


'''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


__lowercase	:						List[Any]       =	logging.get_logger(__name__)





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    ["pixel_values"]




	def __init__(   self						,				__a = True						,				__a = None						,				__a = PILImageResampling.BICUBIC						,				__a = True						,				__a = 1 / 255						,				__a = True						,				__a = None						,				__a = None						,				__a = True						,				**__a						,				):

						'''simple docstring'''





						super().__init__(**__a							)
						__a			:       int					    =  size if size is not None else {'height': 384, 'width': 384}
						__a			:       Any					    =  get_size_dict(__a						,				default_to_square=__a							)

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




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a = PILImageResampling.BICUBIC						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						__a			:       str					    =  get_size_dict(__a						,				default_to_square=__a							)
						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()}"""							)
						__a			:       Optional[Any]					    =  (size['height'], size['width'])
						return resize(__a						,				size=__a						,				resample=__a						,				data_format=__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a = None						,				**__a						,				):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a = None						,				**__a						,				):

						'''simple docstring'''





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





	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						,				**__a						,				):

						'''simple docstring'''





						__a			:       str					    =  do_resize if do_resize is not None else self.do_resize
						__a			:       Tuple					    =  resample if resample is not None else self.resample
						__a			:       Dict					    =  do_rescale if do_rescale is not None else self.do_rescale
						__a			:       Optional[int]					    =  rescale_factor if rescale_factor is not None else self.rescale_factor
						__a			:       str					    =  do_normalize if do_normalize is not None else self.do_normalize
						__a			:       Optional[int]					    =  image_mean if image_mean is not None else self.image_mean
						__a			:       Any					    =  image_std if image_std is not None else self.image_std
						__a			:       str					    =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						__a			:       Any					    =  size if size is not None else self.size
						__a			:       Union[str, Any]					    =  get_size_dict(__a						,				default_to_square=__a							)

						__a			:       Union[str, Any]					    =  make_list_of_images(__a							)

						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.'							)

						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:
											__a			:       List[Any]					    =  [convert_to_rgb(__a							) for image in images]

						# All transformations expect numpy arrays.
						__a			:       int					    =  [to_numpy_array(__a							) for image in images]

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

						if do_rescale:
											__a			:       Union[str, Any]					    =  [self.rescale(image=__a						,				scale=__a							) for image in images]

						if do_normalize:
											__a			:       List[str]					    =  [self.normalize(image=__a						,				mean=__a						,				std=__a							) for image in images]

						__a			:       Tuple					    =  [to_channel_dimension_format(__a						,				__a							) for image in images]

						__a			:       int					    =  BatchFeature(data={'pixel_values': images}						,				tensor_type=__a							)

						return encoded_outputs







'''simple docstring'''



import requests

__lowercase	:						Tuple       =	''  # <-- Put your OpenWeatherMap appid here!
__lowercase	:						Tuple       =	'https://api.openweathermap.org/data/2.5/'






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str = "Chicago"						,      _SCREAMING_SNAKE_CASE      :   str = APPID   ):
					return requests.get(URL_BASE + 'weather'						,      params=locals()   ).json()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str = "Kolkata, India"						,      _SCREAMING_SNAKE_CASE      :   str = APPID   ):
					return requests.get(URL_BASE + 'forecast'						,      params=locals()   ).json()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   float = 5_5.6_8						,      _SCREAMING_SNAKE_CASE      :   float = 1_2.5_7						,      _SCREAMING_SNAKE_CASE      :   str = APPID   ):
					return requests.get(URL_BASE + 'onecall'						,      params=locals()   ).json()


if __name__ == "__main__":
	from pprint import pprint

	while True:
		__lowercase	:						Dict       =	input('Enter a location:').strip()
		if location:
			pprint(current_weather(location))
		else:
			break



