



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:List[Any]					=						"""▁"""

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE_:List[str]					=						{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			int =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			List[Any] =				PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Optional[int] =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			Union[str, Any] =				["input_ids", "attention_mask"]






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__="<s>",		lowerCamelCase__="</s>",		lowerCamelCase__="</s>",		lowerCamelCase__="<s>",		lowerCamelCase__="<unk>",		lowerCamelCase__="<pad>",		lowerCamelCase__="<mask>",		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        # Mask token behave like a normal word, i.e. include the space before it
        A			:	str										=       AddedToken(lowerCamelCase__,		lstrip=lowerCamelCase__,		rstrip=lowerCamelCase__		) if isinstance(lowerCamelCase__,		lowerCamelCase__		) else mask_token

        A			:	Any										=       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCamelCase__,		eos_token=lowerCamelCase__,		unk_token=lowerCamelCase__,		sep_token=lowerCamelCase__,		cls_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		mask_token=lowerCamelCase__,		sp_model_kwargs=self.sp_model_kwargs,		**lowerCamelCase__,		)

        A			:	str										=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(str(lowerCamelCase__		)		)
        A			:	List[Any]										=       vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        A			:	Optional[Any]										=       {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        A			:	int										=       1

        A			:	List[str]										=       len(self.sp_model		) + self.fairseq_offset
        A			:	List[Any]										=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(	self		):
        A			:	Optional[Any]										=       self.__dict__.copy()
        A			:	List[Any]										=       None
        A			:	int										=       self.sp_model.serialized_model_proto()
        return state






    def __setstate__(	self,		lowerCamelCase__		):
        A			:	int										=       d

        # for backward compatibility
        if not hasattr(self,		"""sp_model_kwargs"""		):
            A			:	int										=       {}

        A			:	Optional[Any]										=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):

        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        A			:	int										=       [self.cls_token_id]
        A			:	Any										=       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = False		):

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCamelCase__,		token_ids_a=lowerCamelCase__,		already_has_special_tokens=lowerCamelCase__		)

        if token_ids_a is None:
            return [1] + ([0] * len(lowerCamelCase__		)) + [1]
        return [1] + ([0] * len(lowerCamelCase__		)) + [1, 1] + ([0] * len(lowerCamelCase__		)) + [1]






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A			:	Tuple										=       [self.sep_token_id]
        A			:	Dict										=       [self.cls_token_id]

        if token_ids_a is None:
            return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]






    @property
    def       _lowerCAmelCase (	self		):
        return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       {self.convert_ids_to_tokens(lowerCamelCase__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.sp_model.encode(lowerCamelCase__,		out_type=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        A			:	Any										=       self.sp_model.PieceToId(lowerCamelCase__		)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       """""".join(lowerCamelCase__		).replace(lowerCamelCase__,		""" """		).strip()
        return out_string






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A			:	List[str]										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase__		) and os.path.isfile(self.vocab_file		):
            copyfile(self.vocab_file,		lowerCamelCase__		)
        elif not os.path.isfile(self.vocab_file		):
            with open(lowerCamelCase__,		"""wb"""		) as fi:
                A			:	Dict										=       self.sp_model.serialized_model_proto()
                fi.write(lowerCamelCase__		)

        return (out_vocab_file,)







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


