
'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Optional[int]       =    'Speech2TextFeatureExtractor'
			lowerCamelCase__	:			Dict       =    'Speech2TextTokenizer'




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				super().__init__(UpperCAmelCase					,   UpperCAmelCase	)
				a_      				=						self.feature_extractor
				a_      				=						False




			def __call__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				# For backward compatibility
				if self._in_target_context_manager:
					return self.current_processor(*UpperCAmelCase					,   **UpperCAmelCase	)

				if "raw_speech" in kwargs:
					warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""	)
					a_      				=						kwargs.pop("""raw_speech"""	)
				else:
					a_      				=						kwargs.pop("""audio"""					,   UpperCAmelCase	)
				a_      				=						kwargs.pop("""sampling_rate"""					,   UpperCAmelCase	)
				a_      				=						kwargs.pop("""text"""					,   UpperCAmelCase	)
				if len(UpperCAmelCase	) > 0:
					a_      				=						args[0]
					a_      				=						args[1:]

				if audio is None and text is None:
					raise ValueError("""You need to specify either an `audio` or `text` input to process."""	)

				if audio is not None:
					a_      				=						self.feature_extractor(UpperCAmelCase					,   *UpperCAmelCase					,   sampling_rate=UpperCAmelCase					,   **UpperCAmelCase	)
				if text is not None:
					a_      				=						self.tokenizer(UpperCAmelCase					,   **UpperCAmelCase	)

				if text is None:
					return inputs
				elif audio is None:
					return encodings
				else:
					a_      				=						encodings["""input_ids"""]
					return inputs




			def 					lowerCAmelCase__					(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				return self.tokenizer.batch_decode(*UpperCAmelCase					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				return self.tokenizer.decode(*UpperCAmelCase					,   **UpperCAmelCase	)






			@contextmanager
			def 					lowerCAmelCase__					(       self	):
				warnings.warn(
				    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
				    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
				    """your audio inputs, or in a separate call."""	)
				a_      				=						True
				a_      				=						self.tokenizer
				yield
				a_      				=						self.feature_extractor
				a_      				=						False


'''simple docstring'''
import requests

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






def      UpperCamelCase_				(      A__ = "Chicago"  ,      A__ = APPID						):
	return requests.get(URL_BASE + """weather"""  ,      params=locals()						).json()






def      UpperCamelCase_				(      A__ = "Kolkata, India"  ,      A__ = APPID						):
	return requests.get(URL_BASE + """forecast"""  ,      params=locals()						).json()






def      UpperCamelCase_				(      A__ = 55.68  ,      A__ = 12.57  ,      A__ = APPID						):
	return requests.get(URL_BASE + """onecall"""  ,      params=locals()						).json()


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

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