






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


UpperCamelCase__ =						logging.get_logger(__name__)

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

UpperCamelCase__ =						{
    "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"
        ),
    },
}
UpperCamelCase__ =						{
    "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"
        ),
    },
}
UpperCamelCase__ =						{
    "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"
        ),
    },
}

UpperCamelCase__ =						{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
UpperCamelCase__ =						{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
UpperCamelCase__ =						{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


UpperCamelCase__ =						{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase__ =						{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase__ =						{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Tuple      =			VOCAB_FILES_NAMES
			lowercase__      :  List[str]      =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowercase__      :  Any      =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase__      :  Any      =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  List[Any]      =			VOCAB_FILES_NAMES
			lowercase__      :  Tuple      =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowercase__      :  Tuple      =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase__      :  List[Any]      =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

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


UpperCamelCase__ =						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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(_UpperCAmelCase			)
class 			_lowerCAmelCase   :






			"""simple docstring"""





			def __call__(				self	:       Optional[Any]			,     lowercase	:       Union[str, Any]			,     lowercase	:       Optional[str] = None			,     lowercase	:       Optional[str] = None			,     lowercase	:       Union[bool, str] = False			,     lowercase	:       Union[bool, str] = False			,     lowercase	:       Optional[int] = None			,     lowercase	:       Optional[Union[str, TensorType]] = None			,     lowercase	:       Optional[bool] = None			,     **lowercase	:       Any			,     )						->					List[Any]:
									"""simple docstring"""







									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:
															__lowercase					=				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			,     )
									__lowercase					=				titles if not isinstance(__UpperCamelCase			,     __UpperCamelCase   ) else [titles]
									__lowercase					=				texts if not isinstance(__UpperCamelCase			,     __UpperCamelCase   ) else [texts]
									__lowercase					=				len(__UpperCamelCase   )
									__lowercase					=				questions if not isinstance(__UpperCamelCase			,     __UpperCamelCase   ) else [questions] * n_passages
									if len(__UpperCamelCase   ) != len(__UpperCamelCase   ):
															raise ValueError(
															    F"There should be as many titles than texts but got {len(__UpperCamelCase   )} titles and {len(__UpperCamelCase   )} texts."   )
									__lowercase					=				super().__call__(__UpperCamelCase			,     __UpperCamelCase			,     padding=__UpperCamelCase			,     truncation=__UpperCamelCase   )["""input_ids"""]
									__lowercase					=				super().__call__(__UpperCamelCase			,     add_special_tokens=__UpperCamelCase			,     padding=__UpperCamelCase			,     truncation=__UpperCamelCase   )["""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(__UpperCamelCase			,     __UpperCamelCase   )
									    ]
									}
									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(__UpperCamelCase			,     padding=__UpperCamelCase			,     max_length=__UpperCamelCase			,     return_tensors=__UpperCamelCase   )





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       BatchEncoding			,     lowercase	:       DPRReaderOutput			,     lowercase	:       int = 16			,     lowercase	:       int = 64			,     lowercase	:       int = 4			,     )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				reader_input["""input_ids"""]
									__lowercase	,			__lowercase	,			__lowercase					=				reader_output[:3]
									__lowercase					=				len(__UpperCamelCase   )
									__lowercase					=				sorted(range(__UpperCamelCase   )			,     reverse=__UpperCamelCase			,     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(__UpperCamelCase   )

															__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=__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	:       Tuple			,     lowercase	:       List[int]			,     lowercase	:       List[int]			,     lowercase	:       int			,     lowercase	:       int			,     )						->					Dict:
									"""simple docstring"""







									__lowercase					=				[]
									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)   )
									__lowercase					=				sorted(__UpperCamelCase			,     key=lambda lowercase   : x[1]			,     reverse=__UpperCamelCase   )
									__lowercase					=				[]
									for (start_index, end_index), score in scores:
															if start_index > end_index:
																					raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"   )
															__lowercase					=				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(__UpperCamelCase   ) == top_spans:
																					break
									return chosen_span_intervals





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






			"""simple docstring"""





			lowercase__      :  Optional[Any]      =			VOCAB_FILES_NAMES
			lowercase__      :  Tuple      =			READER_PRETRAINED_VOCAB_FILES_MAP
			lowercase__      :  Union[str, Any]      =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase__      :  List[str]      =			READER_PRETRAINED_INIT_CONFIGURATION
			lowercase__      :  str      =			["""input_ids""", """attention_mask"""]










UpperCamelCase__ =						{
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.355_818,
}






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
												__lowercase					=				(
												    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
												    F"Valid values are: {', '.join(lowercase__   )}"
												)
												raise ValueError(lowercase__   )
						return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





