
import requests

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



def        UpperCamelCase    (							lowerCAmelCase__ = "Chicago" ,			lowerCAmelCase__ = APPID		):
					'''simple docstring'''
					return requests.get(URL_BASE + '''weather''' ,			params=locals()		).json()



def        UpperCamelCase    (							lowerCAmelCase__ = "Kolkata, India" ,			lowerCAmelCase__ = APPID		):
					'''simple docstring'''
					return requests.get(URL_BASE + '''forecast''' ,			params=locals()		).json()



def        UpperCamelCase    (							lowerCAmelCase__ = 55.68 ,			lowerCAmelCase__ = 12.57 ,			lowerCAmelCase__ = APPID		):
					'''simple docstring'''
					return requests.get(URL_BASE + '''onecall''' ,			params=locals()		).json()


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

							while True:
														lowercase__		:List[Any]   =    input("Enter a location:").strip()
														if location:
																					pprint(current_weather(location))
														else:
																					break











import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   List[str]:


      '''simple docstring'''


      return field(default_factory=lambda: default ,						metadata=_SCREAMING_SNAKE_CASE	)



@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	List[str]			=			list_field(
         default=[]							,      metadata={
             "help": (
                 "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                 " of all available models"
             )
         }							,      )

     __snake_case    :	List[int]			=			list_field(
         default=[8]							,      metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}   )

     __snake_case    :	List[int]			=			list_field(
         default=[8, 32, 128, 512]							,      metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}							,      )

     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Use FP16 to accelerate inference."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Benchmark training of model"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Verbose memory tracing"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }							,      )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Trace memory line by line"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save result to a CSV file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save all print statements in a log file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to print environment information"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                 " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                 " for debugging / testing and on TPU."
             )
         }							,      )
     __snake_case    :	str			=			field(
         default=F"inference_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"inference_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"env_info_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving environment information."}							,      )
     __snake_case    :	str			=			field(
         default=F"log_{round(time()   )}.csv"							,      metadata={"help": "Log filename used if print statements are saved in log."}							,      )
     __snake_case    :	int			=			field(default=3							,      metadata={"help": "Times an experiment will be run."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                 " model weights."
             )
         }							,      )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Dict:




           '''simple docstring'''


           warnings.warn(
               F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
               """ are deprecated in general and it is advised to use external Benchmarking libraries """
               """ to benchmark Transformer models."""						,lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Any:




           '''simple docstring'''


           return json.dumps(dataclasses.asdict(self       )						,indent=2       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           if len(self.models       ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""       )
           return self.models






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		int:




           '''simple docstring'''


           if not self.multi_process:
                 return False
           elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""       )
                 return False
           else:
                 return True


