# 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 csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


logger = logging.get_logger(__name__)


@dataclass
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.
    """

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

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(dataclasses.asdict(self), indent=2) + "\n"


@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.
    """

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

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(dataclasses.asdict(self)) + "\n"


class DataProcessor:
    """Base class for data converters for sequence classification data sets."""

    def get_example_from_tensor_dict(self, tensor_dict):
        """
        Gets an example from a dict with tensorflow tensors.

        Args:
            tensor_dict: Keys and values should match the corresponding Glue
                tensorflow_dataset examples.
        """
        raise NotImplementedError()

    def get_train_examples(self, data_dir):
        """Gets a collection of [`InputExample`] for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of [`InputExample`] for the dev set."""
        raise NotImplementedError()

    def get_test_examples(self, data_dir):
        """Gets a collection of [`InputExample`] for the test set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    def tfds_map(self, example):
        """
        Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
        examples to the correct format.
        """
        if len(self.get_labels()) > 1:
            example.label = self.get_labels()[int(example.label)]
        return example

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8-sig") as f:
            return list(csv.reader(f, delimiter="\t", quotechar=quotechar))


class SingleSentenceClassificationProcessor(DataProcessor):
    """Generic processor for a single sentence classification data set."""

    def __init__(self, labels=None, examples=None, mode="classification", verbose=False):
        self.labels = [] if labels is None else labels
        self.examples = [] if examples is None else examples
        self.mode = mode
        self.verbose = verbose

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

    def __getitem__(self, idx):
        if isinstance(idx, slice):
            return SingleSentenceClassificationProcessor(labels=self.labels, examples=self.examples[idx])
        return self.examples[idx]

    @classmethod
    def create_from_csv(
        cls, file_name, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, **kwargs
    ):
        processor = cls(**kwargs)
        processor.add_examples_from_csv(
            file_name,
            split_name=split_name,
            column_label=column_label,
            column_text=column_text,
            column_id=column_id,
            skip_first_row=skip_first_row,
            overwrite_labels=True,
            overwrite_examples=True,
        )
        return processor

    @classmethod
    def create_from_examples(cls, texts_or_text_and_labels, labels=None, **kwargs):
        processor = cls(**kwargs)
        processor.add_examples(texts_or_text_and_labels, labels=labels)
        return processor

    def add_examples_from_csv(
        self,
        file_name,
        split_name="",
        column_label=0,
        column_text=1,
        column_id=None,
        skip_first_row=False,
        overwrite_labels=False,
        overwrite_examples=False,
    ):
        lines = self._read_tsv(file_name)
        if skip_first_row:
            lines = lines[1:]
        texts = []
        labels = []
        ids = []
        for i, line in enumerate(lines):
            texts.append(line[column_text])
            labels.append(line[column_label])
            if column_id is not None:
                ids.append(line[column_id])
            else:
                guid = f"{split_name}-{i}" if split_name else str(i)
                ids.append(guid)

        return self.add_examples(
            texts, labels, ids, overwrite_labels=overwrite_labels, overwrite_examples=overwrite_examples
        )

    def add_examples(
        self, texts_or_text_and_labels, labels=None, ids=None, overwrite_labels=False, overwrite_examples=False
    ):
        if labels is not None and len(texts_or_text_and_labels) != len(labels):
            raise ValueError(
                f"Text and labels have mismatched lengths {len(texts_or_text_and_labels)} and {len(labels)}"
            )
        if ids is not None and len(texts_or_text_and_labels) != len(ids):
            raise ValueError(f"Text and ids have mismatched lengths {len(texts_or_text_and_labels)} and {len(ids)}")
        if ids is None:
            ids = [None] * len(texts_or_text_and_labels)
        if labels is None:
            labels = [None] * len(texts_or_text_and_labels)
        examples = []
        added_labels = set()
        for text_or_text_and_label, label, guid in zip(texts_or_text_and_labels, labels, ids):
            if isinstance(text_or_text_and_label, (tuple, list)) and label is None:
                text, label = text_or_text_and_label
            else:
                text = text_or_text_and_label
            added_labels.add(label)
            examples.append(InputExample(guid=guid, text_a=text, text_b=None, label=label))

        # Update examples
        if overwrite_examples:
            self.examples = examples
        else:
            self.examples.extend(examples)

        # Update labels
        if overwrite_labels:
            self.labels = list(added_labels)
        else:
            self.labels = list(set(self.labels).union(added_labels))

        return self.examples

    def get_features(
        self,
        tokenizer,
        max_length=None,
        pad_on_left=False,
        pad_token=0,
        mask_padding_with_zero=True,
        return_tensors=None,
    ):
        """
        Convert examples in a list of `InputFeatures`

        Args:
            tokenizer: Instance of a tokenizer that will tokenize the examples
            max_length: Maximum example length
            pad_on_left: If set to `True`, the examples will be padded on the left rather than on the right (default)
            pad_token: Padding token
            mask_padding_with_zero: If set to `True`, the attention mask will be filled by `1` for actual values
                and by `0` for padded values. If set to `False`, inverts it (`1` for padded values, `0` for actual
                values)

        Returns:
            If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the
            task-specific features. If the input is a list of `InputExamples`, will return a list of task-specific
            `InputFeatures` which can be fed to the model.

        """
        if max_length is None:
            max_length = tokenizer.max_len

        label_map = {label: i for i, label in enumerate(self.labels)}

        all_input_ids = []
        for ex_index, example in enumerate(self.examples):
            if ex_index % 10000 == 0:
                logger.info(f"Tokenizing example {ex_index}")

            input_ids = tokenizer.encode(
                example.text_a,
                add_special_tokens=True,
                max_length=min(max_length, tokenizer.max_len),
            )
            all_input_ids.append(input_ids)

        batch_length = max(len(input_ids) for input_ids in all_input_ids)

        features = []
        for ex_index, (input_ids, example) in enumerate(zip(all_input_ids, self.examples)):
            if ex_index % 10000 == 0:
                logger.info(f"Writing example {ex_index}/{len(self.examples)}")
            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

            # Zero-pad up to the sequence length.
            padding_length = batch_length - len(input_ids)
            if pad_on_left:
                input_ids = ([pad_token] * padding_length) + input_ids
                attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            else:
                input_ids = input_ids + ([pad_token] * padding_length)
                attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

            if len(input_ids) != batch_length:
                raise ValueError(f"Error with input length {len(input_ids)} vs {batch_length}")
            if len(attention_mask) != batch_length:
                raise ValueError(f"Error with input length {len(attention_mask)} vs {batch_length}")

            if self.mode == "classification":
                label = label_map[example.label]
            elif self.mode == "regression":
                label = float(example.label)
            else:
                raise ValueError(self.mode)

            if ex_index < 5 and self.verbose:
                logger.info("*** Example ***")
                logger.info(f"guid: {example.guid}")
                logger.info(f"input_ids: {' '.join([str(x) for x in input_ids])}")
                logger.info(f"attention_mask: {' '.join([str(x) for x in attention_mask])}")
                logger.info(f"label: {example.label} (id = {label})")

            features.append(InputFeatures(input_ids=input_ids, attention_mask=attention_mask, label=label))

        if return_tensors is None:
            return features
        elif return_tensors == "tf":
            if not is_tf_available():
                raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
            import tensorflow as tf

            def gen():
                for ex in features:
                    yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

            dataset = tf.data.Dataset.from_generator(
                gen,
                ({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64),
                ({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([])),
            )
            return dataset
        elif return_tensors == "pt":
            if not is_torch_available():
                raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
            import torch
            from torch.utils.data import TensorDataset

            all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
            all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
            if self.mode == "classification":
                all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
            elif self.mode == "regression":
                all_labels = torch.tensor([f.label for f in features], dtype=torch.float)

            dataset = TensorDataset(all_input_ids, all_attention_mask, all_labels)
            return dataset
        else:
            raise ValueError("return_tensors should be one of 'tf' or 'pt'")

# 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 csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


logger = logging.get_logger(__name__)


@dataclass
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.
    """

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

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(dataclasses.asdict(self), indent=2) + "\n"


@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.
    """

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

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(dataclasses.asdict(self)) + "\n"


class DataProcessor:
    """Base class for data converters for sequence classification data sets."""

    def get_example_from_tensor_dict(self, tensor_dict):
        """
        Gets an example from a dict with tensorflow tensors.

        Args:
            tensor_dict: Keys and values should match the corresponding Glue
                tensorflow_dataset examples.
        """
        raise NotImplementedError()

    def get_train_examples(self, data_dir):
        """Gets a collection of [`InputExample`] for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of [`InputExample`] for the dev set."""
        raise NotImplementedError()

    def get_test_examples(self, data_dir):
        """Gets a collection of [`InputExample`] for the test set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    def tfds_map(self, example):
        """
        Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
        examples to the correct format.
        """
        if len(self.get_labels()) > 1:
            example.label = self.get_labels()[int(example.label)]
        return example

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8-sig") as f:
            return list(csv.reader(f, delimiter="\t", quotechar=quotechar))


class SingleSentenceClassificationProcessor(DataProcessor):
    """Generic processor for a single sentence classification data set."""

    def __init__(self, labels=None, examples=None, mode="classification", verbose=False):
        self.labels = [] if labels is None else labels
        self.examples = [] if examples is None else examples
        self.mode = mode
        self.verbose = verbose

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

    def __getitem__(self, idx):
        if isinstance(idx, slice):
            return SingleSentenceClassificationProcessor(labels=self.labels, examples=self.examples[idx])
        return self.examples[idx]

    @classmethod
    def create_from_csv(
        cls, file_name, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, **kwargs
    ):
        processor = cls(**kwargs)
        processor.add_examples_from_csv(
            file_name,
            split_name=split_name,
            column_label=column_label,
            column_text=column_text,
            column_id=column_id,
            skip_first_row=skip_first_row,
            overwrite_labels=True,
            overwrite_examples=True,
        )
        return processor

    @classmethod
    def create_from_examples(cls, texts_or_text_and_labels, labels=None, **kwargs):
        processor = cls(**kwargs)
        processor.add_examples(texts_or_text_and_labels, labels=labels)
        return processor

    def add_examples_from_csv(
        self,
        file_name,
        split_name="",
        column_label=0,
        column_text=1,
        column_id=None,
        skip_first_row=False,
        overwrite_labels=False,
        overwrite_examples=False,
    ):
        lines = self._read_tsv(file_name)
        if skip_first_row:
            lines = lines[1:]
        texts = []
        labels = []
        ids = []
        for i, line in enumerate(lines):
            texts.append(line[column_text])
            labels.append(line[column_label])
            if column_id is not None:
                ids.append(line[column_id])
            else:
                guid = f"{split_name}-{i}" if split_name else str(i)
                ids.append(guid)

        return self.add_examples(
            texts, labels, ids, overwrite_labels=overwrite_labels, overwrite_examples=overwrite_examples
        )

    def add_examples(
        self, texts_or_text_and_labels, labels=None, ids=None, overwrite_labels=False, overwrite_examples=False
    ):
        if labels is not None and len(texts_or_text_and_labels) != len(labels):
            raise ValueError(
                f"Text and labels have mismatched lengths {len(texts_or_text_and_labels)} and {len(labels)}"
            )
        if ids is not None and len(texts_or_text_and_labels) != len(ids):
            raise ValueError(f"Text and ids have mismatched lengths {len(texts_or_text_and_labels)} and {len(ids)}")
        if ids is None:
            ids = [None] * len(texts_or_text_and_labels)
        if labels is None:
            labels = [None] * len(texts_or_text_and_labels)
        examples = []
        added_labels = set()
        for text_or_text_and_label, label, guid in zip(texts_or_text_and_labels, labels, ids):
            if isinstance(text_or_text_and_label, (tuple, list)) and label is None:
                text, label = text_or_text_and_label
            else:
                text = text_or_text_and_label
            added_labels.add(label)
            examples.append(InputExample(guid=guid, text_a=text, text_b=None, label=label))

        # Update examples
        if overwrite_examples:
            self.examples = examples
        else:
            self.examples.extend(examples)

        # Update labels
        if overwrite_labels:
            self.labels = list(added_labels)
        else:
            self.labels = list(set(self.labels).union(added_labels))

        return self.examples

    def get_features(
        self,
        tokenizer,
        max_length=None,
        pad_on_left=False,
        pad_token=0,
        mask_padding_with_zero=True,
        return_tensors=None,
    ):
        """
        Convert examples in a list of `InputFeatures`

        Args:
            tokenizer: Instance of a tokenizer that will tokenize the examples
            max_length: Maximum example length
            pad_on_left: If set to `True`, the examples will be padded on the left rather than on the right (default)
            pad_token: Padding token
            mask_padding_with_zero: If set to `True`, the attention mask will be filled by `1` for actual values
                and by `0` for padded values. If set to `False`, inverts it (`1` for padded values, `0` for actual
                values)

        Returns:
            If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the
            task-specific features. If the input is a list of `InputExamples`, will return a list of task-specific
            `InputFeatures` which can be fed to the model.

        """
        if max_length is None:
            max_length = tokenizer.max_len

        label_map = {label: i for i, label in enumerate(self.labels)}

        all_input_ids = []
        for ex_index, example in enumerate(self.examples):
            if ex_index % 10000 == 0:
                logger.info(f"Tokenizing example {ex_index}")

            input_ids = tokenizer.encode(
                example.text_a,
                add_special_tokens=True,
                max_length=min(max_length, tokenizer.max_len),
            )
            all_input_ids.append(input_ids)

        batch_length = max(len(input_ids) for input_ids in all_input_ids)

        features = []
        for ex_index, (input_ids, example) in enumerate(zip(all_input_ids, self.examples)):
            if ex_index % 10000 == 0:
                logger.info(f"Writing example {ex_index}/{len(self.examples)}")
            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

            # Zero-pad up to the sequence length.
            padding_length = batch_length - len(input_ids)
            if pad_on_left:
                input_ids = ([pad_token] * padding_length) + input_ids
                attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            else:
                input_ids = input_ids + ([pad_token] * padding_length)
                attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

            if len(input_ids) != batch_length:
                raise ValueError(f"Error with input length {len(input_ids)} vs {batch_length}")
            if len(attention_mask) != batch_length:
                raise ValueError(f"Error with input length {len(attention_mask)} vs {batch_length}")

            if self.mode == "classification":
                label = label_map[example.label]
            elif self.mode == "regression":
                label = float(example.label)
            else:
                raise ValueError(self.mode)

            if ex_index < 5 and self.verbose:
                logger.info("*** Example ***")
                logger.info(f"guid: {example.guid}")
                logger.info(f"input_ids: {' '.join([str(x) for x in input_ids])}")
                logger.info(f"attention_mask: {' '.join([str(x) for x in attention_mask])}")
                logger.info(f"label: {example.label} (id = {label})")

            features.append(InputFeatures(input_ids=input_ids, attention_mask=attention_mask, label=label))

        if return_tensors is None:
            return features
        elif return_tensors == "tf":
            if not is_tf_available():
                raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
            import tensorflow as tf

            def gen():
                for ex in features:
                    yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

            dataset = tf.data.Dataset.from_generator(
                gen,
                ({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64),
                ({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([])),
            )
            return dataset
        elif return_tensors == "pt":
            if not is_torch_available():
                raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
            import torch
            from torch.utils.data import TensorDataset

            all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
            all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
            if self.mode == "classification":
                all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
            elif self.mode == "regression":
                all_labels = torch.tensor([f.label for f in features], dtype=torch.float)

            dataset = TensorDataset(all_input_ids, all_attention_mask, all_labels)
            return dataset
        else:
            raise ValueError("return_tensors should be one of 'tf' or 'pt'")
