






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


if is_vision_available():
	import PIL


lowercase  							=					logging.get_logger(__name__)





def 						__UpperCAmelCase			(     a_):



							if isinstance(a_							,     (list, tuple)) and isinstance(videos[0]							,     (list, tuple)) and is_valid_image(videos[0][0]):
														return videos

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

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

							raise ValueError(f'''Could not make batched video from {videos}''')




class  UpperCamelCase_      (			UpperCamelCase__				):



	'''simple docstring'''




	lowerCAmelCase													= ["""pixel_values"""]




	def __init__(       self       ,			a = True       ,			a = None       ,			a = PILImageResampling.BILINEAR       ,			a = True       ,			a = None       ,			a = True       ,			a = 1 / 2_55       ,			a = True       ,			a = None       ,			a = None       ,			**a       ,			)       ->    None:
								super().__init__(**lowerCAmelCase__					)
								snake_case_				=							size if size is not None else {"shortest_edge": 2_24}
								snake_case_				=							get_size_dict(lowerCAmelCase__       ,			default_to_square=lowerCAmelCase__					)
								snake_case_				=							crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
								snake_case_				=							get_size_dict(lowerCAmelCase__       ,			param_name='crop_size'					)

								snake_case_				=							do_resize
								snake_case_				=							size
								snake_case_				=							do_center_crop
								snake_case_				=							crop_size
								snake_case_				=							resample
								snake_case_				=							do_rescale
								snake_case_				=							rescale_factor
								snake_case_				=							do_normalize
								snake_case_				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								snake_case_				=							image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a = PILImageResampling.BILINEAR       ,			a = None       ,			**a       ,			)       ->    np.ndarray:
								snake_case_				=							get_size_dict(lowerCAmelCase__       ,			default_to_square=lowerCAmelCase__					)
								if "shortest_edge" in size:
															snake_case_				=							get_resize_output_image_size(lowerCAmelCase__       ,			size['shortest_edge']       ,			default_to_square=lowerCAmelCase__					)
								elif "height" in size and "width" in size:
															snake_case_				=							(size["height"], size["width"])
								else:
															raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
								return resize(lowerCAmelCase__       ,			size=lowerCAmelCase__       ,			resample=lowerCAmelCase__       ,			data_format=lowerCAmelCase__       ,			**lowerCAmelCase__					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a = None       ,			**a       ,			)       ->    np.ndarray:
								snake_case_				=							get_size_dict(lowerCAmelCase__					)
								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(lowerCAmelCase__       ,			size=(size['height'], size['width'])       ,			data_format=lowerCAmelCase__       ,			**lowerCAmelCase__					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a = None       ,			**a       ,			)       ->    int:
								return rescale(lowerCAmelCase__       ,			scale=lowerCAmelCase__       ,			data_format=lowerCAmelCase__       ,			**lowerCAmelCase__					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a = None       ,			**a       ,			)       ->    np.ndarray:
								return normalize(lowerCAmelCase__       ,			mean=lowerCAmelCase__       ,			std=lowerCAmelCase__       ,			data_format=lowerCAmelCase__       ,			**lowerCAmelCase__					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = ChannelDimension.FIRST       ,			)       ->    np.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_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.
								snake_case_				=							to_numpy_array(lowerCAmelCase__					)

								if do_resize:
															snake_case_				=							self.resize(image=lowerCAmelCase__       ,			size=lowerCAmelCase__       ,			resample=lowerCAmelCase__					)

								if do_center_crop:
															snake_case_				=							self.center_crop(lowerCAmelCase__       ,			size=lowerCAmelCase__					)

								if do_rescale:
															snake_case_				=							self.rescale(image=lowerCAmelCase__       ,			scale=lowerCAmelCase__					)

								if do_normalize:
															snake_case_				=							self.normalize(image=lowerCAmelCase__       ,			mean=lowerCAmelCase__       ,			std=lowerCAmelCase__					)

								snake_case_				=							to_channel_dimension_format(lowerCAmelCase__       ,			lowerCAmelCase__					)
								return image






	def 							_UpperCamelCase			(       self       ,			a       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = ChannelDimension.FIRST       ,			**a       ,			)       ->    PIL.Image.Image:
								snake_case_				=							do_resize if do_resize is not None else self.do_resize
								snake_case_				=							resample if resample is not None else self.resample
								snake_case_				=							do_center_crop if do_center_crop is not None else self.do_center_crop
								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_normalize if do_normalize is not None else self.do_normalize
								snake_case_				=							image_mean if image_mean is not None else self.image_mean
								snake_case_				=							image_std if image_std is not None else self.image_std

								snake_case_				=							size if size is not None else self.size
								snake_case_				=							get_size_dict(lowerCAmelCase__       ,			default_to_square=lowerCAmelCase__					)
								snake_case_				=							crop_size if crop_size is not None else self.crop_size
								snake_case_				=							get_size_dict(lowerCAmelCase__       ,			param_name='crop_size'					)

								if not valid_images(lowerCAmelCase__					):
															raise ValueError(
															    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
															    'torch.Tensor, tf.Tensor or jax.ndarray.'					)

								snake_case_				=							make_batched(lowerCAmelCase__					)

								snake_case_				=							[
								    [
								        self._preprocess_image(
								            image=lowerCAmelCase__       ,			do_resize=lowerCAmelCase__       ,			size=lowerCAmelCase__       ,			resample=lowerCAmelCase__       ,			do_center_crop=lowerCAmelCase__       ,			crop_size=lowerCAmelCase__       ,			do_rescale=lowerCAmelCase__       ,			rescale_factor=lowerCAmelCase__       ,			do_normalize=lowerCAmelCase__       ,			image_mean=lowerCAmelCase__       ,			image_std=lowerCAmelCase__       ,			data_format=lowerCAmelCase__       ,			)
								        for img in video
								    ]
								    for video in videos
								]

								snake_case_				=							{"pixel_values": videos}
								return BatchFeature(data=lowerCAmelCase__       ,			tensor_type=lowerCAmelCase__					)












import numpy as np
def      _A       (		SCREAMING_SNAKE_CASE     :   np.array						):





  """simple docstring"""



  return 1 / (1 + np.exp(-vector						))


if __name__ == "__main__":
     import doctest

     doctest.testmod()






