
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


UpperCamelCase_						:     Dict	       =							logging.get_logger(__name__)


if is_vision_available():
							import PIL




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: Optional[Any]      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: bool = True      ,	snake_case				: Union[int, float] = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: bool = True      ,	**snake_case				: Union[str, Any]      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	str           =   size if size is not None else {"shortest_edge": 224}
							_lowercase       :	Optional[Any]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	List[str]           =   crop_size if crop_size is not None else {"height": 224, "width": 224}
							_lowercase       :	Optional[int]           =   get_size_dict(snake_case      ,	default_to_square=snake_case      ,	param_name="crop_size"    )

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






	def       _a(self				: Dict      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Any      ,	) ->			np.ndarray:
							_lowercase       :	List[Any]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							if "shortest_edge" not in size:
													raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
							_lowercase       :	List[str]           =   get_resize_output_image_size(snake_case      ,	size=size["shortest_edge"]      ,	default_to_square=snake_case    )
							return resize(snake_case      ,	size=snake_case      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: List[str]      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: List[str]      ,	) ->			np.ndarray:
							_lowercase       :	List[Any]           =   get_size_dict(snake_case    )
							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(snake_case      ,	size=(size["height"], size["width"])      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Dict      ,	snake_case				: np.ndarray      ,	snake_case				: Union[int, float]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: int      ,	) ->			Tuple:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: List[str]      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: str      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Optional[Any]      ,	snake_case				: ImageInput      ,	snake_case				: bool = None      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = None      ,	snake_case				: bool = None      ,	snake_case				: int = None      ,	snake_case				: bool = None      ,	snake_case				: float = None      ,	snake_case				: bool = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: Optional[Union[float, List[float]]] = None      ,	snake_case				: bool = None      ,	snake_case				: Optional[Union[str, TensorType]] = None      ,	snake_case				: Optional[ChannelDimension] = ChannelDimension.FIRST      ,	**snake_case				: Union[str, Any]      ,	) ->			PIL.Image.Image:
							_lowercase       :	str           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	List[Any]           =   size if size is not None else self.size
							_lowercase       :	int           =   get_size_dict(snake_case      ,	param_name="size"      ,	default_to_square=snake_case    )
							_lowercase       :	List[Any]           =   resample if resample is not None else self.resample
							_lowercase       :	List[str]           =   do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowercase       :	int           =   crop_size if crop_size is not None else self.crop_size
							_lowercase       :	Tuple           =   get_size_dict(snake_case      ,	param_name="crop_size"      ,	default_to_square=snake_case    )
							_lowercase       :	Optional[int]           =   do_rescale if do_rescale is not None else self.do_rescale
							_lowercase       :	Any           =   rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowercase       :	str           =   do_normalize if do_normalize is not None else self.do_normalize
							_lowercase       :	Optional[int]           =   image_mean if image_mean is not None else self.image_mean
							_lowercase       :	Tuple           =   image_std if image_std is not None else self.image_std
							_lowercase       :	Optional[int]           =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							_lowercase       :	Union[str, Any]           =   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:
													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:
													_lowercase       :	int           =   [convert_to_rgb(snake_case    ) for image in images]

							# All transformations expect numpy arrays.
							_lowercase       :	Union[str, Any]           =   [to_numpy_array(snake_case    ) for image in images]

							if do_resize:
													_lowercase       :	str           =   [self.resize(image=snake_case      ,	size=snake_case      ,	resample=snake_case    ) for image in images]

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

							if do_rescale:
													_lowercase       :	Any           =   [self.rescale(image=snake_case      ,	scale=snake_case    ) for image in images]

							if do_normalize:
													_lowercase       :	Optional[Any]           =   [self.normalize(image=snake_case      ,	mean=snake_case      ,	std=snake_case    ) for image in images]

							_lowercase       :	Optional[int]           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Union[str, Any]           =   {"pixel_values": images}
							return BatchFeature(data=snake_case      ,	tensor_type=snake_case    )






import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCamelCase_						:     List[str]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	*snake_case				: Optional[Any]      ,	**snake_case				: Optional[Any]    ) ->			None:
							warnings.warn(
							    "The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use FlavaImageProcessor instead."      ,	snake_case      ,	)
							super().__init__(*snake_case      ,	**snake_case    )




