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		:       str									=     logging.get_logger(__name__)





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""pixel_values"""]


						def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Optional[Dict[str, int]] = None    ,		UpperCamelCase__	:				PILImageResampling = PILImageResampling.BILINEAR    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Dict[str, int] = None    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Union[int, float] = 1 / 255    ,		UpperCamelCase__	:				bool = True    ,		UpperCamelCase__	:				Optional[Union[float, List[float]]] = None    ,		UpperCamelCase__	:				Optional[Union[float, List[float]]] = None    ,		**UpperCamelCase__	:				Optional[int]    ,		):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							size if size is not None else {'''shortest_edge''': 256}
												SCREAMING_SNAKE_CASE							:    Dict							=							get_size_dict(UpperCamelCase__    ,		default_to_square=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Any							=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
												SCREAMING_SNAKE_CASE							:    Any							=							get_size_dict(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							do_resize
												SCREAMING_SNAKE_CASE							:    Dict							=							size
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							resample
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							do_center_crop
												SCREAMING_SNAKE_CASE							:    Dict							=							crop_size
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							do_rescale
												SCREAMING_SNAKE_CASE							:    Any							=							rescale_factor
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							do_normalize
												SCREAMING_SNAKE_CASE							:    Any							=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE							:    str							=							image_std if image_std is not None else IMAGENET_STANDARD_STD


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				np.ndarray    ,		UpperCamelCase__	:				Dict[str, int]    ,		UpperCamelCase__	:				PILImageResampling = PILImageResampling.BICUBIC    ,		UpperCamelCase__	:				Optional[Union[str, ChannelDimension]] = None    ,		**UpperCamelCase__	:				Optional[int]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							get_size_dict(UpperCamelCase__    ,		default_to_square=UpperCamelCase__						)
												if "shortest_edge" not in size:
																		raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
												SCREAMING_SNAKE_CASE							:    int							=							get_resize_output_image_size(UpperCamelCase__    ,		size=size['''shortest_edge''']    ,		default_to_square=UpperCamelCase__						)
												return resize(UpperCamelCase__    ,		size=UpperCamelCase__    ,		resample=UpperCamelCase__    ,		data_format=UpperCamelCase__    ,		**UpperCamelCase__						)


						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				np.ndarray    ,		UpperCamelCase__	:				Dict[str, int]    ,		UpperCamelCase__	:				Optional[Union[str, ChannelDimension]] = None    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							get_size_dict(UpperCamelCase__						)
												return center_crop(UpperCamelCase__    ,		size=(size['''height'''], size['''width'''])    ,		data_format=UpperCamelCase__    ,		**UpperCamelCase__						)


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				np.ndarray    ,		UpperCamelCase__	:				float    ,		UpperCamelCase__	:				Optional[Union[str, ChannelDimension]] = None    ,		**UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





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


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				np.ndarray    ,		UpperCamelCase__	:				Union[float, List[float]]    ,		UpperCamelCase__	:				Union[float, List[float]]    ,		UpperCamelCase__	:				Optional[Union[str, ChannelDimension]] = None    ,		**UpperCamelCase__	:				int    ,		):





												'''simple docstring'''





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



						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				ImageInput    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				Dict[str, int] = None    ,		UpperCamelCase__	:				PILImageResampling = None    ,		UpperCamelCase__	:				bool = None    ,		UpperCamelCase__	:				Dict[str, int] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				Optional[float] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				Optional[Union[float, List[float]]] = None    ,		UpperCamelCase__	:				Optional[Union[float, List[float]]] = None    ,		UpperCamelCase__	:				Optional[Union[str, TensorType]] = None    ,		UpperCamelCase__	:				Union[str, ChannelDimension] = ChannelDimension.FIRST    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE							:    Tuple							=							size if size is not None else self.size
												SCREAMING_SNAKE_CASE							:    Any							=							get_size_dict(UpperCamelCase__    ,		default_to_square=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE							:    str							=							do_center_crop if do_center_crop is not None else self.do_center_crop
												SCREAMING_SNAKE_CASE							:    List[str]							=							crop_size if crop_size is not None else self.crop_size
												SCREAMING_SNAKE_CASE							:    List[str]							=							get_size_dict(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE							:    Dict							=							rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE							:    List[Any]							=							image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE							:    str							=							image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							make_list_of_images(UpperCamelCase__						)

												if not valid_images(UpperCamelCase__						):
																		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.
												SCREAMING_SNAKE_CASE							:    List[Any]							=							[to_numpy_array(UpperCamelCase__						) for image in images]

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

												if do_center_crop:
																		SCREAMING_SNAKE_CASE							:    Any							=							[self.center_crop(image=UpperCamelCase__    ,		size=UpperCamelCase__						) for image in images]

												if do_rescale:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							[self.rescale(image=UpperCamelCase__    ,		scale=UpperCamelCase__						) for image in images]

												if do_normalize:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							[self.normalize(image=UpperCamelCase__    ,		mean=UpperCamelCase__    ,		std=UpperCamelCase__						) for image in images]

												SCREAMING_SNAKE_CASE							:    int							=							[to_channel_dimension_format(UpperCamelCase__    ,		UpperCamelCase__						) for image in images]

												SCREAMING_SNAKE_CASE							:    List[Any]							=							{'''pixel_values''': images}
												return BatchFeature(data=UpperCamelCase__    ,		tensor_type=UpperCamelCase__						)






def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





