


'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowerCamelCase												=						logging.get_logger(__name__)


if is_vision_available():
    import PIL




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["pixel_values"]


 def __init__(      self							:	Dict			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Dict[str, int] = None			,       __lowerCamelCase							:	PILImageResampling = PILImageResampling.BICUBIC			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Dict[str, int] = None			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Union[int, float] = 1 / 2_5_5			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[Union[float, List[float]]] = None			,       __lowerCamelCase							:	Optional[Union[float, List[float]]] = None			,       __lowerCamelCase							:	bool = True			,       **__lowerCamelCase							:	Optional[int]			,       ):
    super().__init__(**__lowerCamelCase )
    UpperCAmelCase__	:Dict      =      size if size is not None else {'''shortest_edge''': 2_2_4}
    UpperCAmelCase__	:Optional[int]      =      get_size_dict(__lowerCamelCase			,       default_to_square=__lowerCamelCase )
    UpperCAmelCase__	:Tuple      =      crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
    UpperCAmelCase__	:Optional[int]      =      get_size_dict(__lowerCamelCase			,       default_to_square=__lowerCamelCase			,       param_name='''crop_size''' )

    UpperCAmelCase__	:Optional[int]      =      do_resize
    UpperCAmelCase__	:int      =      size
    UpperCAmelCase__	:List[Any]      =      resample
    UpperCAmelCase__	:Any      =      do_center_crop
    UpperCAmelCase__	:int      =      crop_size
    UpperCAmelCase__	:int      =      do_rescale
    UpperCAmelCase__	:Any      =      rescale_factor
    UpperCAmelCase__	:Any      =      do_normalize
    UpperCAmelCase__	:Any      =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    UpperCAmelCase__	:int      =      image_std if image_std is not None else OPENAI_CLIP_STD
    UpperCAmelCase__	:Any      =      do_convert_rgb


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	np.ndarray			,       __lowerCamelCase							:	Dict[str, int]			,       __lowerCamelCase							:	PILImageResampling = PILImageResampling.BICUBIC			,       __lowerCamelCase							:	Optional[Union[str, ChannelDimension]] = None			,       **__lowerCamelCase							:	Tuple			,       ):
    UpperCAmelCase__	:int      =      get_size_dict(__lowerCamelCase			,       default_to_square=__lowerCamelCase )
    if "shortest_edge" not in size:
       raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
    UpperCAmelCase__	:Tuple      =      get_resize_output_image_size(__lowerCamelCase			,       size=size['''shortest_edge''']			,       default_to_square=__lowerCamelCase )
    return resize(__lowerCamelCase			,       size=__lowerCamelCase			,       resample=__lowerCamelCase			,       data_format=__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	np.ndarray			,       __lowerCamelCase							:	Dict[str, int]			,       __lowerCamelCase							:	Optional[Union[str, ChannelDimension]] = None			,       **__lowerCamelCase							:	Optional[Any]			,       ):
    UpperCAmelCase__	:List[Any]      =      get_size_dict(__lowerCamelCase )
    if "height" not in size or "width" not in size:
       raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
    return center_crop(__lowerCamelCase			,       size=(size['''height'''], size['''width'''])			,       data_format=__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	np.ndarray			,       __lowerCamelCase							:	Union[int, float]			,       __lowerCamelCase							:	Optional[Union[str, ChannelDimension]] = None			,       **__lowerCamelCase							:	Dict			,       ):
    return rescale(__lowerCamelCase			,       scale=__lowerCamelCase			,       data_format=__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	np.ndarray			,       __lowerCamelCase							:	Union[float, List[float]]			,       __lowerCamelCase							:	Union[float, List[float]]			,       __lowerCamelCase							:	Optional[Union[str, ChannelDimension]] = None			,       **__lowerCamelCase							:	int			,       ):
    return normalize(__lowerCamelCase			,       mean=__lowerCamelCase			,       std=__lowerCamelCase			,       data_format=__lowerCamelCase			,       **__lowerCamelCase )





 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       __lowerCamelCase							:	ImageInput			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	Dict[str, int] = None			,       __lowerCamelCase							:	PILImageResampling = None			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	int = None			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	float = None			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	Optional[Union[float, List[float]]] = None			,       __lowerCamelCase							:	Optional[Union[float, List[float]]] = None			,       __lowerCamelCase							:	bool = None			,       __lowerCamelCase							:	Optional[Union[str, TensorType]] = None			,       __lowerCamelCase							:	Optional[ChannelDimension] = ChannelDimension.FIRST			,       **__lowerCamelCase							:	Optional[Any]			,       ):
    UpperCAmelCase__	:Tuple      =      do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase__	:Dict      =      size if size is not None else self.size
    UpperCAmelCase__	:Optional[Any]      =      get_size_dict(__lowerCamelCase			,       param_name='''size'''			,       default_to_square=__lowerCamelCase )
    UpperCAmelCase__	:str      =      resample if resample is not None else self.resample
    UpperCAmelCase__	:Union[str, Any]      =      do_center_crop if do_center_crop is not None else self.do_center_crop
    UpperCAmelCase__	:Dict      =      crop_size if crop_size is not None else self.crop_size
    UpperCAmelCase__	:Optional[Any]      =      get_size_dict(__lowerCamelCase			,       param_name='''crop_size'''			,       default_to_square=__lowerCamelCase )
    UpperCAmelCase__	:Optional[Any]      =      do_rescale if do_rescale is not None else self.do_rescale
    UpperCAmelCase__	:List[str]      =      rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCAmelCase__	:int      =      do_normalize if do_normalize is not None else self.do_normalize
    UpperCAmelCase__	:List[str]      =      image_mean if image_mean is not None else self.image_mean
    UpperCAmelCase__	:Optional[Any]      =      image_std if image_std is not None else self.image_std
    UpperCAmelCase__	:List[Any]      =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    UpperCAmelCase__	:str      =      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_resize and size is None:
       raise ValueError('''Size must be specified if do_resize is True.''' )

    if do_center_crop and crop_size is None:
       raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

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

    if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

    # PIL RGBA images are converted to RGB
    if do_convert_rgb:
       UpperCAmelCase__	:Optional[int]      =      [convert_to_rgb(__lowerCamelCase ) for image in images]

    # All transformations expect numpy arrays.
    UpperCAmelCase__	:List[str]      =      [to_numpy_array(__lowerCamelCase ) for image in images]

    if do_resize:
       UpperCAmelCase__	:Union[str, Any]      =      [self.resize(image=__lowerCamelCase			,       size=__lowerCamelCase			,       resample=__lowerCamelCase ) for image in images]

    if do_center_crop:
       UpperCAmelCase__	:Optional[int]      =      [self.center_crop(image=__lowerCamelCase			,       size=__lowerCamelCase ) for image in images]

    if do_rescale:
       UpperCAmelCase__	:str      =      [self.rescale(image=__lowerCamelCase			,       scale=__lowerCamelCase ) for image in images]

    if do_normalize:
       UpperCAmelCase__	:Union[str, Any]      =      [self.normalize(image=__lowerCamelCase			,       mean=__lowerCamelCase			,       std=__lowerCamelCase ) for image in images]

    UpperCAmelCase__	:List[Any]      =      [to_channel_dimension_format(__lowerCamelCase			,       __lowerCamelCase ) for image in images]

    UpperCAmelCase__	:Optional[Any]      =      {'''pixel_values''': images}
    return BatchFeature(data=__lowerCamelCase			,       tensor_type=__lowerCamelCase )







'''simple docstring'''





import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    '''split_dict''',   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''',   num_bytes=1_337,   num_examples=42,   dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train''',   num_bytes=1_337,   num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ],   )
def        a__     (					UpperCamelCase_						:      SplitDict						):
   UpperCAmelCase__	:List[Any]      =      split_dict._to_yaml_list()
   assert len(UpperCamelCase_						) == len(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      SplitDict._from_yaml_list(UpperCamelCase_						)
   for split_name, split_info in split_dict.items():
      # dataset_name field is deprecated, and is therefore not part of the YAML dump
      UpperCAmelCase__	:Union[str, Any]      =      None
      # the split name of split_dict takes over the name of the split info object
      UpperCAmelCase__	:Union[str, Any]      =      split_name
   assert split_dict == reloaded


@pytest.mark.parametrize(
    '''split_info''',   [SplitInfo(), SplitInfo(dataset_name=UpperCamelCase_						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def        a__     (					UpperCamelCase_						:      List[Any]						):
   # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
   # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
   UpperCAmelCase__	:List[Any]      =      asdict(SplitDict({'''train''': split_info}						)						)
   assert "dataset_name" in split_dict_asdict["train"]
   assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name



