


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


_lowercase							:   Union[str, Any]						=						logging.get_logger(__name__)

_lowercase							:   List[str]						=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowercase							:   List[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"
        ),
    },
}
_lowercase							:   List[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"
        ),
    },
}
_lowercase							:   str						=						{
    "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"
        ),
    },
}

_lowercase							:   Dict						=						{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
_lowercase							:   str						=						{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
_lowercase							:   Any						=						{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


_lowercase							:   List[Any]						=						{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowercase							:   Tuple						=						{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowercase							:   List[str]						=						{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_lowercase							:   Tuple						=						collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


_lowercase							:   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(__snake_case					)
class    _UpperCamelCase     :

				"""simple docstring"""







				def __call__(    self					, a__					, a__ = None					, a__ = None					, a__ = False					, a__ = False					, a__ = None					, a__ = None					, a__ = None					, **a__					, )						->					BatchEncoding:
					if titles is None and texts is None:
						return super().__call__(
						    a__					, padding=a__					, truncation=a__					, max_length=a__					, return_tensors=a__					, return_attention_mask=a__					, **a__					, )
					elif titles is None or texts is None:
						A             =					titles if texts is None else texts
						return super().__call__(
						    a__					, a__					, padding=a__					, truncation=a__					, max_length=a__					, return_tensors=a__					, return_attention_mask=a__					, **a__					, )
					A             =					titles if not isinstance(a__					, a__ ) else [titles]
					A             =					texts if not isinstance(a__					, a__ ) else [texts]
					A             =					len(a__ )
					A             =					questions if not isinstance(a__					, a__ ) else [questions] * n_passages
					if len(a__ ) != len(a__ ):
						raise ValueError(
						    f'There should be as many titles than texts but got {len(a__ )} titles and {len(a__ )} texts.' )
					A             =					super().__call__(a__					, a__					, padding=a__					, truncation=a__ )["""input_ids"""]
					A             =					super().__call__(a__					, add_special_tokens=a__					, padding=a__					, truncation=a__ )["""input_ids"""]
					A             =					{
					    """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(a__					, a__ )
					    ]
					}
					if return_attention_mask is not False:
						A             =					[]
						for input_ids in encoded_inputs["input_ids"]:
							attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
						A             =					attention_mask
					return self.pad(a__					, padding=a__					, max_length=a__					, return_tensors=a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ = 16					, a__ = 64					, a__ = 4					, )						->					List[DPRSpanPrediction]:
					A             =					reader_input["""input_ids"""]
					A       ,      A       ,      A             =					reader_output[:3]
					A             =					len(a__ )
					A             =					sorted(range(a__ )					, reverse=a__					, key=relevance_logits.__getitem__ )
					A             =					[]
					for doc_id in sorted_docs:
						A             =					list(input_ids[doc_id] )
						# assuming question & title information is at the beginning of the sequence
						A             =					sequence_ids.index(self.sep_token_id					, 2 ) + 1  # second sep id
						if sequence_ids[-1] == self.pad_token_id:
							A             =					sequence_ids.index(self.pad_token_id )
						else:
							A             =					len(a__ )

						A             =					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=a__					, top_spans=a__					, )
						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=a__					, start_index=a__					, end_index=a__					, text=self.decode(sequence_ids[start_index : end_index + 1] )					, ) )
						if len(a__ ) >= num_spans:
							break
					return nbest_spans_predictions[:num_spans]

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__					, )						->					List[DPRSpanPrediction]:
					A             =					[]
					for start_index, start_score in enumerate(a__ ):
						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) )
					A             =					sorted(a__					, key=lambda a__ : x[1]					, reverse=a__ )
					A             =					[]
					for (start_index, end_index), score in scores:
						if start_index > end_index:
							raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]' )
						A             =					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(a__ ) == top_spans:
							break
					return chosen_span_intervals






@add_end_docstrings(__snake_case					)
class    _UpperCamelCase     (       __snake_case     ,		__snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			READER_PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			READER_PRETRAINED_INIT_CONFIGURATION
				lowerCAmelCase					=			['input_ids', 'attention_mask']







_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



