


"""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


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

UpperCAmelCase    :     Optional[int]   						=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase    :     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'
        ),
    },
}
UpperCAmelCase    :     List[str]   						=		{
    '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    :     Tuple   						=		{
    '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    :     List[Any]   						=		{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
UpperCAmelCase    :     Optional[Any]   						=		{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
UpperCAmelCase    :     str   						=		{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


UpperCAmelCase    :     Tuple   						=		{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
UpperCAmelCase    :     List[Any]   						=		{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
UpperCAmelCase    :     Dict   						=		{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							VOCAB_FILES_NAMES
					__a	       =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					__a	       =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a	       =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							VOCAB_FILES_NAMES
					__a	       =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					__a	       =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a	       =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

UpperCAmelCase    :     Dict   						=		collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


UpperCAmelCase    :     int   						=		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(A				)
class      lowerCamelCase__   :







					"""simple docstring"""


					def __call__(       self    : Any ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : Optional[str] = None ,       UpperCamelCase    : Optional[str] = None ,       UpperCamelCase    : Union[bool, str] = False ,       UpperCamelCase    : Union[bool, str] = False ,       UpperCamelCase    : Optional[int] = None ,       UpperCamelCase    : Optional[Union[str, TensorType]] = None ,       UpperCamelCase    : Optional[bool] = None ,       **UpperCamelCase    : int ,       ):
												'''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:
																			__UpperCAmelCase   :						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 ,       )
												__UpperCAmelCase   :						Any      							=					titles if not isinstance(UpperCamelCase ,       UpperCamelCase  ) else [titles]
												__UpperCAmelCase   :						Optional[int]      							=					texts if not isinstance(UpperCamelCase ,       UpperCamelCase  ) else [texts]
												__UpperCAmelCase   :						int      							=					len(UpperCamelCase  )
												__UpperCAmelCase   :						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.'''  )
												__UpperCAmelCase   :						Optional[Any]      							=					super().__call__(UpperCamelCase ,       UpperCamelCase ,       padding=UpperCamelCase ,       truncation=UpperCamelCase  )["""input_ids"""]
												__UpperCAmelCase   :						Optional[Any]      							=					super().__call__(UpperCamelCase ,       add_special_tokens=UpperCamelCase ,       padding=UpperCamelCase ,       truncation=UpperCamelCase  )["""input_ids"""]
												__UpperCAmelCase   :						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(UpperCamelCase ,       UpperCamelCase  )
												    ]
												}
												if return_attention_mask is not False:
																			__UpperCAmelCase   :						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]  )
																			__UpperCAmelCase   :						Dict      							=					attention_mask
												return self.pad(UpperCamelCase ,       padding=UpperCamelCase ,       max_length=UpperCamelCase ,       return_tensors=UpperCamelCase  )






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : BatchEncoding ,       UpperCamelCase    : DPRReaderOutput ,       UpperCamelCase    : int = 16 ,       UpperCamelCase    : int = 64 ,       UpperCamelCase    : int = 4 ,       ):
												'''simple docstring'''






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

																			__UpperCAmelCase   :						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    : Tuple ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : int ,       UpperCamelCase    : int ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					[]
												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)  )
												__UpperCAmelCase   :						str      							=					sorted(UpperCamelCase ,       key=lambda UpperCamelCase  : x[1] ,       reverse=UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					[]
												for (start_index, end_index), score in scores:
																			if start_index > end_index:
																										raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''  )
																			__UpperCAmelCase   :						Dict      							=					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(A				)
class      lowerCamelCase__   (     A     ,    A				):







					"""simple docstring"""


					__a	       =							VOCAB_FILES_NAMES
					__a	       =							READER_PRETRAINED_VOCAB_FILES_MAP
					__a	       =							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a	       =							READER_PRETRAINED_INIT_CONFIGURATION
					__a	       =							["""input_ids""", """attention_mask"""]




"""simple docstring"""






from importlib import import_module

from .logging import get_logger


UpperCAmelCase    :     Any   						=		get_logger(__name__)





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Optional[int] ,       UpperCamelCase    : List[Any] ,       UpperCamelCase    : int=None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					attrs or []
												if module is not None:
																			for key in module.__dict__:
																										if key in attrs or not key.startswith("""__"""  ):
																																	setattr(self ,       UpperCamelCase ,       getattr(UpperCamelCase ,       UpperCamelCase  )  )
												__UpperCAmelCase   :						Any      							=					module._original_module if isinstance(UpperCamelCase ,       _PatchedModuleObj  ) else module






class      lowerCamelCase__   :







					"""simple docstring"""


					__a	       =							[]






					def __init__(       self    : str ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : List[str] ,       UpperCamelCase    : Optional[Any]=None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					obj
												__UpperCAmelCase   :						Union[str, Any]      							=					target
												__UpperCAmelCase   :						List[str]      							=					new
												__UpperCAmelCase   :						Optional[int]      							=					target.split("""."""  )[0]
												__UpperCAmelCase   :						Tuple      							=					{}
												__UpperCAmelCase   :						Union[str, Any]      							=					attrs or []






					def __enter__(       self    : Dict  ):
												'''simple docstring'''






												*__UpperCAmelCase							,__UpperCAmelCase   :						str      							=					self.target.split("""."""  )

												# Patch modules:
												# it's used to patch attributes of submodules like "os.path.join";
												# in this case we need to patch "os" and "os.path"

												for i in range(len(UpperCamelCase  )  ):
																			try:
																										__UpperCAmelCase   :						List[Any]      							=					import_module(""".""".join(submodules[: i + 1]  )  )
																			except ModuleNotFoundError:
																										continue
																			# We iterate over all the globals in self.obj in case we find "os" or "os.path"
																			for attr in self.obj.__dir__():
																										__UpperCAmelCase   :						List[Any]      							=					getattr(self.obj ,       UpperCamelCase  )
																										# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
																										# This allows to patch renamed modules like "from os import path as ospath".
																										if obj_attr is submodule or (
																										    (isinstance(UpperCamelCase ,       _PatchedModuleObj  ) and obj_attr._original_module is submodule)
																										):
																																	__UpperCAmelCase   :						Tuple      							=					obj_attr
																																	# patch at top level
																																	setattr(self.obj ,       UpperCamelCase ,       _PatchedModuleObj(UpperCamelCase ,       attrs=self.attrs  )  )
																																	__UpperCAmelCase   :						int      							=					getattr(self.obj ,       UpperCamelCase  )
																																	# construct lower levels patches
																																	for key in submodules[i + 1 :]:
																																								setattr(UpperCamelCase ,       UpperCamelCase ,       _PatchedModuleObj(getattr(UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase  ) ,       attrs=self.attrs  )  )
																																								__UpperCAmelCase   :						Optional[int]      							=					getattr(UpperCamelCase ,       UpperCamelCase  )
																																	# finally set the target attribute
																																	setattr(UpperCamelCase ,       UpperCamelCase ,       self.new  )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

												if submodules:  # if it's an attribute of a submodule like "os.path.join"
																			try:
																										__UpperCAmelCase   :						int      							=					getattr(import_module(""".""".join(UpperCamelCase  )  ) ,       UpperCamelCase  )
																			except (AttributeError, ModuleNotFoundError):
																										return
																			# We iterate over all the globals in self.obj in case we find "os.path.join"
																			for attr in self.obj.__dir__():
																										# We don't check for the name of the global, but rather if its value *is* "os.path.join".
																										# This allows to patch renamed attributes like "from os.path import join as pjoin".
																										if getattr(self.obj ,       UpperCamelCase  ) is attr_value:
																																	__UpperCAmelCase   :						Union[str, Any]      							=					getattr(self.obj ,       UpperCamelCase  )
																																	setattr(self.obj ,       UpperCamelCase ,       self.new  )
												elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
																			__UpperCAmelCase   :						str      							=					globals()["""__builtins__"""][target_attr]
																			setattr(self.obj ,       UpperCamelCase ,       self.new  )
												else:
																			raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.'''  )






					def __exit__(       self    : str ,       *UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												for attr in list(self.original  ):
																			setattr(self.obj ,       UpperCamelCase ,       self.original.pop(UpperCamelCase  )  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												self.__enter__()
												self._active_patches.append(self  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												try:
																			self._active_patches.remove(self  )
												except ValueError:
																			# If the patch hasn't been started this will fail
																			return None

												return self.__exit__()
