



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


SCREAMING_SNAKE_CASE_:Optional[Any]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:List[str]					=						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
SCREAMING_SNAKE_CASE_:Tuple					=						{
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
SCREAMING_SNAKE_CASE_:List[str]					=						{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
SCREAMING_SNAKE_CASE_:Tuple					=						{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


SCREAMING_SNAKE_CASE_:int					=						{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
SCREAMING_SNAKE_CASE_:Tuple					=						{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
SCREAMING_SNAKE_CASE_:Union[str, Any]					=						{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Tuple =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			Union[str, Any] =				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Any =				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			Union[str, Any] =				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Tuple =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			int =				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Dict =				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			Any =				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


SCREAMING_SNAKE_CASE_:str					=						collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

SCREAMING_SNAKE_CASE_:int					=						collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


SCREAMING_SNAKE_CASE_:str					=						R"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """






@add_start_docstrings(SCREAMING_SNAKE_CASE__   )
class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __call__(	self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        if titles is None and texts is None:
            return super().__call__(
                lowerCamelCase__,		padding=lowerCamelCase__,		truncation=lowerCamelCase__,		max_length=lowerCamelCase__,		return_tensors=lowerCamelCase__,		return_attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        elif titles is None or texts is None:
            A			:	Any										=       titles if texts is None else texts
            return super().__call__(
                lowerCamelCase__,		lowerCamelCase__,		padding=lowerCamelCase__,		truncation=lowerCamelCase__,		max_length=lowerCamelCase__,		return_tensors=lowerCamelCase__,		return_attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        A			:	Any										=       titles if not isinstance(lowerCamelCase__,		lowerCamelCase__		) else [titles]
        A			:	Dict										=       texts if not isinstance(lowerCamelCase__,		lowerCamelCase__		) else [texts]
        A			:	str										=       len(lowerCamelCase__		)
        A			:	int										=       questions if not isinstance(lowerCamelCase__,		lowerCamelCase__		) else [questions] * n_passages
        if len(lowerCamelCase__		) != len(lowerCamelCase__		):
            raise ValueError(
                f'''There should be as many titles than texts but got {len(lowerCamelCase__		)} titles and {len(lowerCamelCase__		)} texts.'''		)
        A			:	Optional[int]										=       super().__call__(lowerCamelCase__,		lowerCamelCase__,		padding=lowerCamelCase__,		truncation=lowerCamelCase__		)["""input_ids"""]
        A			:	str										=       super().__call__(lowerCamelCase__,		add_special_tokens=lowerCamelCase__,		padding=lowerCamelCase__,		truncation=lowerCamelCase__		)["""input_ids"""]
        A			:	str										=       {
            """input_ids""": [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(lowerCamelCase__,		lowerCamelCase__		)
            ]
        }
        if return_attention_mask is not False:
            A			:	Union[str, Any]										=       []
            for input_ids in encoded_inputs["input_ids"]:
                attention_mask.append([int(input_id != self.pad_token_id		) for input_id in input_ids]		)
            A			:	Tuple										=       attention_mask
        return self.pad(lowerCamelCase__,		padding=lowerCamelCase__,		max_length=lowerCamelCase__,		return_tensors=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 16,		lowerCamelCase__ = 64,		lowerCamelCase__ = 4,		):
        A			:	List[Any]										=       reader_input["""input_ids"""]
        A			:	Tuple										=       reader_output[:3]
        A			:	Optional[int]										=       len(lowerCamelCase__		)
        A			:	Optional[int]										=       sorted(range(lowerCamelCase__		),		reverse=lowerCamelCase__,		key=relevance_logits.__getitem__		)
        A			:	List[DPRReaderOutput]										=       []
        for doc_id in sorted_docs:
            A			:	str										=       list(input_ids[doc_id]		)
            # assuming question & title information is at the beginning of the sequence
            A			:	List[Any]										=       sequence_ids.index(self.sep_token_id,		2		) + 1  # second sep id
            if sequence_ids[-1] == self.pad_token_id:
                A			:	int										=       sequence_ids.index(self.pad_token_id		)
            else:
                A			:	Dict										=       len(lowerCamelCase__		)

            A			:	Optional[Any]										=       self._get_best_spans(
                start_logits=start_logits[doc_id][passage_offset:sequence_len],		end_logits=end_logits[doc_id][passage_offset:sequence_len],		max_answer_length=lowerCamelCase__,		top_spans=lowerCamelCase__,		)
            for start_index, end_index in best_spans:
                start_index += passage_offset
                end_index += passage_offset
                nbest_spans_predictions.append(
                    DPRSpanPrediction(
                        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index],		relevance_score=relevance_logits[doc_id],		doc_id=lowerCamelCase__,		start_index=lowerCamelCase__,		end_index=lowerCamelCase__,		text=self.decode(sequence_ids[start_index : end_index + 1]		),		)		)
            if len(lowerCamelCase__		) >= num_spans:
                break
        return nbest_spans_predictions[:num_spans]






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		):
        A			:	Optional[int]										=       []
        for start_index, start_score in enumerate(lowerCamelCase__		):
            for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]		):
                scores.append(((start_index, start_index + answer_length), start_score + end_score)		)
        A			:	List[Any]										=       sorted(lowerCamelCase__,		key=lambda lowerCamelCase__		: x[1],		reverse=lowerCamelCase__		)
        A			:	int										=       []
        for (start_index, end_index), score in scores:
            if start_index > end_index:
                raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''		)
            A			:	List[str]										=       end_index - start_index + 1
            if length > max_answer_length:
                raise ValueError(f'''Span is too long: {length} > {max_answer_length}'''		)
            if any(
                start_index <= prev_start_index <= prev_end_index <= end_index
                or prev_start_index <= start_index <= end_index <= prev_end_index
                for (prev_start_index, prev_end_index) in chosen_span_intervals		):
                continue
            chosen_span_intervals.append((start_index, end_index)		)

            if len(lowerCamelCase__		) == top_spans:
                break
        return chosen_span_intervals






@add_end_docstrings(SCREAMING_SNAKE_CASE__   )
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			int =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			Dict =				READER_PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			List[str] =				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			Dict =				READER_PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase      :			List[str] =				["input_ids", "attention_mask"]






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


