

'''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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


__lowerCamelCase						    =      logging.get_logger(__name__)


if is_vision_available():
      import PIL


class    A__ ( _snake_case     ):
    lowercase		   =						["pixel_values"]



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





           '''simple docstring'''
           super().__init__(**UpperCamelCase__    )
           A_		   = size if size is not None else {"""shortest_edge""": 224}
           A_		   = get_size_dict(UpperCamelCase__						,			default_to_square=UpperCamelCase__    )
           A_		   = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           A_		   = get_size_dict(UpperCamelCase__						,			default_to_square=UpperCamelCase__						,			param_name="""crop_size"""    )

           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 if image_mean is not None else OPENAI_CLIP_MEAN
           A_		   = image_std if image_std is not None else OPENAI_CLIP_STD
           A_		   = do_convert_rgb



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = PILImageResampling.BICUBIC						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> np.ndarray:





           '''simple docstring'''
           A_		   = get_size_dict(UpperCamelCase__						,			default_to_square=UpperCamelCase__    )
           if "shortest_edge" not in size:
                  raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
           A_		   = get_resize_output_image_size(UpperCamelCase__						,			size=size["""shortest_edge"""]						,			default_to_square=UpperCamelCase__    )
           return resize(UpperCamelCase__						,			size=UpperCamelCase__						,			resample=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> np.ndarray:





           '''simple docstring'''
           A_		   = get_size_dict(UpperCamelCase__    )
           if "height" not in size or "width" not in size:
                  raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''    )
           return center_crop(UpperCamelCase__						,			size=(size["""height"""], size["""width"""])						,			data_format=UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> int:





           '''simple docstring'''
           return rescale(UpperCamelCase__						,			scale=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> np.ndarray:





           '''simple docstring'''
           return normalize(UpperCamelCase__						,			mean=UpperCamelCase__						,			std=UpperCamelCase__						,			data_format=UpperCamelCase__						,			**UpperCamelCase__    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = ChannelDimension.FIRST						,			**UpperCamelCase__						,			)  -> PIL.Image.Image:





           '''simple docstring'''
           A_		   = do_resize if do_resize is not None else self.do_resize
           A_		   = size if size is not None else self.size
           A_		   = get_size_dict(UpperCamelCase__						,			param_name="""size"""						,			default_to_square=UpperCamelCase__    )
           A_		   = resample if resample is not None else self.resample
           A_		   = do_center_crop if do_center_crop is not None else self.do_center_crop
           A_		   = crop_size if crop_size is not None else self.crop_size
           A_		   = get_size_dict(UpperCamelCase__						,			param_name="""crop_size"""						,			default_to_square=UpperCamelCase__    )
           A_		   = do_rescale if do_rescale is not None else self.do_rescale
           A_		   = rescale_factor if rescale_factor is not None else self.rescale_factor
           A_		   = do_normalize if do_normalize is not None else self.do_normalize
           A_		   = image_mean if image_mean is not None else self.image_mean
           A_		   = image_std if image_std is not None else self.image_std
           A_		   = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           A_		   = make_list_of_images(UpperCamelCase__    )

           if not valid_images(UpperCamelCase__    ):
                  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_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."""    )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                  A_		   = [convert_to_rgb(UpperCamelCase__    ) for image in images]

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

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

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

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

           if do_normalize:
                  A_		   = [self.normalize(image=UpperCamelCase__						,			mean=UpperCamelCase__						,			std=UpperCamelCase__    ) for image in images]

           A_		   = [to_channel_dimension_format(UpperCamelCase__						,			UpperCamelCase__    ) for image in images]

           A_		   = {"""pixel_values""": images}
           return BatchFeature(data=UpperCamelCase__						,			tensor_type=UpperCamelCase__    )



'''simple docstring'''







import requests

__lowerCamelCase						    =      ''''''  # <-- Put your OpenWeatherMap appid here!
__lowerCamelCase						    =      '''https://api.openweathermap.org/data/2.5/'''
def  UpperCAmelCase__      ( UpperCAmelCase__ = "Chicago",    UpperCAmelCase__ = APPID			)     ->     dict:
       return requests.get(URL_BASE + """weather""",    params=locals()			).json()
def  UpperCAmelCase__      ( UpperCAmelCase__ = "Kolkata, India",    UpperCAmelCase__ = APPID			)     ->     dict:
       return requests.get(URL_BASE + """forecast""",    params=locals()			).json()
def  UpperCAmelCase__      ( UpperCAmelCase__ = 55.68,    UpperCAmelCase__ = 12.57,    UpperCAmelCase__ = APPID			)     ->     dict:
       return requests.get(URL_BASE + """onecall""",    params=locals()			).json()


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

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