'''simple docstring'''




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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


_a   :	Optional[int] 							=   logging.get_logger(__name__)

_a   :	int 							=   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_a   :	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"
        ),
    },
}
_a   :	List[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"
        ),
    },
}
_a   :	List[str] 							=   {
    "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"
        ),
    },
}

_a   :	Optional[Any] 							=   {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
_a   :	Dict 							=   {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
_a   :	Optional[int] 							=   {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


_a   :	List[Any] 							=   {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_a   :	Any 							=   {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_a   :	str 							=   {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class 		__A       (__magic_name__   ):
       snake_case       :Optional[Any]				    =					VOCAB_FILES_NAMES
       snake_case       :List[Any]				    =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Tuple				    =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Optional[Any]				    =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       snake_case       :List[str]				    =					DPRContextEncoderTokenizer






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					VOCAB_FILES_NAMES
       snake_case       :Union[str, Any]				    =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Dict				    =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Union[str, Any]				    =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       snake_case       :List[Any]				    =					DPRQuestionEncoderTokenizer





_a   :	List[Any] 							=   collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

_a   :	List[str] 							=   collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_a   :	List[str] 							=   R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "






@add_start_docstrings(__magic_name__   )
class 		__A       :






       def __call__(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              if titles is None and texts is None:
                     return super().__call__(
                         UpperCamelCase_				,     padding=UpperCamelCase_				,     truncation=UpperCamelCase_				,     max_length=UpperCamelCase_				,     return_tensors=UpperCamelCase_				,     return_attention_mask=UpperCamelCase_				,     **UpperCamelCase_				,     )
              elif titles is None or texts is None:
                     __UpperCAmelCase  :				Union[str, Any]       				=		titles if texts is None else texts
                     return super().__call__(
                         UpperCamelCase_				,     UpperCamelCase_				,     padding=UpperCamelCase_				,     truncation=UpperCamelCase_				,     max_length=UpperCamelCase_				,     return_tensors=UpperCamelCase_				,     return_attention_mask=UpperCamelCase_				,     **UpperCamelCase_				,     )
              __UpperCAmelCase  :				Union[str, Any]       				=		titles if not isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else [titles]
              __UpperCAmelCase  :				List[Any]       				=		texts if not isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else [texts]
              __UpperCAmelCase  :				Union[str, Any]       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				Dict       				=		questions if not isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else [questions] * n_passages
              assert len(UpperCamelCase_      ) == len(
                  UpperCamelCase_      ), f"""There should be as many titles than texts but got {len(UpperCamelCase_      )} titles and {len(UpperCamelCase_      )} texts."""
              __UpperCAmelCase  :				Dict       				=		super().__call__(UpperCamelCase_				,     UpperCamelCase_				,     padding=UpperCamelCase_				,     truncation=UpperCamelCase_      )["input_ids"]
              __UpperCAmelCase  :				str       				=		super().__call__(UpperCamelCase_				,     add_special_tokens=UpperCamelCase_				,     padding=UpperCamelCase_				,     truncation=UpperCamelCase_      )["input_ids"]
              __UpperCAmelCase  :				List[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(UpperCamelCase_				,     UpperCamelCase_      )
                  ]
              }
              if return_attention_mask is not False:
                     __UpperCAmelCase  :				Optional[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]      )
                     __UpperCAmelCase  :				str       				=		attention_mask
              return self.pad(UpperCamelCase_				,     padding=UpperCamelCase_				,     max_length=UpperCamelCase_				,     return_tensors=UpperCamelCase_      )






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

                     __UpperCAmelCase  :				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=UpperCamelCase_				,     top_spans=UpperCamelCase_				,     )
                     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=UpperCamelCase_				,     start_index=UpperCamelCase_				,     end_index=UpperCamelCase_				,     text=self.decode(sequence_ids[start_index : end_index + 1]      )				,     )      )
                     if len(UpperCamelCase_      ) >= num_spans:
                            break
              return nbest_spans_predictions[:num_spans]







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     ):
              __UpperCAmelCase  :				List[Any]       				=		[]
              for start_index, start_score in enumerate(UpperCamelCase_      ):
                     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)      )
              __UpperCAmelCase  :				List[Any]       				=		sorted(UpperCamelCase_				,     key=lambda UpperCamelCase_      : x[1]				,     reverse=UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for (start_index, end_index), score in scores:
                     assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
                     __UpperCAmelCase  :				Tuple       				=		end_index - start_index + 1
                     assert length <= max_answer_length, 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(UpperCamelCase_      ) == top_spans:
                            break
              return chosen_span_intervals






@add_end_docstrings(__magic_name__   )
class 		__A       (__magic_name__ ,					__magic_name__   ):
       snake_case       :int				    =					VOCAB_FILES_NAMES
       snake_case       :int				    =					READER_PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Dict				    =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Dict				    =					READER_PRETRAINED_INIT_CONFIGURATION
       snake_case       :int				    =					["input_ids", "attention_mask"]
       snake_case       :str				    =					DPRReaderTokenizer







'''simple docstring'''




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_a   :	str 							=   datasets.load_iris()

_a   :	List[Any] 							=   np.array(data["data"])
_a   :	Optional[Any] 							=   np.array(data["target"])
_a   :	Dict 							=   data["target_names"]

_a							,					_a							,					_a							,					_a   :	Any 							=   train_test_split(X, y)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       return np.linalg.norm(np.array(lowerCamelCase__						) - np.array(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=5						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		zip(lowerCamelCase__							,    lowerCamelCase__						)
       # List of distances of all points from the point to be classified
       __UpperCAmelCase  :				int       				=		[]
       for data_point in data:
              __UpperCAmelCase  :				Optional[Any]       				=		euclidean_distance(data_point[0]							,    lowerCamelCase__						)
              distances.append((distance, data_point[1])						)
       # Choosing 'k' points with the least distances.
       __UpperCAmelCase  :				Union[str, Any]       				=		[i[1] for i in sorted(lowerCamelCase__						)[:k]]
       # Most commonly occurring class among them
       # is the class into which the point is classified
       __UpperCAmelCase  :				Dict       				=		Counter(lowerCamelCase__						).most_common(1						)[0][0]
       return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))






