

'''simple docstring'''







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__lowerCamelCase						    =      logging.get_logger(__name__)






class    A__ :



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = question_encoder
           A_		   = generator
           A_		   = self.question_encoder



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           if os.path.isfile(__A    ):
                  raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''    )
           os.makedirs(__A						,			exist_ok=__A    )
           A_		   = os.path.join(__A						,			"""question_encoder_tokenizer"""    )
           A_		   = os.path.join(__A						,			"""generator_tokenizer"""    )
           self.question_encoder.save_pretrained(__A    )
           self.generator.save_pretrained(__A    )



    @classmethod
    def    snake_case_     (   cls						,			UpperCamelCase__						,			**UpperCamelCase__    )  -> int:





           '''simple docstring'''
           # dynamically import AutoTokenizer
           from ..auto.tokenization_auto import AutoTokenizer

           A_		   = kwargs.pop("""config"""						,			__A    )

           if config is None:
                  A_		   = RagConfig.from_pretrained(__A    )

           A_		   = AutoTokenizer.from_pretrained(
               __A						,			config=config.question_encoder						,			subfolder="""question_encoder_tokenizer"""    )
           A_		   = AutoTokenizer.from_pretrained(
               __A						,			config=config.generator						,			subfolder="""generator_tokenizer"""    )
           return cls(question_encoder=__A						,			generator=__A    )



    def __call__(   self						,			*UpperCamelCase__						,			**UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           return self.current_tokenizer(*__A						,			**__A    )



    def    snake_case_     (   self						,			*UpperCamelCase__						,			**UpperCamelCase__    )  -> Any:





           '''simple docstring'''
           return self.generator.batch_decode(*__A						,			**__A    )



    def    snake_case_     (   self						,			*UpperCamelCase__						,			**UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           return self.generator.decode(*__A						,			**__A    )



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           A_		   = self.question_encoder



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = self.generator







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = "longest"						,			UpperCamelCase__ = None						,			UpperCamelCase__ = True						,			**UpperCamelCase__						,			)  -> BatchEncoding:





           '''simple docstring'''
           warnings.warn(
               """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
               """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
               """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
               """details"""						,			__A						,			)
           if max_length is None:
                  A_		   = self.current_tokenizer.model_max_length
           A_		   = self(
               __A						,			add_special_tokens=__A						,			return_tensors=__A						,			max_length=__A						,			padding=__A						,			truncation=__A						,			**__A						,			)
           if tgt_texts is None:
                  return model_inputs
           # Process tgt_texts
           if max_target_length is None:
                  A_		   = self.current_tokenizer.model_max_length
           A_		   = self(
               text_target=__A						,			add_special_tokens=__A						,			return_tensors=__A						,			padding=__A						,			max_length=__A						,			truncation=__A						,			**__A						,			)
           A_		   = labels["""input_ids"""]
           return model_inputs



'''simple docstring'''







import requests

__lowerCamelCase						    =      ''''''  # <-- Put your OpenWeatherMap appid here!
__lowerCamelCase						    =      '''https://api.openweathermap.org/data/2.5/'''
def  UpperCAmelCase__      ( UpperCAmelCase__ = "Chicago",    UpperCAmelCase__ = APPID			)     ->     dict:
       return requests.get(URL_BASE + """weather""",    params=locals()			).json()
def  UpperCAmelCase__      ( UpperCAmelCase__ = "Kolkata, India",    UpperCAmelCase__ = APPID			)     ->     dict:
       return requests.get(URL_BASE + """forecast""",    params=locals()			).json()
def  UpperCAmelCase__      ( UpperCAmelCase__ = 55.68,    UpperCAmelCase__ = 12.57,    UpperCAmelCase__ = APPID			)     ->     dict:
       return requests.get(URL_BASE + """onecall""",    params=locals()			).json()


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

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