
'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
		import PIL

if is_torch_available():
		import torch


UpperCAmelCase__ :			Union[str, Any]											=		logging.get_logger(__name__)






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Any   =  ['pixel_values']





	def __init__(       self      :							List[Any]   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BILINEAR   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Union[int, float] = 1 / 255   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							bool = True   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)
				lowerCAmelCase__										=				size if size is not None else {"shortest_edge": 224}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				lowerCAmelCase__										=				crop_size if crop_size is not None else {"height": 256, "width": 256}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				param_name="crop_size"				)

				lowerCAmelCase__										=				do_resize
				lowerCAmelCase__										=				size
				lowerCAmelCase__										=				resample
				lowerCAmelCase__										=				do_rescale
				lowerCAmelCase__										=				rescale_factor
				lowerCAmelCase__										=				do_center_crop
				lowerCAmelCase__										=				crop_size
				lowerCAmelCase__										=				do_flip_channel_order





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							PILImageResampling = PIL.Image.BILINEAR   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							str   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				if "shortest_edge" not in size:
							raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
				lowerCAmelCase__										=				get_resize_output_image_size(__magic_name__   ,				size=size["shortest_edge"]   ,				default_to_square=__magic_name__				)
				return resize(__magic_name__   ,				size=__magic_name__   ,				resample=__magic_name__   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							int   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__				)
				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()}"""				)
				return center_crop(__magic_name__   ,				size=(size["height"], size["width"])   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Union[int, float]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Optional[int]   ,				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None				):


				"""simple docstring"""

				return flip_channel_order(__magic_name__   ,				data_format=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							ImageInput   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							PILImageResampling = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							float = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Optional[Union[str, TensorType]] = None   ,				__magic_name__      :							ChannelDimension = ChannelDimension.FIRST   ,				**__magic_name__      :							str   ,				):


				"""simple docstring"""

				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_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_center_crop if do_center_crop is not None else self.do_center_crop
				lowerCAmelCase__										=				(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

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

				lowerCAmelCase__										=				make_list_of_images(__magic_name__				)

				if not valid_images(__magic_name__				):
							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:
							raise ValueError("Size 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_center_crop and crop_size is None:
							raise ValueError("Crop size must be specified if do_center_crop is True."				)

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

				if do_resize:
							lowerCAmelCase__										=				[self.resize(image=__magic_name__   ,				size=__magic_name__   ,				resample=__magic_name__				) for image in images]

				if do_center_crop:
							lowerCAmelCase__										=				[self.center_crop(image=__magic_name__   ,				size=__magic_name__				) for image in images]

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

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
							lowerCAmelCase__										=				[self.flip_channel_order(image=__magic_name__				) for image in images]

				lowerCAmelCase__										=				[to_channel_dimension_format(__magic_name__   ,				__magic_name__				) for image in images]

				lowerCAmelCase__										=				{"pixel_values": images}
				return BatchFeature(data=__magic_name__   ,				tensor_type=__magic_name__				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Dict   ,				__magic_name__      :							List[Tuple] = None				):


				"""simple docstring"""

				lowerCAmelCase__										=				outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
							if len(__magic_name__				) != len(__magic_name__				):
										raise ValueError(
										    "Make sure that you pass in as many target sizes as the batch dimension of the logits"				)

							if is_torch_tensor(__magic_name__				):
										lowerCAmelCase__										=				target_sizes.numpy()

							lowerCAmelCase__										=				[]

							for idx in range(len(__magic_name__				)				):
										lowerCAmelCase__										=				torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0				)   ,				size=target_sizes[idx]   ,				mode="bilinear"   ,				align_corners=__magic_name__				)
										lowerCAmelCase__										=				resized_logits[0].argmax(dim=0				)
										semantic_segmentation.append(__magic_name__				)
				else:
							lowerCAmelCase__										=				logits.argmax(dim=1				)
							lowerCAmelCase__										=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

				return semantic_segmentation









'''simple docstring'''
import requests
from bsa import BeautifulSoup


def      a (     __a = "AAPL"  )    ->      str:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
					UpperCamelCase__  :Tuple				   =					BeautifulSoup(requests.get(__a  ).text					,   '''html.parser'''  )
					UpperCamelCase__  :Tuple				   =					'''My(6px) Pos(r) smartphone_Mt(6px)'''
					return soup.find('''div'''					,   class_=class_  ).find('''span'''  ).text


if __name__ == "__main__":
		for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
				print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")