






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       :       List[str]        =				logging.get_logger(__name__)

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

lowerCamelCase       :       Union[str, 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'
        ),
    },
}
lowerCamelCase       :       int        =				{
    '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       :       Dict        =				{
    '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       :       List[Any]        =				{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
lowerCamelCase       :       List[str]        =				{
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
lowerCamelCase       :       Any        =				{
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


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

class 						lowerCAmelCase   (       UpperCamelCase_	):

					'''simple docstring'''

					_A  :	Tuple     =					VOCAB_FILES_NAMES
					_A  :	str     =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					_A  :	Any     =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A  :	int     =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 						lowerCAmelCase   (       UpperCamelCase_	):

					'''simple docstring'''

					_A  :	Tuple     =					VOCAB_FILES_NAMES
					_A  :	Optional[int]     =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					_A  :	List[str]     =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A  :	Dict     =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







lowerCamelCase       :       List[Any]        =				collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowerCamelCase       :       Any        =				collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowerCamelCase       :       Dict        =				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 :		str						,   __a :		str						,   __a :		Optional[str] = None						,   __a :		Optional[str] = None						,   __a :		Union[bool, str] = False						,   __a :		Union[bool, str] = False						,   __a :		Optional[int] = None						,   __a :		Optional[Union[str, TensorType]] = None						,   __a :		Optional[bool] = None						,   **__a :		List[Any]						,   )	->     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					:							str			=   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					:							Dict			=   titles if not isinstance(UpperCamelCase__						,   UpperCamelCase__							) else [titles]
												__lowercase					:							Optional[Any]			=   texts if not isinstance(UpperCamelCase__						,   UpperCamelCase__							) else [texts]
												__lowercase					:							Tuple			=   len(UpperCamelCase__							)
												__lowercase					:							List[str]			=   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					:							Optional[int]			=   super().__call__(UpperCamelCase__						,   UpperCamelCase__						,   padding=UpperCamelCase__						,   truncation=UpperCamelCase__							)['''input_ids''']
												__lowercase					:							List[str]			=   super().__call__(UpperCamelCase__						,   add_special_tokens=UpperCamelCase__						,   padding=UpperCamelCase__						,   truncation=UpperCamelCase__							)['''input_ids''']
												__lowercase					:							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(UpperCamelCase__						,   UpperCamelCase__							)
												    ]
												}
												if return_attention_mask is not False:
																			__lowercase					:							List[Any]			=   []
																			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					:							List[str]			=   attention_mask
												return self.pad(UpperCamelCase__						,   padding=UpperCamelCase__						,   max_length=UpperCamelCase__						,   return_tensors=UpperCamelCase__							)



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		BatchEncoding						,   __a :		DPRReaderOutput						,   __a :		int = 16						,   __a :		int = 64						,   __a :		int = 4						,   )	->     int:


												"""simple docstring"""

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

																			__lowercase					:							Optional[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=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 					lowerCAmelCase (     self :		List[str]						,   __a :		List[int]						,   __a :		List[int]						,   __a :		int						,   __a :		int						,   )	->     int:


												"""simple docstring"""

												__lowercase					:							Dict			=   []
												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					:							Dict			=   sorted(UpperCamelCase__						,   key=lambda __a							: x[1]						,   reverse=UpperCamelCase__							)
												__lowercase					:							Dict			=   []
												for (start_index, end_index), score in scores:
																			if start_index > end_index:
																										raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"							)
																			__lowercase					:							str			=   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'''

					_A  :	int     =					VOCAB_FILES_NAMES
					_A  :	List[Any]     =					READER_PRETRAINED_VOCAB_FILES_MAP
					_A  :	List[str]     =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A  :	List[str]     =					READER_PRETRAINED_INIT_CONFIGURATION
					_A  :	Optional[Any]     =					['''input_ids''', '''attention_mask''']
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
					import accelerate
def       A	(    _lowercase						):
						if not is_accelerate_available():
												return method
						SCREAMING_SNAKE_CASE							:    int							=							version.parse(accelerate.__version__						).base_version
						if version.parse(_lowercase						) < version.parse('''0.17.0'''						):
												return method

						def wrapper(self		,  *_lowercase		,  **_lowercase						):
												if hasattr(self		,  '''_hf_hook'''						) and hasattr(self._hf_hook		,  '''pre_forward'''						):
																		self._hf_hook.pre_forward(self						)
												return method(self		,  *_lowercase		,  **_lowercase						)

						return wrapper





