            )
            label_list = processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                label_list[1], label_list[2] = label_list[2], label_list[1]
            self.label_list = label_list

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            lock_path = cached_features_file + ".lock"
            with FileLock(lock_path):
                if os.path.exists(cached_features_file) and not overwrite_cache:
                    logger.info(f"Loading features from cached file {cached_features_file}")
                    self.features = torch.load(cached_features_file)
                else:
                    logger.info(f"Creating features from dataset file at {data_dir}")

                    examples = (
                        processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
                    )

                    logger.info("Training examples: %s", len(examples))
                    self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)
                    logger.info("Saving features into cached file %s", cached_features_file)
                    torch.save(self.features, cached_features_file)

        def __len__(self):
            return len(self.features)

        def __getitem__(self, i) -> InputFeatures:
            return self.features[i]

        def get_labels(self):
            return self.label_list


if is_tf_available():
    import tensorflow as tf

    class TFHansDataset:
        """
        This will be superseded by a framework-agnostic approach
        soon.
        """

        features: List[InputFeatures]

        def __init__(
            self,
            data_dir: str,
            tokenizer: PreTrainedTokenizer,
            task: str,
            max_seq_length: Optional[int] = 128,
            overwrite_cache=False,
            evaluate: bool = False,
        ):
            processor = hans_processors[task]()
            label_list = processor.get_labels()
            if tokenizer.__class__ in (
                RobertaTokenizer,
                RobertaTokenizerFast,
                XLMRobertaTokenizer,
                BartTokenizer,
                BartTokenizerFast,
            ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                label_list[1], label_list[2] = label_list[2], label_list[1]
            self.label_list = label_list

            examples = processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
            self.features = hans_convert_examples_to_features(examples, label_list, max_seq_length, tokenizer)

            def gen():
                for ex_index, ex in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
                    if ex_index % 10000 == 0:
                        logger.info("Writing example %d of %d" % (ex_index, len(examples)))

                    yield (
                        {
                            "example_id": 0,
                            "input_ids": ex.input_ids,
                            "attention_mask": ex.attention_mask,
                            "token_type_ids": ex.token_type_ids,
                        },
                        ex.label,
                    )

            self.dataset = tf.data.Dataset.from_generator(
                gen,
                (
                    {
                        "example_id": tf.int32,
                        "input_ids": tf.int32,
                        "attention_mask": tf.int32,
                        "token_type_ids": tf.int32,
                    },
                    tf.int64,
                ),
                (
                    {
                        "example_id": tf.TensorShape([]),
                        "input_ids": tf.TensorShape([None, None]),
                        "attention_mask": tf.TensorShape([None, None]),
                        "token_type_ids": tf.TensorShape([None, None]),
                    },
                    tf.TensorShape([]),
                ),
            )

        def get_dataset(self):
            return self.dataset

        def __len__(self):
            return len(self.features)

        def __getitem__(self, i) -> InputFeatures:
            return self.features[i]

        def get_labels(self):
            return self.label_list


class HansProcessor(DataProcessor):
    """Processor for the HANS data set."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_evaluation_set.txt")), "dev")

    def get_labels(self):
        """See base class.
        Note that we follow the standard three labels for MNLI
        (see :class:`~transformers.data.processors.utils.MnliProcessor`)
        but the HANS evaluation groups `contradiction` and `neutral` into `non-entailment` (label 0) while
        `entailment` is label 1."""
        return ["contradiction", "entailment", "neutral"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for i, line in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[5]
            text_b = line[6]
            pairID = line[7][2:] if line[7].startswith("ex") else line[7]
            label = line[0]
            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, pairID=pairID))
        return examples


def hans_convert_examples_to_features(
    examples: List[InputExample],
    label_list: List[str],
    max_length: int,
    tokenizer: PreTrainedTokenizer,
):
    """
    Loads a data file into a list of ``InputFeatures``

    Args:
        examples: List of ``InputExamples`` containing the examples.
        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method.
        max_length: Maximum example length.
        tokenizer: Instance of a tokenizer that will tokenize the examples.

    Returns:
        A list of task-specific ``InputFeatures`` which can be fed to the model.

    """

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for ex_index, example in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d" % (ex_index))

        inputs = tokenizer(
            example.text_a,
            example.text_b,
            add_special_tokens=True,
            max_length=max_length,
            padding="max_length",
            truncation=True,
            return_overflowing_tokens=True,
        )

        label = label_map[example.label] if example.label in label_map else 0

        pairID = int(example.pairID)

        features.append(InputFeatures(**inputs, label=label, pairID=pairID))

    for i, example in enumerate(examples[:5]):
        logger.info("*** Example ***")
        logger.info(f"guid: {example}")
        logger.info(f"features: {features[i]}")

    return features


hans_tasks_num_labels = {
    "hans": 3,
}

hans_processors = {
    "hans": HansProcessor,
}
