




import requests

lowerCamelCase							:						Union[str, Any]					       =  ""  # <-- Put your OpenWeatherMap appid here!
lowerCamelCase							:						Any					       =  "https://api.openweathermap.org/data/2.5/"

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			str = "Chicago"      ,				lowercase      :			str = APPID	):


      '''simple docstring'''



      return requests.get(URL_BASE + 'weather'      ,				params=locals()	).json()

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			str = "Kolkata, India"      ,				lowercase      :			str = APPID	):


      '''simple docstring'''



      return requests.get(URL_BASE + 'forecast'      ,				params=locals()	).json()

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			float = 55.68      ,				lowercase      :			float = 12.57      ,				lowercase      :			str = APPID	):


      '''simple docstring'''



      return requests.get(URL_BASE + 'onecall'      ,				params=locals()	).json()


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

 while True:
  lowerCamelCase							:						Any					       =  input("Enter a location:").strip()
  if location:
   pprint(current_weather(location))
  else:
   break











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CanineForMultipleChoice""",
					    """CanineForQuestionAnswering""",
					    """CanineForSequenceClassification""",
					    """CanineForTokenClassification""",
					    """CanineLayer""",
					    """CanineModel""",
					    """CaninePreTrainedModel""",
					    """load_tf_weights_in_canine""",
					]


if TYPE_CHECKING:
					from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
					from .tokenization_canine import CanineTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_canine import (
										    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CanineForMultipleChoice,
										    CanineForQuestionAnswering,
										    CanineForSequenceClassification,
										    CanineForTokenClassification,
										    CanineLayer,
										    CanineModel,
										    CaninePreTrainedModel,
										    load_tf_weights_in_canine,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






