




from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''vqvae''']


    def __init__(			self      :	Optional[int]					, UpperCAmelCase__      :	AutoencoderKL					, UpperCAmelCase__      :	UNetaDConditionModel					, UpperCAmelCase__      :	Mel					, UpperCAmelCase__      :	Union[DDIMScheduler, DDPMScheduler]					, )						->Dict:





           '''simple docstring'''

           super().__init__()
           self.register_modules(unet=UpperCAmelCase__					, scheduler=UpperCAmelCase__					, mel=UpperCAmelCase__					, vqvae=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any])						->int:





           '''simple docstring'''

           return 50 if isinstance(self.scheduler					, UpperCAmelCase__) else 1_000


    @torch.no_grad()
    def __call__(			self      :	Tuple					, UpperCAmelCase__      :	int = 1					, UpperCAmelCase__      :	str = None					, UpperCAmelCase__      :	np.ndarray = None					, UpperCAmelCase__      :	int = 0					, UpperCAmelCase__      :	int = 0					, UpperCAmelCase__      :	int = None					, UpperCAmelCase__      :	torch.Generator = None					, UpperCAmelCase__      :	float = 0					, UpperCAmelCase__      :	float = 0					, UpperCAmelCase__      :	torch.Generator = None					, UpperCAmelCase__      :	float = 0					, UpperCAmelCase__      :	torch.Tensor = None					, UpperCAmelCase__      :	torch.Tensor = None					, UpperCAmelCase__      :	Union[str, Any]=True					, )						->Union[
        Union[AudioPipelineOutput, ImagePipelineOutput],
        Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
    ]:





           '''simple docstring'''

           A__       =    steps or self.get_default_steps()
           self.scheduler.set_timesteps(UpperCAmelCase__)
           A__       =    step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size) == int:
                  A__       =    (self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                  A__       =    randn_tensor(
                      (
                          batch_size,
                          self.unet.config.in_channels,
                          self.unet.config.sample_size[0],
                          self.unet.config.sample_size[1],
                      )					, generator=UpperCAmelCase__					, device=self.device					, )
           A__       =    noise
           A__       =    None

           if audio_file is not None or raw_audio is not None:
                  self.mel.load_audio(UpperCAmelCase__					, UpperCAmelCase__)
                  A__       =    self.mel.audio_slice_to_image(UpperCAmelCase__)
                  A__       =    np.frombuffer(input_image.tobytes()					, dtype='''uint8''').reshape(
                      (input_image.height, input_image.width))
                  A__       =    (input_image / 255) * 2 - 1
                  A__       =    torch.tensor(input_image[np.newaxis, :, :]					, dtype=torch.float).to(self.device)

                  if self.vqvae is not None:
                         A__       =    self.vqvae.encode(torch.unsqueeze(UpperCAmelCase__					, 0)).latent_dist.sample(
                             generator=UpperCAmelCase__)[0]
                         A__       =    self.vqvae.config.scaling_factor * input_images

                  if start_step > 0:
                         A__       =    self.scheduler.add_noise(UpperCAmelCase__					, UpperCAmelCase__					, self.scheduler.timesteps[start_step - 1])

                  A__       =    (
                      self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                  )
                  A__       =    int(mask_start_secs * pixels_per_second)
                  A__       =    int(mask_end_secs * pixels_per_second)
                  A__       =    self.scheduler.add_noise(UpperCAmelCase__					, UpperCAmelCase__					, torch.tensor(self.scheduler.timesteps[start_step:]))

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
                  if isinstance(self.unet					, UpperCAmelCase__):
                         A__       =    self.unet(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)['''sample''']
                  else:
                         A__       =    self.unet(UpperCAmelCase__					, UpperCAmelCase__)['''sample''']

                  if isinstance(self.scheduler					, UpperCAmelCase__):
                         A__       =    self.scheduler.step(
                             model_output=UpperCAmelCase__					, timestep=UpperCAmelCase__					, sample=UpperCAmelCase__					, eta=UpperCAmelCase__					, generator=UpperCAmelCase__					, )['''prev_sample''']
                  else:
                         A__       =    self.scheduler.step(
                             model_output=UpperCAmelCase__					, timestep=UpperCAmelCase__					, sample=UpperCAmelCase__					, generator=UpperCAmelCase__					, )['''prev_sample''']

                  if mask is not None:
                         if mask_start > 0:
                                A__       =    mask[:, step, :, :mask_start]
                         if mask_end > 0:
                                A__       =    mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
                  # 0.18215 was scaling factor used in training to ensure unit variance
                  A__       =    1 / self.vqvae.config.scaling_factor * images
                  A__       =    self.vqvae.decode(UpperCAmelCase__)['''sample''']

           A__       =    (images / 2 + 0.5).clamp(0					, 1)
           A__       =    images.cpu().permute(0					, 2					, 3					, 1).numpy()
           A__       =    (images * 255).round().astype('''uint8''')
           A__       =    list(
               (Image.fromarray(_[:, :, 0]) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(UpperCAmelCase__					, mode='''RGB''').convert('''L''') for _ in images))

           A__       =    [self.mel.image_to_audio(UpperCAmelCase__) for _ in images]
           if not return_dict:
                  return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(UpperCAmelCase__)[:, np.newaxis, :])					, **ImagePipelineOutput(UpperCAmelCase__))


    @torch.no_grad()
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	List[Image.Image]					, UpperCAmelCase__      :	int = 50)						->np.ndarray:





           '''simple docstring'''

           assert isinstance(self.scheduler					, UpperCAmelCase__)
           self.scheduler.set_timesteps(UpperCAmelCase__)
           A__       =    np.array(
               [np.frombuffer(image.tobytes()					, dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
           A__       =    (sample / 255) * 2 - 1
           A__       =    torch.Tensor(UpperCAmelCase__).to(self.device)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps					, (0,))):
                  A__       =    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                  A__       =    self.scheduler.alphas_cumprod[t]
                  A__       =    (
                      self.scheduler.alphas_cumprod[prev_timestep]
                      if prev_timestep >= 0
                      else self.scheduler.final_alpha_cumprod
                  )
                  A__       =    1 - alpha_prod_t
                  A__       =    self.unet(UpperCAmelCase__					, UpperCAmelCase__)['''sample''']
                  A__       =    (1 - alpha_prod_t_prev) ** 0.5 * model_output
                  A__       =    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                  A__       =    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample





    @staticmethod
    def        SCREAMING_SNAKE_CASE							(			UpperCAmelCase__      :	torch.Tensor					, UpperCAmelCase__      :	torch.Tensor					, UpperCAmelCase__      :	float)						->torch.Tensor:





           '''simple docstring'''

           A__       =    acos(torch.dot(torch.flatten(UpperCAmelCase__)					, torch.flatten(UpperCAmelCase__)) / torch.norm(UpperCAmelCase__) / torch.norm(UpperCAmelCase__))
           return sin((1 - alpha) * theta) * xa / sin(UpperCAmelCase__) + sin(alpha * theta) * xa / sin(UpperCAmelCase__)








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


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

_lowerCamelCase	:		Tuple							       =   {"""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	:		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"""
        ),
    },
}
_lowerCamelCase	:		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"""
        ),
    },
}

_lowerCamelCase	:		Any							       =   {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_lowerCamelCase	:		List[str]							       =   {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_lowerCamelCase	:		Tuple							       =   {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_lowerCamelCase	:		Optional[Any]							       =   {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCamelCase	:		Optional[int]							       =   {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCamelCase	:		Optional[Any]							       =   {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''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 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''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
_lowerCamelCase	:		int							       =   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"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """


@add_start_docstrings(UpperCAmelCase__     )
class 				UpperCamelCase_    :

    '''simple docstring'''






    def __call__(			self      :	Optional[int]					, UpperCAmelCase__      :	Tuple					, 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__      :	Optional[int]					, )						->BatchEncoding:





           '''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:
                  A__       =    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__					, )
           A__       =    titles if not isinstance(UpperCAmelCase__					, UpperCAmelCase__) else [titles]
           A__       =    texts if not isinstance(UpperCAmelCase__					, UpperCAmelCase__) else [texts]
           A__       =    len(UpperCAmelCase__)
           A__       =    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."""
           A__       =    super().__call__(UpperCAmelCase__					, UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__)['''input_ids''']
           A__       =    super().__call__(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__)['''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(UpperCAmelCase__					, UpperCAmelCase__)
               ]
           }
           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(UpperCAmelCase__					, padding=UpperCAmelCase__					, max_length=UpperCAmelCase__					, return_tensors=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	BatchEncoding					, UpperCAmelCase__      :	DPRReaderOutput					, UpperCAmelCase__      :	int = 16					, UpperCAmelCase__      :	int = 64					, UpperCAmelCase__      :	int = 4					, )						->List[DPRSpanPrediction]:





           '''simple docstring'''

           A__       =    reader_input['''input_ids''']
           A__ ,							A__ ,							A__       =    reader_output[:3]
           A__       =    len(UpperCAmelCase__)
           A__       =    sorted(range(UpperCAmelCase__)					, reverse=UpperCAmelCase__					, 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(UpperCAmelCase__)

                  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=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        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	int					, )						->List[DPRSpanPrediction]:





           '''simple docstring'''

           A__       =    []
           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))
           A__       =    sorted(UpperCAmelCase__					, key=lambda UpperCAmelCase__: x[1]					, reverse=UpperCAmelCase__)
           A__       =    []
           for (start_index, end_index), score in scores:
                  assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
                  A__       =    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(UpperCAmelCase__     )
class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''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


