


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


_snake_case					:		Tuple      =    logging.get_logger(__name__)

_snake_case					:		Optional[Any]      =    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_snake_case					:		List[Any]      =    {
    "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"
        ),
    },
}
_snake_case					:		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"
        ),
    },
}
_snake_case					:		List[Any]      =    {
    "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"
        ),
    },
}

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


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

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							str					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							Dict					  =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							List[str]					  =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							Union[str, Any]					  =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase   :							Optional[Any]					  =  DPRContextEncoderTokenizer

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							str					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							int					  =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							Optional[int]					  =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							Any					  =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase   :							Dict					  =  DPRQuestionEncoderTokenizer
_snake_case					:		List[str]      =    collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


_snake_case					:		List[Any]      =    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(_lowerCAmelCase				)
class     a		:




							"""simple docstring"""


							def __call__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Optional[str] = None   ,     lowerCamelCase	:  Optional[str] = None   ,     lowerCamelCase	:  Union[bool, str] = False   ,     lowerCamelCase	:  Union[bool, str] = False   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     lowerCamelCase	:  Optional[bool] = None   ,     **lowerCamelCase	:  List[str]   ,     )     ->   BatchEncoding:
														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:
																					__snake_case		:      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   ,     )
														__snake_case		:      Any										=       titles if not isinstance(lowerCamelCase   ,     lowerCamelCase    ) else [titles]
														__snake_case		:      Tuple										=       texts if not isinstance(lowerCamelCase   ,     lowerCamelCase    ) else [texts]
														__snake_case		:      Tuple										=       len(lowerCamelCase    )
														__snake_case		:      Optional[int]										=       questions if not isinstance(lowerCamelCase   ,     lowerCamelCase    ) else [questions] * n_passages
														assert len(lowerCamelCase    ) == len(
														    lowerCamelCase    ), F'There should be as many titles than texts but got {len(lowerCamelCase    )} titles and {len(lowerCamelCase    )} texts.'
														__snake_case		:      Dict										=       super().__call__(lowerCamelCase   ,     lowerCamelCase   ,     padding=lowerCamelCase   ,     truncation=lowerCamelCase    )["input_ids"]
														__snake_case		:      List[str]										=       super().__call__(lowerCamelCase   ,     add_special_tokens=lowerCamelCase   ,     padding=lowerCamelCase   ,     truncation=lowerCamelCase    )["input_ids"]
														__snake_case		:      Dict										=       {
														    "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:
																					__snake_case		:      Optional[int]										=       []
																					for input_ids in encoded_inputs["input_ids"]:
																												attention_mask.append([int(input_id != self.pad_token_id    ) for input_id in input_ids]    )
																					__snake_case		:      Any										=       attention_mask
														return self.pad(lowerCamelCase   ,     padding=lowerCamelCase   ,     max_length=lowerCamelCase   ,     return_tensors=lowerCamelCase    )

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

																					__snake_case		:      str										=       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    __snake_case (							self	:  str   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  int   ,     )     ->   List[DPRSpanPrediction]:
														__snake_case		:      Any										=       []
														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)    )
														__snake_case		:      str										=       sorted(lowerCamelCase   ,     key=lambda lowerCamelCase    : x[1]   ,     reverse=lowerCamelCase    )
														__snake_case		:      int										=       []
														for (start_index, end_index), score in scores:
																					assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
																					__snake_case		:      str										=       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(lowerCamelCase    ) == top_spans:
																												break
														return chosen_span_intervals

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase					,    _lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							Union[str, Any]					  =  READER_PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							Any					  =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							Optional[Any]					  =  READER_PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase   :							Union[str, Any]					  =  ["input_ids", "attention_mask"]
							__UpperCAmelCase   :							str					  =  DPRReaderTokenizer





from collections import Counter

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

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       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]))

