






"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

A__			:							List[Any]					   =   logging.get_logger(__name__)

def 					_snake_case							(	lowerCamelCase__      :       Optional[int]    )							->      List[List[ImageInput]]:
							if isinstance(__snake_case							,		(list, tuple)    ) and isinstance(videos[0]							,		(list, tuple)    ) and is_valid_image(videos[0][0]    ):
														return videos

							elif isinstance(__snake_case							,		(list, tuple)    ) and is_valid_image(videos[0]    ):
														return [videos]

							elif is_valid_image(__snake_case    ):
														return [[videos]]

							raise ValueError(F"""Could not make batched video from {videos}"""    )







class   lowercase__     (				__SCREAMING_SNAKE_CASE  ):
			_UpperCAmelCase						:Any       =					["pixel_values"]


			def __init__( self	:    int  ,			snake_case__	:    bool = True  ,			snake_case__	:    Dict[str, int] = None  ,			snake_case__	:    PILImageResampling = PILImageResampling.BILINEAR  ,			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__	:    bool = True  ,			snake_case__	:    Optional[Union[float, List[float]]] = None  ,			snake_case__	:    Optional[Union[float, List[float]]] = None  ,			**snake_case__	:    List[str]  ,			):
										super().__init__(**_SCREAMING_SNAKE_CASE   )
										lowerCamelCase_			:				Any										=size if size is not None else {"shortest_edge": 256}
										lowerCamelCase_			:				str										=get_size_dict(_SCREAMING_SNAKE_CASE  ,			default_to_square=_SCREAMING_SNAKE_CASE   )
										lowerCamelCase_			:				Optional[int]										=crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase_			:				Optional[Any]										=get_size_dict(_SCREAMING_SNAKE_CASE  ,			param_name="crop_size"   )

										lowerCamelCase_			:				Optional[int]										=do_resize
										lowerCamelCase_			:				Tuple										=size
										lowerCamelCase_			:				int										=do_center_crop
										lowerCamelCase_			:				str										=crop_size
										lowerCamelCase_			:				List[str]										=resample
										lowerCamelCase_			:				Dict										=do_rescale
										lowerCamelCase_			:				Optional[Any]										=rescale_factor
										lowerCamelCase_			:				List[str]										=offset
										lowerCamelCase_			:				str										=do_normalize
										lowerCamelCase_			:				Any										=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowerCamelCase_			:				str										=image_std if image_std is not None else IMAGENET_STANDARD_STD


			def       UpperCAmelCase__		( self	:    List[str]  ,			snake_case__	:    np.ndarray  ,			snake_case__	:    Dict[str, int]  ,			snake_case__	:    PILImageResampling = PILImageResampling.BILINEAR  ,			snake_case__	:    Optional[Union[str, ChannelDimension]] = None  ,			**snake_case__	:    Dict  ,			):
										lowerCamelCase_			:				List[str]										=get_size_dict(_SCREAMING_SNAKE_CASE  ,			default_to_square=_SCREAMING_SNAKE_CASE   )
										if "shortest_edge" in size:
																	lowerCamelCase_			:				int										=get_resize_output_image_size(_SCREAMING_SNAKE_CASE  ,			size["shortest_edge"]  ,			default_to_square=_SCREAMING_SNAKE_CASE   )
										elif "height" in size and "width" in size:
																	lowerCamelCase_			:				Tuple										=(size["height"], size["width"])
										else:
																	raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""   )
										return resize(_SCREAMING_SNAKE_CASE  ,			size=_SCREAMING_SNAKE_CASE  ,			resample=_SCREAMING_SNAKE_CASE  ,			data_format=_SCREAMING_SNAKE_CASE  ,			**_SCREAMING_SNAKE_CASE   )


			def       UpperCAmelCase__		( self	:    int  ,			snake_case__	:    np.ndarray  ,			snake_case__	:    Dict[str, int]  ,			snake_case__	:    Optional[Union[str, ChannelDimension]] = None  ,			**snake_case__	:    List[str]  ,			):
										lowerCamelCase_			:				List[Any]										=get_size_dict(_SCREAMING_SNAKE_CASE   )
										if "height" not in size or "width" not in size:
																	raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""   )
										return center_crop(_SCREAMING_SNAKE_CASE  ,			size=(size["height"], size["width"])  ,			data_format=_SCREAMING_SNAKE_CASE  ,			**_SCREAMING_SNAKE_CASE   )


			def       UpperCAmelCase__		( self	:    Any  ,			snake_case__	:    np.ndarray  ,			snake_case__	:    Union[int, float]  ,			snake_case__	:    bool = True  ,			snake_case__	:    Optional[Union[str, ChannelDimension]] = None  ,			**snake_case__	:    List[Any]  ,			):
										lowerCamelCase_			:				Union[str, Any]										=image.astype(np.floataa   )
										if offset:
																	lowerCamelCase_			:				Tuple										=image - (scale / 2)
										return rescale(_SCREAMING_SNAKE_CASE  ,			scale=_SCREAMING_SNAKE_CASE  ,			data_format=_SCREAMING_SNAKE_CASE  ,			**_SCREAMING_SNAKE_CASE   )


			def       UpperCAmelCase__		( self	:    int  ,			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__	:    Dict  ,			):
										return normalize(_SCREAMING_SNAKE_CASE  ,			mean=_SCREAMING_SNAKE_CASE  ,			std=_SCREAMING_SNAKE_CASE  ,			data_format=_SCREAMING_SNAKE_CASE  ,			**_SCREAMING_SNAKE_CASE   )


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

										if do_resize and size is None or resample is None:
																	raise ValueError("Size and resample 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."   )

										if offset and not do_rescale:
																	raise ValueError("For offset, do_rescale must also be set to True."   )

										# All transformations expect numpy arrays.
										lowerCamelCase_			:				Tuple										=to_numpy_array(_SCREAMING_SNAKE_CASE   )

										if do_resize:
																	lowerCamelCase_			:				Dict										=self.resize(image=_SCREAMING_SNAKE_CASE  ,			size=_SCREAMING_SNAKE_CASE  ,			resample=_SCREAMING_SNAKE_CASE   )

										if do_center_crop:
																	lowerCamelCase_			:				Tuple										=self.center_crop(_SCREAMING_SNAKE_CASE  ,			size=_SCREAMING_SNAKE_CASE   )

										if do_rescale:
																	lowerCamelCase_			:				Optional[Any]										=self.rescale(image=_SCREAMING_SNAKE_CASE  ,			scale=_SCREAMING_SNAKE_CASE  ,			offset=_SCREAMING_SNAKE_CASE   )

										if do_normalize:
																	lowerCamelCase_			:				Any										=self.normalize(image=_SCREAMING_SNAKE_CASE  ,			mean=_SCREAMING_SNAKE_CASE  ,			std=_SCREAMING_SNAKE_CASE   )

										lowerCamelCase_			:				Any										=to_channel_dimension_format(_SCREAMING_SNAKE_CASE  ,			_SCREAMING_SNAKE_CASE   )
										return image


			def       UpperCAmelCase__		( self	:    Tuple  ,			snake_case__	:    ImageInput  ,			snake_case__	:    bool = None  ,			snake_case__	:    Dict[str, int] = None  ,			snake_case__	:    PILImageResampling = None  ,			snake_case__	:    bool = None  ,			snake_case__	:    Dict[str, int] = None  ,			snake_case__	:    bool = None  ,			snake_case__	:    float = None  ,			snake_case__	:    bool = None  ,			snake_case__	:    bool = None  ,			snake_case__	:    Optional[Union[float, List[float]]] = None  ,			snake_case__	:    Optional[Union[float, List[float]]] = None  ,			snake_case__	:    Optional[Union[str, TensorType]] = None  ,			snake_case__	:    ChannelDimension = ChannelDimension.FIRST  ,			**snake_case__	:    Dict  ,			):
										lowerCamelCase_			:				Optional[int]										=do_resize if do_resize is not None else self.do_resize
										lowerCamelCase_			:				Optional[Any]										=resample if resample is not None else self.resample
										lowerCamelCase_			:				Tuple										=do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase_			:				int										=do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase_			:				List[Any]										=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase_			:				Union[str, Any]										=offset if offset is not None else self.offset
										lowerCamelCase_			:				List[Any]										=do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase_			:				Dict										=image_mean if image_mean is not None else self.image_mean
										lowerCamelCase_			:				List[Any]										=image_std if image_std is not None else self.image_std

										lowerCamelCase_			:				Any										=size if size is not None else self.size
										lowerCamelCase_			:				str										=get_size_dict(_SCREAMING_SNAKE_CASE  ,			default_to_square=_SCREAMING_SNAKE_CASE   )
										lowerCamelCase_			:				Optional[Any]										=crop_size if crop_size is not None else self.crop_size
										lowerCamelCase_			:				Optional[Any]										=get_size_dict(_SCREAMING_SNAKE_CASE  ,			param_name="crop_size"   )

										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."   )

										lowerCamelCase_			:				Optional[int]										=make_batched(_SCREAMING_SNAKE_CASE   )

										lowerCamelCase_			:				Union[str, Any]										=[
										    [
										        self._preprocess_image(
										            image=_SCREAMING_SNAKE_CASE  ,			do_resize=_SCREAMING_SNAKE_CASE  ,			size=_SCREAMING_SNAKE_CASE  ,			resample=_SCREAMING_SNAKE_CASE  ,			do_center_crop=_SCREAMING_SNAKE_CASE  ,			crop_size=_SCREAMING_SNAKE_CASE  ,			do_rescale=_SCREAMING_SNAKE_CASE  ,			rescale_factor=_SCREAMING_SNAKE_CASE  ,			offset=_SCREAMING_SNAKE_CASE  ,			do_normalize=_SCREAMING_SNAKE_CASE  ,			image_mean=_SCREAMING_SNAKE_CASE  ,			image_std=_SCREAMING_SNAKE_CASE  ,			data_format=_SCREAMING_SNAKE_CASE  ,			)
										        for img in video
										    ]
										    for video in videos
										]

										lowerCamelCase_			:				Optional[int]										={"pixel_values": videos}
										return BatchFeature(data=_SCREAMING_SNAKE_CASE  ,			tensor_type=_SCREAMING_SNAKE_CASE   )








"""simple docstring"""






# Function to print upper half of diamond (pyramid)


def      _lowercase							(    __snake_case      )		->  Dict:
			for i in range(0   ,__snake_case      ):
						for _ in range(0   ,n - i - 1      ):  # printing spaces
									print(" "   ,end=""      )
						for _ in range(0   ,i + 1      ):  # printing stars
									print("* "   ,end=""      )
						print()


def      _lowercase							(    __snake_case      )		->  Tuple:
			for i in range(__snake_case   ,0   ,-1      ):
						for _ in range(__snake_case   ,0   ,-1      ):  # printing stars
									print("* "   ,end=""      )
						print()
						for _ in range(n - i + 1   ,0   ,-1      ):  # printing spaces
									print(" "   ,end=""      )


def      _lowercase							(    __snake_case      )		->  Any:
			if n <= 0:
						print("       ...       ....        nothing printing :("      )
						return
			floyd(__snake_case      )  # upper half
			reverse_floyd(__snake_case      )  # lower half


if __name__ == "__main__":
	print(R'| /\ | |- |  |-  |--| |\  /| |-')
	print(R'|/  \| |- |_ |_  |__| | \/ | |_')
	__snake_case				:  Tuple =       1
	while K:
		__snake_case				:  List[str] =       int(input('enter the number and , and see the magic : '))
		print()
		pretty_print(user_number)
		__snake_case				:  Optional[Any] =       int(input('press 0 to exit... and 1 to continue...'))

	print('Good Bye...')