lib/python3.12/site-packages/datasets/formatting/tf_formatter.py

# Copyright 2020 The HuggingFace Authors.
#
# 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.

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import tensorflow as tf


class TFFormatter(TensorFormatter[Mapping, "tf.Tensor", Mapping]):
    def __init__(self, features=None, token_per_repo_id=None, **tf_tensor_kwargs):
        super().__init__(features=features, token_per_repo_id=token_per_repo_id)
        self.tf_tensor_kwargs = tf_tensor_kwargs
        import tensorflow as tf  # noqa: F401 - import tf at initialization

    def _consolidate(self, column):
        import tensorflow as tf

        if isinstance(column, list) and column:
            if all(
                isinstance(x, tf.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column
            ):
                return tf.stack(column)
            elif all(
                isinstance(x, (tf.Tensor, tf.RaggedTensor)) and x.ndim == 1 and x.dtype == column[0].dtype
                for x in column
            ):
                # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                return tf.ragged.stack(column)

        return column

    def _tensorize(self, value):
        import tensorflow as tf

        if value is None:
            return value

        default_dtype = {}

        if isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.integer):
            default_dtype = {"dtype": tf.int64}
        elif isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.floating):
            default_dtype = {"dtype": tf.float32}

        if config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(value, PIL.Image.Image):
                value = np.asarray(value)
        if config.TORCHVISION_AVAILABLE and "torchvision" in sys.modules:
            from torchvision.io import VideoReader

            if isinstance(value, VideoReader):
                return value  # TODO(QL): set output to tf tensors ?

        return tf.convert_to_tensor(value, **{**default_dtype, **self.tf_tensor_kwargs})

    def _recursive_tensorize(self, data_struct):
        import tensorflow as tf

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
            import torch

            if isinstance(data_struct, torch.Tensor):
                return self._tensorize(data_struct.detach().cpu().numpy()[()])
        if hasattr(data_struct, "__array__") and not isinstance(data_struct, tf.Tensor):
            data_struct = data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(data_struct, np.ndarray):
            if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
        elif isinstance(data_struct, (list, tuple)):
            return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
        return self._tensorize(data_struct)

    def recursive_tensorize(self, data_struct: dict):
        return map_nested(self._recursive_tensorize, data_struct, map_list=False)

    def format_row(self, pa_table: pa.Table) -> Mapping:
        row = self.numpy_arrow_extractor().extract_row(pa_table)
        row = self.python_features_decoder.decode_row(row)
        return self.recursive_tensorize(row)

    def format_column(self, pa_table: pa.Table) -> "tf.Tensor":
        column = self.numpy_arrow_extractor().extract_column(pa_table)
        column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
        column = self.recursive_tensorize(column)
        column = self._consolidate(column)
        return column

    def format_batch(self, pa_table: pa.Table) -> Mapping:
        batch = self.numpy_arrow_extractor().extract_batch(pa_table)
        batch = self.python_features_decoder.decode_batch(batch)
        batch = self.recursive_tensorize(batch)
        for column_name in batch:
            batch[column_name] = self._consolidate(batch[column_name])
        return batch