


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCAmelCase			:  Dict     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[Any]					=							["""pixel_values"""]




						def __init__(				self   ,						__lowercase = True   ,						__lowercase = 3_2   ,						__lowercase=PILImageResampling.BILINEAR   ,						__lowercase = True   ,						**__lowercase   ,						)					->    None:
										lowerCAmelCase_ :      List[Any]    				=				do_resize
										lowerCAmelCase_ :      List[Any]    				=				do_rescale
										lowerCAmelCase_ :      Tuple    				=				size_divisor
										lowerCAmelCase_ :      Tuple    				=				resample
										super().__init__(**UpperCamelCase_							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase							)					->    np.ndarray:
										lowerCAmelCase_ :      Any    				=				get_image_size(UpperCamelCase_							)
										# Rounds the height and width down to the closest multiple of size_divisor
										lowerCAmelCase_ :      Union[str, Any]    				=				height // size_divisor * size_divisor
										lowerCAmelCase_ :      Optional[Any]    				=				width // size_divisor * size_divisor
										lowerCAmelCase_ :      Dict    				=				resize(UpperCamelCase_   ,						(new_h, new_w)   ,						resample=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)
										return image




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase							)					->    np.ndarray:
										return rescale(image=UpperCamelCase_   ,						scale=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase=None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = ChannelDimension.FIRST   ,						**__lowercase   ,						)					->    BatchFeature:
										lowerCAmelCase_ :      List[Any]    				=				do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase_ :      Union[str, Any]    				=				do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase_ :      Any    				=				size_divisor if size_divisor is not None else self.size_divisor
										lowerCAmelCase_ :      Dict    				=				resample if resample is not None else self.resample

										if do_resize and size_divisor is None:
														raise ValueError('''size_divisor is required for resizing'''							)

										lowerCAmelCase_ :      Optional[Any]    				=				make_list_of_images(UpperCamelCase_							)

										if not valid_images(UpperCamelCase_							):
														raise ValueError('''Invalid image(s)'''							)

										# All transformations expect numpy arrays.
										lowerCAmelCase_ :      int    				=				[to_numpy_array(UpperCamelCase_							) for img in images]

										if do_resize:
														lowerCAmelCase_ :      str    				=				[self.resize(UpperCamelCase_   ,						size_divisor=UpperCamelCase_   ,						resample=UpperCamelCase_							) for image in images]

										if do_rescale:
														lowerCAmelCase_ :      Optional[int]    				=				[self.rescale(UpperCamelCase_   ,						scale=1 / 2_5_5							) for image in images]

										lowerCAmelCase_ :      Optional[int]    				=				[to_channel_dimension_format(UpperCamelCase_   ,						UpperCamelCase_							) for image in images]

										lowerCAmelCase_ :      int    				=				{'pixel_values': images}
										return BatchFeature(data=UpperCamelCase_   ,						tensor_type=UpperCamelCase_							)



import re



def 			lowerCAmelCase (		lowerCAmelCase_ )-> bool:
				lowerCAmelCase_ :      Tuple    				=				re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
				if match := re.search(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								return match.string == phone
				return False


if __name__ == "__main__":
						print(indian_phone_validator("""+918827897895"""))