
"""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 import BertTokenizer


lowerCamelCase__        =		logging.get_logger(__name__)

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

lowerCamelCase__        =		{
    """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"""
        ),
    },
}
lowerCamelCase__        =		{
    """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"""
        ),
    },
}
lowerCamelCase__        =		{
    """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"""
        ),
    },
}

lowerCamelCase__        =		{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
lowerCamelCase__        =		{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
lowerCamelCase__        =		{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


lowerCamelCase__        =		{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCamelCase__        =		{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCamelCase__        =		{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}




class 			A__	(     _lowerCamelCase):
		A_						:	Union[str, Any]           =     VOCAB_FILES_NAMES
		A_						:	int           =     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		A_						:	List[str]           =     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:	Any           =     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 			A__	(     _lowerCamelCase):
		A_						:	Optional[int]           =     VOCAB_FILES_NAMES
		A_						:	List[Any]           =     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		A_						:	Tuple           =     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:	int           =     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

lowerCamelCase__        =		collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


lowerCamelCase__        =		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(_lowerCamelCase)
class 			A__	:

		def __call__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			if titles is None and texts is None:
				return super().__call__(
				    _SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE	,		return_tensors=_SCREAMING_SNAKE_CASE	,		return_attention_mask=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
			elif titles is None or texts is None:
				__lowerCAmelCase      :						Optional[Any]						      =				titles if texts is None else texts
				return super().__call__(
				    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE	,		return_tensors=_SCREAMING_SNAKE_CASE	,		return_attention_mask=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[Any]						      =				titles if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) else [titles]
			__lowerCAmelCase      :						Optional[Any]						      =				texts if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) else [texts]
			__lowerCAmelCase      :						List[Any]						      =				len(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				questions if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) else [questions] * n_passages
			if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
				raise ValueError(
				    f"There should be as many titles than texts but got {len(_SCREAMING_SNAKE_CASE			)} titles and {len(_SCREAMING_SNAKE_CASE			)} texts."			)
			__lowerCAmelCase      :						Tuple						      =				super().__call__(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE			)['input_ids']
			__lowerCAmelCase      :						Optional[int]						      =				super().__call__(_SCREAMING_SNAKE_CASE	,		add_special_tokens=_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		truncation=_SCREAMING_SNAKE_CASE			)['input_ids']
			__lowerCAmelCase      :						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(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			    ]
			}
			if return_attention_mask is not False:
				__lowerCAmelCase      :						Tuple						      =				[]
				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      :						int						      =				attention_mask
			return self.pad(_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		max_length=_SCREAMING_SNAKE_CASE	,		return_tensors=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 16	,		_SCREAMING_SNAKE_CASE = 64	,		_SCREAMING_SNAKE_CASE = 4	,		):
			__lowerCAmelCase      :						Any						      =				reader_input['input_ids']
			__lowerCAmelCase      :						Tuple						      =				reader_output[:3]
			__lowerCAmelCase      :						Tuple						      =				len(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				sorted(range(_SCREAMING_SNAKE_CASE			)	,		reverse=_SCREAMING_SNAKE_CASE	,		key=relevance_logits.__getitem__			)
			__lowerCAmelCase      :						List[DPRReaderOutput]						      =				[]
			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[int]						      =				sequence_ids.index(self.sep_token_id	,		2			) + 1  # second sep id
				if sequence_ids[-1] == self.pad_token_id:
					__lowerCAmelCase      :						List[str]						      =				sequence_ids.index(self.pad_token_id			)
				else:
					__lowerCAmelCase      :						Any						      =				len(_SCREAMING_SNAKE_CASE			)

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







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						List[Any]						      =				[]
			for start_index, start_score in enumerate(_SCREAMING_SNAKE_CASE			):
				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      :						List[Any]						      =				sorted(_SCREAMING_SNAKE_CASE	,		key=lambda _SCREAMING_SNAKE_CASE			: x[1]	,		reverse=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				[]
			for (start_index, end_index), score in scores:
				if start_index > end_index:
					raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"			)
				__lowerCAmelCase      :						Union[str, Any]						      =				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(_SCREAMING_SNAKE_CASE			) == top_spans:
					break
			return chosen_span_intervals




@add_end_docstrings(_lowerCamelCase)
class 			A__	(     _lowerCamelCase	,					_lowerCamelCase):
		A_						:	Optional[int]           =     VOCAB_FILES_NAMES
		A_						:	int           =     READER_PRETRAINED_VOCAB_FILES_MAP
		A_						:	List[Any]           =     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:	Optional[int]           =     READER_PRETRAINED_INIT_CONFIGURATION
		A_						:	Dict           =     ['input_ids', 'attention_mask']

"""simple docstring"""







import qiskit
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Union[str, Any]						      =				qiskit.Aer.get_backend('aer_simulator'		)

	# Create a Quantum Circuit acting on the q register
	__lowerCAmelCase      :						str						      =				qiskit.QuantumCircuit(_UpperCamelCase	,  _UpperCamelCase		)

	# Map the quantum measurement to the classical bits
	circuit.measure([0]	,  [0]		)

	# Execute the circuit on the simulator
	__lowerCAmelCase      :						Optional[int]						      =				qiskit.execute(_UpperCamelCase	,  _UpperCamelCase	,  shots=1000		)

	# Return the histogram data of the results of the experiment.
	return job.result().get_counts(_UpperCamelCase		)


if __name__ == "__main__":
							print(f'Total count for various states are: {single_qubit_measure(1, 1)}')