




'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_						:      str       =						logging.get_logger(__name__)

class        _a    (						__lowerCAmelCase							):
					SCREAMING_SNAKE_CASE_							:   str       =						["""pixel_values"""]






					def __init__(			self ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = 1 / 255 ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = 8 ,**_SCREAMING_SNAKE_CASE ,)							->      None:
										super().__init__(**_SCREAMING_SNAKE_CASE   )

										_snake_case							=				do_rescale
										_snake_case							=				rescale_factor
										_snake_case							=				do_pad
										_snake_case							=				pad_size






					def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE   )							->      np.ndarray:
										return rescale(_SCREAMING_SNAKE_CASE ,scale=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






					def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[Any]:
										_snake_case							, _snake_case							=				get_image_size(_SCREAMING_SNAKE_CASE   )
										_snake_case							=				(old_height // size + 1) * size - old_height
										_snake_case							=				(old_width // size + 1) * size - old_width

										return pad(_SCREAMING_SNAKE_CASE ,((0, pad_height), (0, pad_width)) ,mode="symmetric" ,data_format=_SCREAMING_SNAKE_CASE   )






					def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,**_SCREAMING_SNAKE_CASE ,)							->      str:
										_snake_case							=				do_rescale if do_rescale is not None else self.do_rescale
										_snake_case							=				rescale_factor if rescale_factor is not None else self.rescale_factor
										_snake_case							=				do_pad if do_pad is not None else self.do_pad
										_snake_case							=				pad_size if pad_size is not None else self.pad_size

										_snake_case							=				make_list_of_images(_SCREAMING_SNAKE_CASE   )

										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."   )

										if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True."   )

										# All transformations expect numpy arrays.
										_snake_case							=				[to_numpy_array(_SCREAMING_SNAKE_CASE   ) for image in images]

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

										if do_pad:
															_snake_case							=				[self.pad(_SCREAMING_SNAKE_CASE ,size=_SCREAMING_SNAKE_CASE   ) for image in images]

										_snake_case							=				[to_channel_dimension_format(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) for image in images]

										_snake_case							=				{"pixel_values": images}
										return BatchFeature(data=_SCREAMING_SNAKE_CASE ,tensor_type=_SCREAMING_SNAKE_CASE   )








'''simple docstring'''





import pprint

import requests

UpperCamelCase_						:      Tuple       =						'''https://zenquotes.io/api'''





def      __a     (       )						->			list:

     """simple docstring"""




     return requests.get(API_ENDPOINT_URL + "/today"   ).json()





def      __a     (       )						->			list:

     """simple docstring"""




     return requests.get(API_ENDPOINT_URL + "/random"   ).json()


if __name__ == "__main__":
   UpperCamelCase_						:      Any       =						random_quotes()
   pprint.pprint(response)


