"""simple docstring"""


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__UpperCamelCase									=			logging.get_logger(__name__)




class  UpperCamelCase					(				__a   ):
       SCREAMING_SNAKE_CASE_			=      ["""pixel_values"""]







       def __init__( self,							lowerCAmelCase__ = True,							lowerCAmelCase__ = 1 / 255,							lowerCAmelCase__ = True,							lowerCAmelCase__ = 8,							**lowerCAmelCase__,							)      ->      str:
            super().__init__(**lowerCAmelCase__)

            snake_case_					       =					do_rescale
            snake_case_					       =					rescale_factor
            snake_case_					       =					do_pad
            snake_case_					       =					pad_size







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__)      ->      Any:
            return rescale(lowerCAmelCase__,							scale=lowerCAmelCase__,							data_format=lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      Optional[Any]:
            snake_case_       ,	snake_case_					       =					get_image_size(lowerCAmelCase__)
            snake_case_					       =					(old_height // size + 1) * size - old_height
            snake_case_					       =					(old_width // size + 1) * size - old_width

            return pad(lowerCAmelCase__,							((0, pad_height), (0, pad_width)),							mode='symmetric',							data_format=lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = ChannelDimension.FIRST,							**lowerCAmelCase__,							)      ->      List[Any]:
            snake_case_					       =					do_rescale if do_rescale is not None else self.do_rescale
            snake_case_					       =					rescale_factor if rescale_factor is not None else self.rescale_factor
            snake_case_					       =					do_pad if do_pad is not None else self.do_pad
            snake_case_					       =					pad_size if pad_size is not None else self.pad_size

            snake_case_					       =					make_list_of_images(lowerCAmelCase__)

            if not valid_images(lowerCAmelCase__):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.')

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.')

            # All transformations expect numpy arrays.
            snake_case_					       =					[to_numpy_array(lowerCAmelCase__) for image in images]

            if do_rescale:
                 snake_case_					       =					[self.rescale(image=lowerCAmelCase__,							scale=lowerCAmelCase__) for image in images]

            if do_pad:
                 snake_case_					       =					[self.pad(lowerCAmelCase__,							size=lowerCAmelCase__) for image in images]

            snake_case_					       =					[to_channel_dimension_format(lowerCAmelCase__,							lowerCAmelCase__) for image in images]

            snake_case_					       =					{'pixel_values': images}
            return BatchFeature(data=lowerCAmelCase__,							tensor_type=lowerCAmelCase__)







'''simple docstring'''







from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


A     =logging.get_logger(__name__)


A     =TypeVar('DatasetType', Dataset, IterableDataset)





def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[List[float]] = None    ,   _a	:							Optional[int] = None    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,   ):
    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError('''Unable to interleave an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."     )
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )
    else:
        return _interleave_iterable_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )







def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							int = 0    ,   ):

    if not dsets:
        raise ValueError('''Unable to concatenate an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )
    else:
        return _concatenate_iterable_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )




