



"""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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


__A   :							Dict										=   logging.get_logger(__name__)

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

__A   :							Optional[int]										=   {
    "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   :							Optional[Any]										=   {
    "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   :							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"
        ),
    },
}

__A   :							Optional[Any]										=   {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
__A   :							Union[str, Any]										=   {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__A   :							Optional[int]										=   {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


__A   :							List[Any]										=   {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__A   :							List[str]										=   {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__A   :							List[Any]										=   {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase__			      =	DPRContextEncoderTokenizer



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase__			      =	DPRQuestionEncoderTokenizer



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

__A   :							Any										=   collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


__A   :							List[str]										=   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(lowerCAmelCase)
class 			_a :
			"""simple docstring"""



			def __call__(		self     :    int				,   __UpperCamelCase     :    Optional[int]				,   __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				,   )->BatchEncoding:
							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					=    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					=    titles if not isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else [titles]
							_UpperCAmelCase					=    texts if not isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else [texts]
							_UpperCAmelCase					=    len(__UpperCamelCase   )
							_UpperCAmelCase					=    questions if not isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else [questions] * n_passages
							assert len(__UpperCamelCase   ) == len(
							    __UpperCamelCase   ), F'There should be as many titles than texts but got {len(__UpperCamelCase   )} titles and {len(__UpperCamelCase   )} texts.'
							_UpperCAmelCase					=    super().__call__(__UpperCamelCase				,   __UpperCamelCase				,   padding=__UpperCamelCase				,   truncation=__UpperCamelCase   )['''input_ids''']
							_UpperCAmelCase					=    super().__call__(__UpperCamelCase				,   add_special_tokens=__UpperCamelCase				,   padding=__UpperCamelCase				,   truncation=__UpperCamelCase   )['''input_ids''']
							_UpperCAmelCase					=    {
							    '''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					=    []
											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					=    attention_mask
							return self.pad(__UpperCamelCase				,   padding=__UpperCamelCase				,   max_length=__UpperCamelCase				,   return_tensors=__UpperCamelCase   )



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    BatchEncoding				,   __UpperCamelCase     :    DPRReaderOutput				,   __UpperCamelCase     :    int = 1_6				,   __UpperCamelCase     :    int = 6_4				,   __UpperCamelCase     :    int = 4				,   )->List[DPRSpanPrediction]:
							_UpperCAmelCase					=    reader_input['''input_ids''']
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    reader_output[:3]
							_UpperCAmelCase					=    len(__UpperCamelCase   )
							_UpperCAmelCase					=    sorted(range(__UpperCamelCase   )				,   reverse=__UpperCamelCase				,   key=relevance_logits.__getitem__   )
							_UpperCAmelCase					=    []
							for doc_id in sorted_docs:
											_UpperCAmelCase					=    list(input_ids[doc_id]   )
											# assuming question & title information is at the beginning of the sequence
											_UpperCAmelCase					=    sequence_ids.index(self.sep_token_id				,   2   ) + 1  # second sep id
											if sequence_ids[-1] == self.pad_token_id:
															_UpperCAmelCase					=    sequence_ids.index(self.pad_token_id   )
											else:
															_UpperCAmelCase					=    len(__UpperCamelCase   )

											_UpperCAmelCase					=    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  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int				,   )->List[DPRSpanPrediction]:
							_UpperCAmelCase					=    []
							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					=    sorted(__UpperCamelCase				,   key=lambda __UpperCamelCase   : x[1]				,   reverse=__UpperCamelCase   )
							_UpperCAmelCase					=    []
							for (start_index, end_index), score in scores:
											assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
											_UpperCAmelCase					=    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(__UpperCamelCase   ) == top_spans:
															break
							return chosen_span_intervals



@add_end_docstrings(lowerCAmelCase)
class 			_a (					lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	READER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	READER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase__			      =	["""input_ids""", """attention_mask"""]
			UpperCamelCase__			      =	DPRReaderTokenizer








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A   :							Optional[int]										=   logging.get_logger(__name__)

__A   :							Optional[Any]										=   {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 			_a (					lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""bit"""
			UpperCamelCase__			      =	["""preactivation""", """bottleneck"""]
			UpperCamelCase__			      =	["""SAME""", """VALID"""]



			def __init__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    Optional[int]=6_4				,   __UpperCamelCase     :    List[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,   __UpperCamelCase     :    Any=[3, 4, 6, 3]				,   __UpperCamelCase     :    str="preactivation"				,   __UpperCamelCase     :    Union[str, Any]="relu"				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Dict=3_2				,   __UpperCamelCase     :    List[str]=0.0				,   __UpperCamelCase     :    Any=False				,   __UpperCamelCase     :    Union[str, Any]=3_2				,   __UpperCamelCase     :    List[str]=1				,   __UpperCamelCase     :    List[str]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   **__UpperCamelCase     :    List[str]				,   )->List[Any]:
							super().__init__(**__UpperCamelCase   )
							if layer_type not in self.layer_types:
											raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
							if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
															_UpperCAmelCase					=    global_padding.upper()
											else:
															raise ValueError(F'Padding strategy {global_padding} not supported'   )
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    embedding_size
							_UpperCAmelCase					=    hidden_sizes
							_UpperCAmelCase					=    depths
							_UpperCAmelCase					=    layer_type
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    global_padding
							_UpperCAmelCase					=    num_groups
							_UpperCAmelCase					=    drop_path_rate
							_UpperCAmelCase					=    embedding_dynamic_padding
							_UpperCAmelCase					=    output_stride
							_UpperCAmelCase					=    width_factor

							_UpperCAmelCase					=    ['''stem'''] + [F'stage{idx}' for idx in range(1				,   len(__UpperCamelCase   ) + 1   )]
							_UpperCAmelCase		,						_UpperCAmelCase					=    get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase				,   out_indices=__UpperCamelCase				,   stage_names=self.stage_names   )



