



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


__A             =logging.get_logger(__name__)


if is_vision_available():
				import PIL

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	["""pixel_values"""]




			def __init__(       self							:			Optional[int]	,	a_							:			bool = True	,	a_							:			Dict[str, int] = None	,	a_							:			PILImageResampling = PILImageResampling.BICUBIC	,	a_							:			bool = True	,	a_							:			Dict[str, int] = None	,	a_							:			bool = True	,	a_							:			Union[int, float] = 1 / 2_55	,	a_							:			bool = True	,	a_							:			Optional[Union[float, List[float]]] = None	,	a_							:			Optional[Union[float, List[float]]] = None	,	a_							:			bool = True	,	**a_							:			Any	,	):
					'''simple docstring'''





					super().__init__(**a_			)
					__UpperCAmelCase   :					Any													=     size if size is not None else {'''shortest_edge''': 2_24}
					__UpperCAmelCase   :					Optional[int]													=     get_size_dict(a_	,	default_to_square=a_			)
					__UpperCAmelCase   :					Union[str, Any]													=     crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
					__UpperCAmelCase   :					Union[str, Any]													=     get_size_dict(a_			)

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




			def 			snake_case__					(       self							:			Any	,	a_							:			np.ndarray	,	a_							:			Dict[str, int]	,	a_							:			PILImageResampling = PILImageResampling.BICUBIC	,	a_							:			Optional[Union[str, ChannelDimension]] = None	,	**a_							:			Dict	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Tuple													=     get_size_dict(a_	,	default_to_square=a_			)
					__UpperCAmelCase   :					Union[str, Any]													=     get_resize_output_image_size(
					    a_	,	size=(size['''height'''], size['''width'''])	,	default_to_square=a_			)
					return resize(a_	,	size=a_	,	resample=a_	,	data_format=a_	,	**a_			)




			def 			snake_case__					(       self							:			int	,	a_							:			np.ndarray	,	a_							:			Dict[str, int]	,	a_							:			Optional[Union[str, ChannelDimension]] = None	,	**a_							:			Optional[int]	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     get_size_dict(a_			)
					return center_crop(a_	,	size=(size['''height'''], size['''width'''])	,	data_format=a_	,	**a_			)




			def 			snake_case__					(       self							:			List[Any]	,	a_							:			np.ndarray	,	a_							:			Union[int, float]	,	a_							:			Optional[Union[str, ChannelDimension]] = None	,	**a_							:			str	,	):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			List[str]	,	a_							:			np.ndarray	,	a_							:			Union[float, List[float]]	,	a_							:			Union[float, List[float]]	,	a_							:			Optional[Union[str, ChannelDimension]] = None	,	**a_							:			Dict	,	):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			Tuple	,	a_							:			ImageInput	,	a_							:			bool = None	,	a_							:			Dict[str, int] = None	,	a_							:			PILImageResampling = None	,	a_							:			bool = None	,	a_							:			int = None	,	a_							:			bool = None	,	a_							:			float = None	,	a_							:			bool = None	,	a_							:			Optional[Union[float, List[float]]] = None	,	a_							:			Optional[Union[float, List[float]]] = None	,	a_							:			bool = None	,	a_							:			Optional[Union[str, TensorType]] = None	,	a_							:			Optional[ChannelDimension] = ChannelDimension.FIRST	,	**a_							:			str	,	):
					'''simple docstring'''





					__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   :					Dict													=     get_size_dict(a_	,	default_to_square=a_			)
					__UpperCAmelCase   :					str													=     resample if resample is not None else self.resample
					__UpperCAmelCase   :					Optional[Any]													=     do_center_crop if do_center_crop is not None else self.do_center_crop
					__UpperCAmelCase   :					str													=     crop_size if crop_size is not None else self.crop_size
					__UpperCAmelCase   :					Tuple													=     get_size_dict(a_			)
					__UpperCAmelCase   :					List[str]													=     do_rescale if do_rescale is not None else self.do_rescale
					__UpperCAmelCase   :					Optional[Any]													=     rescale_factor if rescale_factor is not None else self.rescale_factor
					__UpperCAmelCase   :					Optional[Any]													=     do_normalize if do_normalize is not None else self.do_normalize
					__UpperCAmelCase   :					Union[str, Any]													=     image_mean if image_mean is not None else self.image_mean
					__UpperCAmelCase   :					Dict													=     image_std if image_std is not None else self.image_std
					__UpperCAmelCase   :					str													=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					__UpperCAmelCase   :					Tuple													=     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.'''			)

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

					# All transformations expect numpy arrays.
					__UpperCAmelCase   :					Any													=     [to_numpy_array(a_			) for image in images]

					if do_resize:
							__UpperCAmelCase   :					Any													=     [self.resize(image=a_	,	size=a_	,	resample=a_			) for image in images]

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

					if do_rescale:
							__UpperCAmelCase   :					List[Any]													=     [self.rescale(image=a_	,	scale=a_			) for image in images]

					if do_normalize:
							__UpperCAmelCase   :					Any													=     [self.normalize(image=a_	,	mean=a_	,	std=a_			) for image in images]

					__UpperCAmelCase   :					int													=     [to_channel_dimension_format(a_	,	a_			) for image in images]

					__UpperCAmelCase   :					Dict													=     {'''pixel_values''': images}
					return BatchFeature(data=a_	,	tensor_type=a_			)










def 	a						(				_UpperCAmelCase							: str						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[Any]													=     53_81
		for x in s:
				__UpperCAmelCase   :					str													=     ((hash_value << 5) + hash_value) + ord(_UpperCAmelCase						)
		return hash_value & 0xFFFF_FFFF





