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_      =      logging.get_logger(__name__)

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

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


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






class      __lowercase					(    _A    ):
						lowercase					 =     VOCAB_FILES_NAMES
						lowercase					 =     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						lowercase					 =     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase					 =     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						lowercase					 =     DPRContextEncoderTokenizer






class      __lowercase					(    _A    ):
						lowercase					 =     VOCAB_FILES_NAMES
						lowercase					 =     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						lowercase					 =     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase					 =     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						lowercase					 =     DPRQuestionEncoderTokenizer





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

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


A_      =      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(_A    )
class      __lowercase					:

						def __call__(	self				:      Any							,							__lowerCamelCase				:      Optional[Any]							,							__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				:      Dict							,							)							->		BatchEncoding:


									'''simple docstring'''

									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:
												lowercase							=		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							,							)
									lowercase							=		titles if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else [titles]
									lowercase							=		texts if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else [texts]
									lowercase							=		len(__lowerCamelCase   )
									lowercase							=		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.'
									lowercase							=		super().__call__(__lowerCamelCase							,							__lowerCamelCase							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase   )['''input_ids''']
									lowercase							=		super().__call__(__lowerCamelCase							,							add_special_tokens=__lowerCamelCase							,							padding=__lowerCamelCase							,							truncation=__lowerCamelCase   )['''input_ids''']
									lowercase							=		{
									    '''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:
												lowercase							=		[]
												for input_ids in encoded_inputs["input_ids"]:
															attention_mask.append([int(input_id != self.pad_token_id   ) for input_id in input_ids]   )
												lowercase							=		attention_mask
									return self.pad(__lowerCamelCase							,							padding=__lowerCamelCase							,							max_length=__lowerCamelCase							,							return_tensors=__lowerCamelCase   )

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      BatchEncoding							,							__lowerCamelCase				:      DPRReaderOutput							,							__lowerCamelCase				:      int = 16							,							__lowerCamelCase				:      int = 64							,							__lowerCamelCase				:      int = 4							,							)							->		List[DPRSpanPrediction]:


									'''simple docstring'''

									lowercase							=		reader_input['''input_ids''']
									lowercase		,lowercase		,lowercase							=		reader_output[:3]
									lowercase							=		len(__lowerCamelCase   )
									lowercase							=		sorted(range(__lowerCamelCase   )							,							reverse=__lowerCamelCase							,							key=relevance_logits.__getitem__   )
									lowercase							=		[]
									for doc_id in sorted_docs:
												lowercase							=		list(input_ids[doc_id]   )
												# assuming question & title information is at the beginning of the sequence
												lowercase							=		sequence_ids.index(self.sep_token_id							,							2   ) + 1  # second sep id
												if sequence_ids[-1] == self.pad_token_id:
															lowercase							=		sequence_ids.index(self.pad_token_id   )
												else:
															lowercase							=		len(__lowerCamelCase   )

												lowercase							=		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        __a		(	self				:      List[str]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      int							,							)							->		List[DPRSpanPrediction]:


									'''simple docstring'''

									lowercase							=		[]
									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)   )
									lowercase							=		sorted(__lowerCamelCase							,							key=lambda __lowerCamelCase   : x[1]							,							reverse=__lowerCamelCase   )
									lowercase							=		[]
									for (start_index, end_index), score in scores:
												assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
												lowercase							=		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(_A    )
class      __lowercase					(    _A       ,     _A    ):
						lowercase					 =     VOCAB_FILES_NAMES
						lowercase					 =     READER_PRETRAINED_VOCAB_FILES_MAP
						lowercase					 =     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase					 =     READER_PRETRAINED_INIT_CONFIGURATION
						lowercase					 =     ['input_ids', 'attention_mask']
						lowercase					 =     DPRReaderTokenizer






from __future__ import annotations

from math import pi






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       dict[str, float]:



			"""simple docstring"""







			if (inductance, frequency, reactance).count(0       ) != 1:
						raise ValueError('''One and only one argument must be 0'''       )
			if inductance < 0:
						raise ValueError('''Inductance cannot be negative'''       )
			if frequency < 0:
						raise ValueError('''Frequency cannot be negative'''       )
			if reactance < 0:
						raise ValueError('''Inductive reactance cannot be negative'''       )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError('''Exactly one argument must be 0'''       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





