





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					:				Optional[Any]  							=					logging.get_logger(__name__)


if is_vision_available():
		import PIL


class 						lowerCamelCase   (__lowerCAmelCase			):







							"""simple docstring"""




							UpperCAmelCase_									=    ["""pixel_values"""]






							def __init__(					self	:     Union[str, Any], _UpperCAmelCase	:     int = True, _UpperCAmelCase	:     str = None, _UpperCAmelCase	:     Union[str, Any] = PILImageResampling.BICUBIC, _UpperCAmelCase	:     Tuple = True, _UpperCAmelCase	:     Union[str, Any] = None, _UpperCAmelCase	:     List[Any] = True, _UpperCAmelCase	:     Optional[int] = 1 / 2_5_5, _UpperCAmelCase	:     str = True, _UpperCAmelCase	:     int = None, _UpperCAmelCase	:     Optional[int] = None, _UpperCAmelCase	:     int = True, **_UpperCAmelCase	:     Optional[Any], )      -> List[Any]:





														"""simple docstring"""
														super().__init__(**__SCREAMING_SNAKE_CASE						)
														SCREAMING_SNAKE_CASE__   :			Dict      =						size if size is not None else {"shortest_edge": 2_2_4}
														SCREAMING_SNAKE_CASE__   :			Dict      =						get_size_dict(__SCREAMING_SNAKE_CASE, default_to_square=__SCREAMING_SNAKE_CASE						)
														SCREAMING_SNAKE_CASE__   :			Dict      =						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
														SCREAMING_SNAKE_CASE__   :			List[str]      =						get_size_dict(__SCREAMING_SNAKE_CASE						)

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






							def 					A_	(					self	:     Union[str, Any], _UpperCAmelCase	:     Tuple, _UpperCAmelCase	:     Dict, _UpperCAmelCase	:     Optional[Any] = PILImageResampling.BICUBIC, _UpperCAmelCase	:     Any = None, **_UpperCAmelCase	:     int, )      -> Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Dict      =						get_size_dict(__SCREAMING_SNAKE_CASE, default_to_square=__SCREAMING_SNAKE_CASE						)
														SCREAMING_SNAKE_CASE__   :			str      =						get_resize_output_image_size(
														    __SCREAMING_SNAKE_CASE, size=(size["height"], size["width"]), default_to_square=__SCREAMING_SNAKE_CASE						)
														return resize(__SCREAMING_SNAKE_CASE, size=__SCREAMING_SNAKE_CASE, resample=__SCREAMING_SNAKE_CASE, data_format=__SCREAMING_SNAKE_CASE, **__SCREAMING_SNAKE_CASE						)






							def 					A_	(					self	:     Dict, _UpperCAmelCase	:     int, _UpperCAmelCase	:     Optional[Any], _UpperCAmelCase	:     List[Any] = None, **_UpperCAmelCase	:     Union[str, Any], )      -> str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Dict      =						get_size_dict(__SCREAMING_SNAKE_CASE						)
														return center_crop(__SCREAMING_SNAKE_CASE, size=(size["height"], size["width"]), data_format=__SCREAMING_SNAKE_CASE, **__SCREAMING_SNAKE_CASE						)






							def 					A_	(					self	:     Tuple, _UpperCAmelCase	:     List[str], _UpperCAmelCase	:     Union[str, Any], _UpperCAmelCase	:     Optional[Any] = None, **_UpperCAmelCase	:     int, )      -> int:





														"""simple docstring"""
														return rescale(__SCREAMING_SNAKE_CASE, scale=__SCREAMING_SNAKE_CASE, data_format=__SCREAMING_SNAKE_CASE, **__SCREAMING_SNAKE_CASE						)






							def 					A_	(					self	:     Tuple, _UpperCAmelCase	:     List[Any], _UpperCAmelCase	:     Any, _UpperCAmelCase	:     str, _UpperCAmelCase	:     Optional[Any] = None, **_UpperCAmelCase	:     str, )      -> int:





														"""simple docstring"""
														return normalize(__SCREAMING_SNAKE_CASE, mean=__SCREAMING_SNAKE_CASE, std=__SCREAMING_SNAKE_CASE, data_format=__SCREAMING_SNAKE_CASE, **__SCREAMING_SNAKE_CASE						)






							def 					A_	(					self	:     int, _UpperCAmelCase	:     Dict, _UpperCAmelCase	:     Union[str, Any] = None, _UpperCAmelCase	:     int = None, _UpperCAmelCase	:     Dict = None, _UpperCAmelCase	:     Any = None, _UpperCAmelCase	:     str = None, _UpperCAmelCase	:     str = None, _UpperCAmelCase	:     str = None, _UpperCAmelCase	:     int = None, _UpperCAmelCase	:     Optional[int] = None, _UpperCAmelCase	:     Dict = None, _UpperCAmelCase	:     List[str] = None, _UpperCAmelCase	:     List[str] = None, _UpperCAmelCase	:     int = ChannelDimension.FIRST, **_UpperCAmelCase	:     List[str], )      -> int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						do_resize if do_resize is not None else self.do_resize
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						size if size is not None else self.size
														SCREAMING_SNAKE_CASE__   :			str      =						get_size_dict(__SCREAMING_SNAKE_CASE, default_to_square=__SCREAMING_SNAKE_CASE						)
														SCREAMING_SNAKE_CASE__   :			int      =						resample if resample is not None else self.resample
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						do_center_crop if do_center_crop is not None else self.do_center_crop
														SCREAMING_SNAKE_CASE__   :			str      =						crop_size if crop_size is not None else self.crop_size
														SCREAMING_SNAKE_CASE__   :			List[str]      =						get_size_dict(__SCREAMING_SNAKE_CASE						)
														SCREAMING_SNAKE_CASE__   :			Dict      =						do_rescale if do_rescale is not None else self.do_rescale
														SCREAMING_SNAKE_CASE__   :			int      =						rescale_factor if rescale_factor is not None else self.rescale_factor
														SCREAMING_SNAKE_CASE__   :			Dict      =						do_normalize if do_normalize is not None else self.do_normalize
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						image_mean if image_mean is not None else self.image_mean
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						image_std if image_std is not None else self.image_std
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														SCREAMING_SNAKE_CASE__   :			Dict      =						make_list_of_images(__SCREAMING_SNAKE_CASE						)

														if not valid_images(__SCREAMING_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:
																					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:
																					SCREAMING_SNAKE_CASE__   :			Any      =						[convert_to_rgb(__SCREAMING_SNAKE_CASE						) for image in images]

														# All transformations expect numpy arrays.
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						[to_numpy_array(__SCREAMING_SNAKE_CASE						) for image in images]

														if do_resize:
																					SCREAMING_SNAKE_CASE__   :			Optional[int]      =						[self.resize(image=__SCREAMING_SNAKE_CASE, size=__SCREAMING_SNAKE_CASE, resample=__SCREAMING_SNAKE_CASE						) for image in images]

														if do_center_crop:
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						[self.center_crop(image=__SCREAMING_SNAKE_CASE, size=__SCREAMING_SNAKE_CASE						) for image in images]

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

														if do_normalize:
																					SCREAMING_SNAKE_CASE__   :			Optional[int]      =						[self.normalize(image=__SCREAMING_SNAKE_CASE, mean=__SCREAMING_SNAKE_CASE, std=__SCREAMING_SNAKE_CASE						) for image in images]

														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						[to_channel_dimension_format(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE						) for image in images]

														SCREAMING_SNAKE_CASE__   :			List[Any]      =						{"pixel_values": images}
														return BatchFeature(data=__SCREAMING_SNAKE_CASE, tensor_type=__SCREAMING_SNAKE_CASE						)













"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


a__    :   str    						= logging.get_logger(__name__)



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    *__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        warnings.warn(
            "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use ViltImageProcessor instead."      ,    __SCREAMING_SNAKE_CASE      ,    )
        super().__init__(*__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

