
import requests

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






def 				_lowercase				(	UpperCamelCase_ = "Chicago"      ,      UpperCamelCase_ = APPID						)						->						List[str]:
					'''simple docstring'''


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






def 				_lowercase				(	UpperCamelCase_ = "Kolkata, India"      ,      UpperCamelCase_ = APPID						)						->						str:
					'''simple docstring'''


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






def 				_lowercase				(	UpperCamelCase_ = 55.68      ,      UpperCamelCase_ = 12.57      ,      UpperCamelCase_ = APPID						)						->						Tuple:
					'''simple docstring'''


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


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

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







'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				int	=			"""openai/whisper-base"""
 A_				:				str	=			(
     """This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
     """transcribed text."""
 )
 A_				:				Union[str, Any]	=			"""transcriber"""
 A_				:				Tuple	=			WhisperProcessor
 A_				:				Dict	=			WhisperForConditionalGeneration

 A_				:				Tuple	=			["""audio"""]
 A_				:				Optional[int]	=			["""text"""]


 def    _A	(    self  : Optional[int]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  return self.pre_processor(a__			,							return_tensors="pt"    ).input_features


 def    _A	(    self  : List[str]			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  return self.model.generate(inputs=a__    )


 def    _A	(    self  : Optional[Any]			,							a__  : Dict    ):
  '''simple docstring'''

  return self.pre_processor.batch_decode(a__			,							skip_special_tokens=a__    )[0]





