
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 convert_to_rgb, 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


if is_vision_available():
					import PIL


A     :	Optional[int]    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    ['''pixel_values''']




				def __init__(     self		,     _snake_case = True		,     _snake_case = None		,     _snake_case = PILImageResampling.BICUBIC		,     _snake_case = True		,     _snake_case = 1 / 255		,     _snake_case = True		,     _snake_case = None		,     _snake_case = None		,     _snake_case = True		,     **_snake_case		,     )    ->      None:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    size if size is not None else {'''height''': 384, '''width''': 384}
								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)

								__a										=    do_resize
								__a										=    size
								__a										=    resample
								__a										=    do_rescale
								__a										=    rescale_factor
								__a										=    do_normalize
								__a										=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								__a										=    image_std if image_std is not None else OPENAI_CLIP_STD
								__a										=    do_convert_rgb




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = PILImageResampling.BICUBIC		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								if "height" not in size or "width" not in size:
												raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
								__a										=    (size['''height'''], size['''width'''])
								return resize(_snake_case		,     size=_snake_case		,     resample=_snake_case		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      Tuple:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								return normalize(_snake_case		,     mean=_snake_case		,     std=_snake_case		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = ChannelDimension.FIRST		,     **_snake_case		,     )    ->      PIL.Image.Image:


								'''simple docstring'''




								__a										=    do_resize if do_resize is not None else self.do_resize
								__a										=    resample if resample is not None else self.resample
								__a										=    do_rescale if do_rescale is not None else self.do_rescale
								__a										=    rescale_factor if rescale_factor is not None else self.rescale_factor
								__a										=    do_normalize if do_normalize is not None else self.do_normalize
								__a										=    image_mean if image_mean is not None else self.image_mean
								__a										=    image_std if image_std is not None else self.image_std
								__a										=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__a										=    size if size is not None else self.size
								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)

								__a										=    make_list_of_images(_snake_case				)

								if not valid_images(_snake_case				):
												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 or resample is None:
												raise ValueError('''Size and resample must be specified if do_resize 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:
												__a										=    [convert_to_rgb(_snake_case				) for image in images]

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

								if do_resize:
												__a										=    [self.resize(image=_snake_case		,     size=_snake_case		,     resample=_snake_case				) for image in images]

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

								if do_normalize:
												__a										=    [self.normalize(image=_snake_case		,     mean=_snake_case		,     std=_snake_case				) for image in images]

								__a										=    [to_channel_dimension_format(_snake_case		,     _snake_case				) for image in images]

								__a										=    BatchFeature(data={'''pixel_values''': images}		,     tensor_type=_snake_case				)

								return encoded_outputs






__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






