





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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCAmelCase       :   Dict				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     ["""pixel_values"""]




       def __init__(  self :    List[Any]							,				A :    bool = True							,				A :    Optional[Dict[str, int]] = None							,				A :    PILImageResampling = PILImageResampling.BILINEAR							,				A :    bool = True							,				A :    Dict[str, int] = None							,				A :    bool = True							,				A :    Union[int, float] = 1 / 255							,				A :    bool = True							,				A :    Optional[Union[float, List[float]]] = None							,				A :    Optional[Union[float, List[float]]] = None							,				**A :    Optional[Any]							,				):
         super().__init__(**A		)
         __snake_case:						List[str]   =						size if size is not None else {"""shortest_edge""": 256}
         __snake_case:						Dict   =						get_size_dict(A							,				default_to_square=A		)
         __snake_case:						Union[str, Any]   =						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         __snake_case:						Optional[Any]   =						get_size_dict(A		)
         __snake_case:						Optional[int]   =						do_resize
         __snake_case:						Tuple   =						size
         __snake_case:						Optional[Any]   =						resample
         __snake_case:						str   =						do_center_crop
         __snake_case:						Dict   =						crop_size
         __snake_case:						Union[str, Any]   =						do_rescale
         __snake_case:						List[Any]   =						rescale_factor
         __snake_case:						Union[str, Any]   =						do_normalize
         __snake_case:						Any   =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __snake_case:						str   =						image_std if image_std is not None else IMAGENET_STANDARD_STD




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




       def 							UpperCAmelCase__ (  self :    str							,				A :    np.ndarray							,				A :    Dict[str, int]							,				A :    Optional[Union[str, ChannelDimension]] = None							,				**A :    Union[str, Any]							,				):
         __snake_case:						Any   =						get_size_dict(A		)
         return center_crop(A							,				size=(size["""height"""], size["""width"""])							,				data_format=A							,				**A		)




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    np.ndarray							,				A :    float							,				A :    Optional[Union[str, ChannelDimension]] = None							,				**A :    int		):
         return rescale(A							,				scale=A							,				data_format=A							,				**A		)




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






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

         __snake_case:						Dict   =						make_list_of_images(A		)

         if not valid_images(A		):
           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."""		)

         # All transformations expect numpy arrays.
         __snake_case:						Tuple   =						[to_numpy_array(A		) for image in images]

         if do_resize:
           __snake_case:						List[str]   =						[self.resize(image=A							,				size=A							,				resample=A		) for image in images]

         if do_center_crop:
           __snake_case:						List[Any]   =						[self.center_crop(image=A							,				size=A		) for image in images]

         if do_rescale:
           __snake_case:						Optional[int]   =						[self.rescale(image=A							,				scale=A		) for image in images]

         if do_normalize:
           __snake_case:						List[Any]   =						[self.normalize(image=A							,				mean=A							,				std=A		) for image in images]

         __snake_case:						Any   =						[to_channel_dimension_format(A							,				A		) for image in images]

         __snake_case:						str   =						{"""pixel_values""": images}
         return BatchFeature(data=A							,				tensor_type=A		)













__UpperCAmelCase       :   int				       =							[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






