

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


lowerCamelCase      				=     logging.get_logger(__name__)


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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

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

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

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





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =["""pixel_values"""]




							def __init__( self					,						_A = True					,						_A = None					,						_A = PILImageResampling.BILINEAR					,						_A = True					,						_A = None					,						_A = True					,						_A = 1 / 2_5_5					,						_A = True					,						_A = None					,						_A = None					,						**_A					,						):
												super().__init__(**_A							)
												__lowerCAmelCase					=							size if size is not None else {'shortest_edge': 2_2_4}
												__lowerCAmelCase					=							get_size_dict(_A					,						default_to_square=_A							)
												__lowerCAmelCase					=							crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
												__lowerCAmelCase					=							get_size_dict(_A					,						param_name='crop_size'							)

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




							def        A__  ( self					,						_A					,						_A					,						_A = PILImageResampling.BILINEAR					,						_A = None					,						**_A					,						):
												__lowerCAmelCase					=							get_size_dict(_A					,						default_to_square=_A							)
												if "shortest_edge" in size:
																	__lowerCAmelCase					=							get_resize_output_image_size(_A					,						size['shortest_edge']					,						default_to_square=_A							)
												elif "height" in size and "width" in size:
																	__lowerCAmelCase					=							(size['height'], size['width'])
												else:
																	raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""							)
												return resize(_A					,						size=_A					,						resample=_A					,						data_format=_A					,						**_A							)




							def        A__  ( self					,						_A					,						_A					,						_A = None					,						**_A					,						):
												__lowerCAmelCase					=							get_size_dict(_A							)
												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(_A					,						size=(size['height'], size['width'])					,						data_format=_A					,						**_A							)




							def        A__  ( self					,						_A					,						_A					,						_A = None					,						**_A					,						):
												return rescale(_A					,						scale=_A					,						data_format=_A					,						**_A							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A = None					,						**_A					,						):
												return normalize(_A					,						mean=_A					,						std=_A					,						data_format=_A					,						**_A							)




							def        A__  ( 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					,						):
												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.
												__lowerCAmelCase					=							to_numpy_array(_A							)

												if do_resize:
																	__lowerCAmelCase					=							self.resize(image=_A					,						size=_A					,						resample=_A							)

												if do_center_crop:
																	__lowerCAmelCase					=							self.center_crop(_A					,						size=_A							)

												if do_rescale:
																	__lowerCAmelCase					=							self.rescale(image=_A					,						scale=_A							)

												if do_normalize:
																	__lowerCAmelCase					=							self.normalize(image=_A					,						mean=_A					,						std=_A							)

												__lowerCAmelCase					=							to_channel_dimension_format(_A					,						_A							)
												return image




							def        A__  ( 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					,						):
												__lowerCAmelCase					=							do_resize if do_resize is not None else self.do_resize
												__lowerCAmelCase					=							resample if resample is not None else self.resample
												__lowerCAmelCase					=							do_center_crop if do_center_crop is not None else self.do_center_crop
												__lowerCAmelCase					=							do_rescale if do_rescale is not None else self.do_rescale
												__lowerCAmelCase					=							rescale_factor if rescale_factor is not None else self.rescale_factor
												__lowerCAmelCase					=							do_normalize if do_normalize is not None else self.do_normalize
												__lowerCAmelCase					=							image_mean if image_mean is not None else self.image_mean
												__lowerCAmelCase					=							image_std if image_std is not None else self.image_std

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

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

												__lowerCAmelCase					=							make_batched(_A							)

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

												__lowerCAmelCase					=							{'pixel_values': videos}
												return BatchFeature(data=_A					,						tensor_type=_A							)









def        __lowercase    (  UpperCAmelCase__ = 10		,					UpperCAmelCase__ = 1_000		,					UpperCAmelCase__ = True      ):

					"""simple docstring"""






					assert (
					    isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					    and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					    and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					), "Invalid type of value(s) specified to function!"

					if min_val > max_val:
										raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'      )
					return min_val if option else max_val


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					return int((number_a + number_a) / 2      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert (
					    isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ) and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ) and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					), 'argument values must be type of "int"'

					if lower > higher:
										raise ValueError('argument value for lower and higher must be(lower > higher)'      )

					if not lower < to_guess < higher:
										raise ValueError(
										    'guess value must be within the range of lower and higher value'      )

					def answer(UpperCAmelCase__      ) -> str:
										if number > to_guess:
															return "high"
										elif number < to_guess:
															return "low"
										else:
															return "same"

					print('started...'      )

					__lowerCAmelCase					=							lower
					__lowerCAmelCase					=							higher

					__lowerCAmelCase					=							[]

					while True:
										__lowerCAmelCase					=							get_avg(UpperCAmelCase__		,					UpperCAmelCase__      )
										last_numbers.append(UpperCAmelCase__      )

										if answer(UpperCAmelCase__      ) == "low":
															__lowerCAmelCase					=							number
										elif answer(UpperCAmelCase__      ) == "high":
															__lowerCAmelCase					=							number
										else:
															break

					print(F"""guess the number : {last_numbers[-1]}"""      )
					print(F"""details : {last_numbers!s}"""      )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							int(input('Enter lower value : '      ).strip()      )
					__lowerCAmelCase					=							int(input('Enter high value : '      ).strip()      )
					__lowerCAmelCase					=							int(input('Enter value to guess : '      ).strip()      )
					guess_the_number(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )


if __name__ == "__main__":
		main()






