



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE_:List[Any]					=						500_000

SCREAMING_SNAKE_CASE_:List[Any]					=						os.path.split(__file__)
SCREAMING_SNAKE_CASE_:int					=						os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))




@get_duration
def      __UpperCamelCase     (  _lowerCAmelCase				,					**_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	List[Any]										=       dataset.map(**_lowerCAmelCase		)




@get_duration
def      __UpperCamelCase     (  _lowerCAmelCase				,					**_lowerCAmelCase		)	->  Optional[Any]:



    """simple docstring"""






    A			:	str										=       dataset.filter(**_lowerCAmelCase		)




def      __UpperCamelCase     (  )	->  Dict:



    """simple docstring"""






    A			:	Union[str, Any]										=       {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        A			:	Union[str, Any]										=       datasets.Features({"""text""": datasets.Value("""string"""		), """numbers""": datasets.Value("""float32"""		)}		)
        A			:	List[Any]										=       generate_example_dataset(
            os.path.join(_lowerCAmelCase				,					"""dataset.arrow"""		)				,					_lowerCAmelCase				,					num_examples=_lowerCAmelCase		)

        A			:	Optional[int]										=       transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""				,					use_fast=_lowerCAmelCase		)

        def tokenize(_lowerCAmelCase		):
            return tokenizer(examples["""text"""]		)

        A			:	List[Any]										=       map(_lowerCAmelCase		)

        A			:	str										=       map(_lowerCAmelCase				,					batched=_lowerCAmelCase		)

        A			:	List[str]										=       map(_lowerCAmelCase				,					function=lambda _lowerCAmelCase		: None				,					batched=_lowerCAmelCase		)

        with dataset.formatted_as(type="""numpy"""		):
            A			:	Optional[int]										=       map(_lowerCAmelCase				,					function=lambda _lowerCAmelCase		: None				,					batched=_lowerCAmelCase		)

        with dataset.formatted_as(type="""pandas"""		):
            A			:	Tuple										=       map(_lowerCAmelCase				,					function=lambda _lowerCAmelCase		: None				,					batched=_lowerCAmelCase		)

        with dataset.formatted_as(type="""torch"""				,					columns="""numbers"""		):
            A			:	str										=       map(_lowerCAmelCase				,					function=lambda _lowerCAmelCase		: None				,					batched=_lowerCAmelCase		)

        with dataset.formatted_as(type="""tensorflow"""				,					columns="""numbers"""		):
            A			:	List[str]										=       map(_lowerCAmelCase				,					function=lambda _lowerCAmelCase		: None				,					batched=_lowerCAmelCase		)

        A			:	Dict										=       map(_lowerCAmelCase				,					function=_lowerCAmelCase				,					batched=_lowerCAmelCase		)

        A			:	Optional[Any]										=       filter(_lowerCAmelCase		)

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_lowerCAmelCase				,					"""wb"""		) as f:
        f.write(json.dumps(_lowerCAmelCase		).encode("""utf-8"""		)		)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_map_filter()






import requests

SCREAMING_SNAKE_CASE_:List[str]					=						""""""  # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE_:Dict					=						"""https://api.openweathermap.org/data/2.5/"""




def      __UpperCamelCase     (  _lowerCAmelCase = "Chicago"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase = "Kolkata, India"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase = 55.68				,					_lowerCAmelCase = 12.57				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






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


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

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


