
'''simple docstring'''
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""laion/clap-htsat-unfused"""
				a_      				=						tempfile.mkdtemp()




			def 					lowerCAmelCase__					(       self					,   **UpperCAmelCase	):
				return RobertaTokenizer.from_pretrained(self.checkpoint					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   **UpperCAmelCase	):
				return ClapFeatureExtractor.from_pretrained(self.checkpoint					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				shutil.rmtree(self.tmpdirname	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_tokenizer()
				a_      				=						self.get_feature_extractor()

				a_      				=						ClapProcessor(tokenizer=UpperCAmelCase					,   feature_extractor=UpperCAmelCase	)

				processor.save_pretrained(self.tmpdirname	)
				a_      				=						ClapProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor.tokenizer.get_vocab()					,   tokenizer.get_vocab()	)
				self.assertIsInstance(processor.tokenizer					,   UpperCAmelCase	)

				self.assertEqual(processor.feature_extractor.to_json_string()					,   feature_extractor.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						ClapProcessor(tokenizer=self.get_tokenizer()					,   feature_extractor=self.get_feature_extractor()	)
				processor.save_pretrained(self.tmpdirname	)

				a_      				=						self.get_tokenizer(bos_token="""(BOS)"""					,   eos_token="""(EOS)"""	)
				a_      				=						self.get_feature_extractor(do_normalize=UpperCAmelCase					,   padding_value=1.0	)

				a_      				=						ClapProcessor.from_pretrained(
				    self.tmpdirname					,   bos_token="""(BOS)"""					,   eos_token="""(EOS)"""					,   do_normalize=UpperCAmelCase					,   padding_value=1.0	)

				self.assertEqual(processor.tokenizer.get_vocab()					,   tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer					,   UpperCAmelCase	)

				self.assertEqual(processor.feature_extractor.to_json_string()					,   feature_extractor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_feature_extractor()
				a_      				=						self.get_tokenizer()

				a_      				=						ClapProcessor(tokenizer=UpperCAmelCase					,   feature_extractor=UpperCAmelCase	)

				a_      				=						floats_list((3, 10_00)	)

				a_      				=						feature_extractor(UpperCAmelCase					,   return_tensors="""np"""	)
				a_      				=						processor(audios=UpperCAmelCase					,   return_tensors="""np"""	)

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()					,   input_processor[key].sum()					,   delta=1e-2	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_feature_extractor()
				a_      				=						self.get_tokenizer()

				a_      				=						ClapProcessor(tokenizer=UpperCAmelCase					,   feature_extractor=UpperCAmelCase	)

				a_      				=						"""This is a test string"""

				a_      				=						processor(text=UpperCAmelCase	)

				a_      				=						tokenizer(UpperCAmelCase	)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]					,   encoded_processor[key]	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_feature_extractor()
				a_      				=						self.get_tokenizer()

				a_      				=						ClapProcessor(tokenizer=UpperCAmelCase					,   feature_extractor=UpperCAmelCase	)

				a_      				=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				a_      				=						processor.batch_decode(UpperCAmelCase	)
				a_      				=						tokenizer.batch_decode(UpperCAmelCase	)

				self.assertListEqual(UpperCAmelCase					,   UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_feature_extractor()
				a_      				=						self.get_tokenizer()

				a_      				=						ClapProcessor(tokenizer=UpperCAmelCase					,   feature_extractor=UpperCAmelCase	)

				self.assertListEqual(
				    processor.model_input_names[2:]					,   feature_extractor.model_input_names					,   msg="""`processor` and `feature_extractor` model input names do not match"""					,   )


'''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
