



"""simple docstring"""







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 convert_to_rgb, 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


if is_vision_available():
							import PIL


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				["pixel_values"]







		def __init__(	self						:  Any			,_snake_case						:  bool = True			,_snake_case						:  Dict[str, int] = None			,_snake_case						:  PILImageResampling = PILImageResampling.BICUBIC			,_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:






				"""simple docstring"""



				super().__init__(**_snake_case						)
				lowercase__   :    List[str]				=			size if size is not None else {'''height''': 384, '''width''': 384}
				lowercase__   :    Optional[Any]				=			get_size_dict(_snake_case			,default_to_square=_snake_case						)

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







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  np.ndarray			,_snake_case						:  Dict[str, int]			,_snake_case						:  PILImageResampling = PILImageResampling.BICUBIC			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Optional[Any]			,)					->		np.ndarray:






				"""simple docstring"""



				lowercase__   :    List[str]				=			get_size_dict(_snake_case			,default_to_square=_snake_case						)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""						)
				lowercase__   :    Tuple				=			(size['''height'''], size['''width'''])
				return resize(_snake_case			,size=_snake_case			,resample=_snake_case			,data_format=_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  np.ndarray			,_snake_case						:  Union[int, float]			,_snake_case						:  Optional[Union[str, ChannelDimension]] = None			,**_snake_case						:  Optional[Any]			,)					->		int:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Any			,_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						:  Union[str, Any]			,)					->		np.ndarray:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  ImageInput			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[Dict[str, int]] = None			,_snake_case						:  PILImageResampling = None			,_snake_case						:  Optional[bool] = None			,_snake_case						:  Optional[float] = None			,_snake_case						:  Optional[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						:  bool = None			,_snake_case						:  ChannelDimension = ChannelDimension.FIRST			,**_snake_case						:  Dict			,)					->		PIL.Image.Image:






				"""simple docstring"""



				lowercase__   :    List[str]				=			do_resize if do_resize is not None else self.do_resize
				lowercase__   :    Tuple				=			resample if resample is not None else self.resample
				lowercase__   :    int				=			do_rescale if do_rescale is not None else self.do_rescale
				lowercase__   :    List[str]				=			rescale_factor if rescale_factor is not None else self.rescale_factor
				lowercase__   :    Dict				=			do_normalize if do_normalize is not None else self.do_normalize
				lowercase__   :    Tuple				=			image_mean if image_mean is not None else self.image_mean
				lowercase__   :    str				=			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__   :    List[Any]				=			size if size is not None else self.size
				lowercase__   :    str				=			get_size_dict(_snake_case			,default_to_square=_snake_case						)

				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 or resample is None:
						raise ValueError('''Size and resample must be specified if do_resize 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__   :    List[str]				=			[convert_to_rgb(_snake_case						) for image in images]

				# All transformations expect numpy arrays.
				lowercase__   :    List[Any]				=			[to_numpy_array(_snake_case						) for image in images]

				if do_resize:
						lowercase__   :    Optional[Any]				=			[self.resize(image=_snake_case			,size=_snake_case			,resample=_snake_case						) for image in images]

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

				if do_normalize:
						lowercase__   :    Dict				=			[self.normalize(image=_snake_case			,mean=_snake_case			,std=_snake_case						) for image in images]

				lowercase__   :    List[str]				=			[to_channel_dimension_format(_snake_case			,_snake_case						) for image in images]

				lowercase__   :    Optional[Any]				=			BatchFeature(data={'''pixel_values''': images}			,tensor_type=_snake_case						)

				return encoded_outputs





"""simple docstring"""







def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		if not isinstance(__lowerCamelCase						,     __lowerCamelCase	):
				raise ValueError('''iterations must be defined as integers'''	)
		if not isinstance(__lowerCamelCase						,     __lowerCamelCase	) or not number >= 1:
				raise ValueError(
				    '''starting number must be
                         and integer and be more than 0'''	)
		if not iterations >= 1:
				raise ValueError('''Iterations must be done more than 0 times to play FizzBuzz'''	)

		lowercase__   :    Tuple				=			''''''
		while number <= iterations:
				if number % 3 == 0:
						out += "Fizz"
				if number % 5 == 0:
						out += "Buzz"
				if 0 not in (number % 3, number % 5):
						out += str(__lowerCamelCase	)

				# print(out)
				number += 1
				out += " "
		return out


if __name__ == "__main__":
							import doctest

							doctest.testmod()
