


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

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



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						List[List[ImageInput]]:
    if isinstance(lowerCamelCase_							, (list, tuple) ) and isinstance(videos[0]							, (list, tuple) ) and is_valid_image(videos[0][0] ):
        return videos

    elif isinstance(lowerCamelCase_							, (list, tuple) ) and is_valid_image(videos[0] ):
        return [videos]

    elif is_valid_image(lowerCamelCase_ ):
        return [[videos]]

    raise ValueError(F'''Could not make batched video from {videos}''' )






class _lowerCamelCase(						_a   ):
       lowercase_ :	Union[str, Any]     =			["""pixel_values"""]
       def __init__(					self,      lowerCamelCase = True,      lowerCamelCase = None,      lowerCamelCase = PILImageResampling.BILINEAR,      lowerCamelCase = True,      lowerCamelCase = None,      lowerCamelCase = True,      lowerCamelCase = 1 / 2_55,      lowerCamelCase = True,      lowerCamelCase = True,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase       :							str      		=		size if size is not None else {'shortest_edge': 2_56}
           _lowercase       :							Any      		=		get_size_dict(lowerCamelCase,      default_to_square=lowerCamelCase)
           _lowercase       :							List[str]      		=		crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
           _lowercase       :							Optional[Any]      		=		get_size_dict(lowerCamelCase,      param_name='crop_size')

           _lowercase       :							Optional[int]      		=		do_resize
           _lowercase       :							Tuple      		=		size
           _lowercase       :							Any      		=		do_center_crop
           _lowercase       :							Any      		=		crop_size
           _lowercase       :							int      		=		resample
           _lowercase       :							int      		=		do_rescale
           _lowercase       :							str      		=		rescale_factor
           _lowercase       :							Tuple      		=		offset
           _lowercase       :							List[str]      		=		do_normalize
           _lowercase       :							Tuple      		=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           _lowercase       :							Any      		=		image_std if image_std is not None else IMAGENET_STANDARD_STD
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = PILImageResampling.BILINEAR,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   np.ndarray:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		get_size_dict(lowerCamelCase,      default_to_square=lowerCamelCase)
           if "shortest_edge" in size:
               _lowercase       :							Dict      		=		get_resize_output_image_size(lowerCamelCase,      size['shortest_edge'],      default_to_square=lowerCamelCase)
           elif "height" in size and "width" in size:
               _lowercase       :							List[Any]      		=		(size['height'], size['width'])
           else:
               raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
           return resize(lowerCamelCase,      size=lowerCamelCase,      resample=lowerCamelCase,      data_format=lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   np.ndarray:




           """simple docstring"""

           _lowercase       :							Dict      		=		get_size_dict(lowerCamelCase)
           if "height" not in size or "width" not in size:
               raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
           return center_crop(lowerCamelCase,      size=(size['height'], size['width']),      data_format=lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = True,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   Dict:




           """simple docstring"""

           _lowercase       :							str      		=		image.astype(np.floataa)
           if offset:
               _lowercase       :							List[str]      		=		image - (scale / 2)
           return rescale(lowerCamelCase,      scale=lowerCamelCase,      data_format=lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   np.ndarray:




           """simple docstring"""

           return normalize(lowerCamelCase,      mean=lowerCamelCase,      std=lowerCamelCase,      data_format=lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = ChannelDimension.FIRST,      )       ->   np.ndarray:




           """simple docstring"""

           if do_resize and size is None or resample is None:
               raise ValueError('Size and resample must be specified if do_resize is True.')

           if do_center_crop and crop_size is None:
               raise ValueError('Crop size must be specified if do_center_crop is True.')

           if do_rescale and rescale_factor is None:
               raise ValueError('Rescale factor must be specified if do_rescale is True.')

           if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('Image mean and std must be specified if do_normalize is True.')

           if offset and not do_rescale:
               raise ValueError('For offset, do_rescale must also be set to True.')

           # All transformations expect numpy arrays.
           _lowercase       :							Tuple      		=		to_numpy_array(lowerCamelCase)

           if do_resize:
               _lowercase       :							Optional[Any]      		=		self.resize(image=lowerCamelCase,      size=lowerCamelCase,      resample=lowerCamelCase)

           if do_center_crop:
               _lowercase       :							Dict      		=		self.center_crop(lowerCamelCase,      size=lowerCamelCase)

           if do_rescale:
               _lowercase       :							Dict      		=		self.rescale(image=lowerCamelCase,      scale=lowerCamelCase,      offset=lowerCamelCase)

           if do_normalize:
               _lowercase       :							Optional[Any]      		=		self.normalize(image=lowerCamelCase,      mean=lowerCamelCase,      std=lowerCamelCase)

           _lowercase       :							Tuple      		=		to_channel_dimension_format(lowerCamelCase,      lowerCamelCase)
           return image




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = ChannelDimension.FIRST,      **lowerCamelCase,      )       ->   PIL.Image.Image:




           """simple docstring"""

           _lowercase       :							Any      		=		do_resize if do_resize is not None else self.do_resize
           _lowercase       :							Optional[int]      		=		resample if resample is not None else self.resample
           _lowercase       :							Dict      		=		do_center_crop if do_center_crop is not None else self.do_center_crop
           _lowercase       :							Optional[int]      		=		do_rescale if do_rescale is not None else self.do_rescale
           _lowercase       :							Dict      		=		rescale_factor if rescale_factor is not None else self.rescale_factor
           _lowercase       :							Optional[int]      		=		offset if offset is not None else self.offset
           _lowercase       :							List[Any]      		=		do_normalize if do_normalize is not None else self.do_normalize
           _lowercase       :							Any      		=		image_mean if image_mean is not None else self.image_mean
           _lowercase       :							Union[str, Any]      		=		image_std if image_std is not None else self.image_std

           _lowercase       :							Union[str, Any]      		=		size if size is not None else self.size
           _lowercase       :							Any      		=		get_size_dict(lowerCamelCase,      default_to_square=lowerCamelCase)
           _lowercase       :							List[str]      		=		crop_size if crop_size is not None else self.crop_size
           _lowercase       :							Any      		=		get_size_dict(lowerCamelCase,      param_name='crop_size')

           if not valid_images(lowerCamelCase):
               raise ValueError(
                   'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                   'torch.Tensor, tf.Tensor or jax.ndarray.')

           _lowercase       :							List[str]      		=		make_batched(lowerCamelCase)

           _lowercase       :							Any      		=		[
               [
                   self._preprocess_image(
                       image=lowerCamelCase,      do_resize=lowerCamelCase,      size=lowerCamelCase,      resample=lowerCamelCase,      do_center_crop=lowerCamelCase,      crop_size=lowerCamelCase,      do_rescale=lowerCamelCase,      rescale_factor=lowerCamelCase,      offset=lowerCamelCase,      do_normalize=lowerCamelCase,      image_mean=lowerCamelCase,      image_std=lowerCamelCase,      data_format=lowerCamelCase,      )
                   for img in video
               ]
               for video in videos
           ]

           _lowercase       :							Dict      		=		{'pixel_values': videos}
           return BatchFeature(data=lowerCamelCase,      tensor_type=lowerCamelCase)






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


SCREAMING_SNAKE_CASE       :      Any												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE       :      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"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      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"
        ),
    },
}

SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Optional[Any]												= {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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






class _lowerCamelCase(						_a   ):
       lowercase_ :	Any     =			VOCAB_FILES_NAMES
       lowercase_ :	Optional[int]     =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	str     =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	str     =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class _lowerCamelCase(						_a   ):
       lowercase_ :	Optional[int]     =			VOCAB_FILES_NAMES
       lowercase_ :	Any     =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	str     =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	Union[str, Any]     =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


SCREAMING_SNAKE_CASE       :      Optional[int]												= collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


SCREAMING_SNAKE_CASE       :      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    ```\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:
       def __call__(					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   BatchEncoding:




           """simple docstring"""

           if titles is None and texts is None:
               return super().__call__(
                   lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase,      return_attention_mask=lowerCamelCase,      **lowerCamelCase,      )
           elif titles is None or texts is None:
               _lowercase       :							Dict      		=		titles if texts is None else texts
               return super().__call__(
                   lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase,      return_attention_mask=lowerCamelCase,      **lowerCamelCase,      )
           _lowercase       :							Union[str, Any]      		=		titles if not isinstance(lowerCamelCase,      lowerCamelCase) else [titles]
           _lowercase       :							Tuple      		=		texts if not isinstance(lowerCamelCase,      lowerCamelCase) else [texts]
           _lowercase       :							Optional[Any]      		=		len(lowerCamelCase)
           _lowercase       :							Any      		=		questions if not isinstance(lowerCamelCase,      lowerCamelCase) else [questions] * n_passages
           if len(lowerCamelCase) != len(lowerCamelCase):
               raise ValueError(
                   F'''There should be as many titles than texts but got {len(lowerCamelCase)} titles and {len(lowerCamelCase)} texts.''')
           _lowercase       :							Any      		=		super().__call__(lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							Tuple      		=		super().__call__(lowerCamelCase,      add_special_tokens=lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							int      		=		{
               '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(lowerCamelCase,      lowerCamelCase)
               ]
           }
           if return_attention_mask is not False:
               _lowercase       :							Optional[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       :							Union[str, Any]      		=		attention_mask
           return self.pad(lowerCamelCase,      padding=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 16,      lowerCamelCase = 64,      lowerCamelCase = 4,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		reader_input['input_ids']
           _lowercase		,				_lowercase		,				_lowercase       :							Tuple      		=		reader_output[:3]
           _lowercase       :							Tuple      		=		len(lowerCamelCase)
           _lowercase       :							str      		=		sorted(range(lowerCamelCase),      reverse=lowerCamelCase,      key=relevance_logits.__getitem__)
           _lowercase       :							List[DPRReaderOutput]      		=		[]
           for doc_id in sorted_docs:
               _lowercase       :							str      		=		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       :							List[Any]      		=		sequence_ids.index(self.pad_token_id)
               else:
                   _lowercase       :							List[str]      		=		len(lowerCamelCase)

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




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

           _lowercase       :							str      		=		[]
           for start_index, start_score in enumerate(lowerCamelCase):
               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(lowerCamelCase,      key=lambda lowerCamelCase: x[1],      reverse=lowerCamelCase)
           _lowercase       :							List[str]      		=		[]
           for (start_index, end_index), score in scores:
               if start_index > end_index:
                   raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''')
               _lowercase       :							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(lowerCamelCase) == top_spans:
                   break
           return chosen_span_intervals






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a, _a   ):
       lowercase_ :	Union[str, Any]     =			VOCAB_FILES_NAMES
       lowercase_ :	Any     =			READER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	Dict     =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	Optional[int]     =			READER_PRETRAINED_INIT_CONFIGURATION
       lowercase_ :	str     =			["""input_ids""", """attention_mask"""]


