





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_							=			logging.get_logger(__name__)

snake_case_							=			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_							=			{
    '''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_							=			{
    '''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_							=			{
    '''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_							=			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
snake_case_							=			{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
snake_case_							=			{
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


snake_case_							=			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
snake_case_							=			{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
snake_case_							=			{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class        snake_case_		(			_A):
		lowerCamelCase    :Tuple						=     VOCAB_FILES_NAMES
		lowerCamelCase    :str						=     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase    :Dict						=     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase    :Tuple						=     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase    :Optional[Any]						=     DPRContextEncoderTokenizer

class        snake_case_		(			_A):
		lowerCamelCase    :Tuple						=     VOCAB_FILES_NAMES
		lowerCamelCase    :str						=     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase    :Dict						=     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase    :str						=     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase    :Optional[Any]						=     DPRQuestionEncoderTokenizer

snake_case_							=			collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

snake_case_							=			collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


snake_case_							=			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)

    Return:
        `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(_A)
class        snake_case_		:







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







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase = 1_6			,     __lowercase = 6_4			,     __lowercase = 4			,     )				->   List[Any]:
				lowerCamelCase   : int								=reader_input['''input_ids''']
				lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : int								=reader_output[:3]
				lowerCamelCase   : Union[str, Any]								=len(__lowercase   )
				lowerCamelCase   : Any								=sorted(range(__lowercase   )			,     reverse=__lowercase			,     key=relevance_logits.__getitem__   )
				lowerCamelCase   : Union[str, Any]								=[]
				for doc_id in sorted_docs:
						lowerCamelCase   : Union[str, Any]								=list(input_ids[doc_id]   )
						# assuming question & title information is at the beginning of the sequence
						lowerCamelCase   : Optional[Any]								=sequence_ids.index(self.sep_token_id			,     2   ) + 1  # second sep id
						if sequence_ids[-1] == self.pad_token_id:
								lowerCamelCase   : int								=sequence_ids.index(self.pad_token_id   )
						else:
								lowerCamelCase   : int								=len(__lowercase   )

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







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     )				->   str:
				lowerCamelCase   : Optional[int]								=[]
				for start_index, start_score in enumerate(__lowercase   ):
						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)   )
				lowerCamelCase   : Dict								=sorted(__lowercase			,     key=lambda __lowercase   : x[1]			,     reverse=__lowercase   )
				lowerCamelCase   : int								=[]
				for (start_index, end_index), score in scores:
						assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
						lowerCamelCase   : Union[str, Any]								=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(__lowercase   ) == top_spans:
								break
				return chosen_span_intervals

@add_end_docstrings(_A)
class        snake_case_		(			_A						,					_A):
		lowerCamelCase    :Tuple						=     VOCAB_FILES_NAMES
		lowerCamelCase    :Tuple						=     READER_PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase    :Optional[Any]						=     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase    :Optional[int]						=     READER_PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase    :Dict						=     ["input_ids", "attention_mask"]
		lowerCamelCase    :Optional[int]						=     DPRReaderTokenizer








import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     tuple:
		return (data["data"], data["target"])


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     np.ndarray:
		lowerCamelCase   : Dict								=XGBRegressor(verbosity=0							,       random_state=4_2		)
		xgb.fit(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Predict target for test data
		lowerCamelCase   : List[str]								=xgb.predict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : List[Any]								=predictions.reshape(len(SCREAMING_SNAKE_CASE_		)							,       1		)
		return predictions


def 					A__	(       )   ->     None:
		lowerCamelCase   : Union[str, Any]								=fetch_california_housing()
		lowerCamelCase		,   lowerCamelCase   : Optional[Any]								=data_handling(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : str								=train_test_split(
		    SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       test_size=0.2_5							,       random_state=1		)
		lowerCamelCase   : str								=xgboost(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Error printing
		print(F"Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}"		)
		print(F"Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}"		)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

