





"""simple docstring"""



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
       import datasets






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @slow
 @require_torch
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''					,						'''prajjwal1/bert-tiny'''  )
     __A    :						Optional[int]						 =     BertTokenizer.from_pretrained('''bert-base-uncased'''  )

     __A    :						Optional[Any]						 =     bertabert.config.encoder.vocab_size
     __A    :						str						 =     tokenizer.sep_token_id
     __A    :						List[str]						 =     tokenizer.cls_token_id
     __A    :						str						 =     128

     __A    :						Union[str, Any]						 =     datasets.load_dataset('''cnn_dailymail'''					,						'''3.0.0'''					,						split='''train[:1%]'''  )
     __A    :						List[Any]						 =     datasets.load_dataset('''cnn_dailymail'''					,						'''3.0.0'''					,						split='''validation[:1%]'''  )

     __A    :						List[str]						 =     train_dataset.select(range(32  )  )
     __A    :						Optional[int]						 =     val_dataset.select(range(16  )  )

     __A    :						List[Any]						 =     4

     def _map_to_encoder_decoder_inputs(__lowerCamelCase  ):
         # Tokenizer will automatically set [BOS] <text> [EOS]
         __A    :						int						 =     tokenizer(batch['''article''']					,						padding='''max_length'''					,						truncation=__lowerCamelCase					,						max_length=512  )
         __A    :						List[str]						 =     tokenizer(batch['''highlights''']					,						padding='''max_length'''					,						truncation=__lowerCamelCase					,						max_length=128  )
         __A    :						Dict						 =     inputs.input_ids
         __A    :						List[Any]						 =     inputs.attention_mask

         __A    :						List[Any]						 =     outputs.input_ids
         __A    :						Optional[Any]						 =     outputs.input_ids.copy()
         __A    :						int						 =     [
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
         ]
         __A    :						Dict						 =     outputs.attention_mask

         assert all(len(__lowerCamelCase  ) == 512 for x in inputs.input_ids  )
         assert all(len(__lowerCamelCase  ) == 128 for x in outputs.input_ids  )

         return batch

     def _compute_metrics(__lowerCamelCase  ):
         __A    :						int						 =     pred.label_ids
         __A    :						int						 =     pred.predictions

         # all unnecessary tokens are removed
         __A    :						List[str]						 =     tokenizer.batch_decode(__lowerCamelCase					,						skip_special_tokens=__lowerCamelCase  )
         __A    :						Optional[Any]						 =     tokenizer.batch_decode(__lowerCamelCase					,						skip_special_tokens=__lowerCamelCase  )

         __A    :						Any						 =     sum([int(pred_str[i] == label_str[i]  ) for i in range(len(__lowerCamelCase  )  )]  ) / len(__lowerCamelCase  )

         return {"accuracy": accuracy}

     # map train dataset
     __A    :						Optional[int]						 =     train_dataset.map(
         _map_to_encoder_decoder_inputs					,						batched=__lowerCamelCase					,						batch_size=__lowerCamelCase					,						remove_columns=['''article''', '''highlights''']					,						)
     train_dataset.set_format(
         type='''torch'''					,						columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']					,						)

     # same for validation dataset
     __A    :						List[Any]						 =     val_dataset.map(
         _map_to_encoder_decoder_inputs					,						batched=__lowerCamelCase					,						batch_size=__lowerCamelCase					,						remove_columns=['''article''', '''highlights''']					,						)
     val_dataset.set_format(
         type='''torch'''					,						columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']					,						)

     __A    :						Any						 =     self.get_auto_remove_tmp_dir()

     __A    :						Union[str, Any]						 =     SeqaSeqTrainingArguments(
         output_dir=__lowerCamelCase					,						per_device_train_batch_size=__lowerCamelCase					,						per_device_eval_batch_size=__lowerCamelCase					,						predict_with_generate=__lowerCamelCase					,						evaluation_strategy='''steps'''					,						do_train=__lowerCamelCase					,						do_eval=__lowerCamelCase					,						warmup_steps=0					,						eval_steps=2					,						logging_steps=2					,						)

     # instantiate trainer
     __A    :						Optional[Any]						 =     SeqaSeqTrainer(
         model=__lowerCamelCase					,						args=__lowerCamelCase					,						compute_metrics=_compute_metrics					,						train_dataset=__lowerCamelCase					,						eval_dataset=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						)

     # start training
     trainer.train()











"""simple docstring"""



import requests

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







def 		__lowercase     (   snake_case_  :  str = "Chicago"				,snake_case_  :  str = APPID						)   ->dict:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  str = "Kolkata, India"				,snake_case_  :  str = APPID						)   ->dict:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  float = 55.68				,snake_case_  :  float = 12.57				,snake_case_  :  str = APPID						)   ->dict:



    '''simple docstring'''







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


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

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




