


'''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


lowerCamelCase				:  Optional[int]    		=      logging.get_logger(__name__)
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Optional[int]       =				["""pixel_values"""]



      def __init__(self       :       str  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Union[int, float] = 1 / 255  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       int = 8  ,		**UpperCamelCase       :       Dict  ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase					)

             lowercase__      =					do_rescale
             lowercase__      =					rescale_factor
             lowercase__      =					do_pad
             lowercase__      =					pad_size



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       float  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       Tuple					):

             '''simple docstring'''







             return rescale(UpperCamelCase  ,		scale=UpperCamelCase  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					get_image_size(UpperCamelCase					)
             lowercase__      =					(old_height // size + 1) * size - old_height
             lowercase__      =					(old_width // size + 1) * size - old_width

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






      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       ImageInput  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[float] = None  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       Optional[Union[str, TensorType]] = None  ,		UpperCamelCase       :       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,		**UpperCamelCase       :       int  ,		):

             '''simple docstring'''







             lowercase__      =					do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      =					rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      =					do_pad if do_pad is not None else self.do_pad
             lowercase__      =					pad_size if pad_size is not None else self.pad_size

             lowercase__      =					make_list_of_images(UpperCamelCase					)

             if not valid_images(UpperCamelCase					):
                    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.
             lowercase__      =					[to_numpy_array(UpperCamelCase					) for image in images]

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

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

             lowercase__      =					[to_channel_dimension_format(UpperCamelCase  ,		UpperCamelCase					) for image in images]

             lowercase__      =					{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase  ,		tensor_type=UpperCamelCase					)








'''simple docstring'''

# 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 torch
class     __lowerCAmelCase		(TensorFormatter[Mapping, """torch.Tensor""", Mapping]	):





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple=None  ,		**UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             super().__init__(features=UpperCamelCase					)
             lowercase__      =					torch_tensor_kwargs
             import torch  # noqa import torch at initialization



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







             import torch

             if isinstance(UpperCamelCase  ,		UpperCamelCase					) and column:
                    if all(
                        isinstance(UpperCamelCase  ,		torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
                        for x in column					):
                           return torch.stack(UpperCamelCase					)
             return column



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             import torch

             if isinstance(UpperCamelCase  ,		(str, bytes, type(UpperCamelCase					))					):
                    return value
             elif isinstance(UpperCamelCase  ,		(np.character, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.character					):
                    return value.tolist()

             lowercase__      =					{}

             if isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.integer					):
                    lowercase__      =					{'''dtype''': torch.intaa}
             elif isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.floating					):
                    lowercase__      =					{'''dtype''': torch.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(UpperCamelCase  ,		PIL.Image.Image					):
                           lowercase__      =					np.asarray(UpperCamelCase					)
             return torch.tensor(UpperCamelCase  ,		**{**default_dtype, **self.torch_tensor_kwargs}					)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             import torch

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



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       dict					):

             '''simple docstring'''







             return map_nested(self._recursive_tensorize  ,		UpperCamelCase  ,		map_list=UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_row(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_row(UpperCamelCase					)
             return self.recursive_tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_column(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_column(UpperCamelCase  ,		pa_table.column_names[0]					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             lowercase__      =					self._consolidate(UpperCamelCase					)
             return column






      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_batch(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_batch(UpperCamelCase					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             for column_name in batch:
                    lowercase__      =					self._consolidate(batch[column_name]					)
             return batch




