


'''simple docstring'''

import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
 from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


lowerCamelCase							:   Optional[Any]  						=	logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase							:   Dict  						=	256
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[Any]  					=							["""melgan"""]



      def __init__(self    :    Tuple   ,						UpperCamelCase    :    SpectrogramNotesEncoder   ,						UpperCamelCase    :    SpectrogramContEncoder   ,						UpperCamelCase    :    TaFilmDecoder   ,						UpperCamelCase    :    DDPMScheduler   ,						UpperCamelCase    :    OnnxRuntimeModel if is_onnx_available() else Any   ,						):

             '''simple docstring'''







             super().__init__()

             # From MELGAN
             lowercase__      			= math.log(1E-5 )  # Matches MelGAN training.
             lowercase__      			= 4.0  # Largest value for most examples
             lowercase__      			= 128

             self.register_modules(
                 notes_encoder=UpperCamelCase   ,						continuous_encoder=UpperCamelCase   ,						decoder=UpperCamelCase   ,						scheduler=UpperCamelCase   ,						melgan=UpperCamelCase   ,						)



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    Dict   ,						UpperCamelCase    :    Tuple=(-1.0, 1.0)   ,						UpperCamelCase    :    int=False ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= output_range
             if clip:
                    lowercase__      			= torch.clip(UpperCamelCase   ,						self.min_value   ,						self.max_value )
             # Scale to [0, 1].
             lowercase__      			= (features - self.min_value) / (self.max_value - self.min_value)
             # Scale to [min_out, max_out].
             return zero_one * (max_out - min_out) + min_out



      def       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    Union[str, Any]=(-1.0, 1.0)   ,						UpperCamelCase    :    Dict=False ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= input_range
             lowercase__      			= torch.clip(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase ) if clip else outputs
             # Scale to [0, 1].
             lowercase__      			= (outputs - min_out) / (max_out - min_out)
             # Scale to [self.min_value, self.max_value].
             return zero_one * (self.max_value - self.min_value) + self.min_value



      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= input_tokens > 0
             lowercase__			,lowercase__      			= self.notes_encoder(
                 encoder_input_tokens=UpperCamelCase   ,						encoder_inputs_mask=UpperCamelCase )

             lowercase__			,lowercase__      			= self.continuous_encoder(
                 encoder_inputs=UpperCamelCase   ,						encoder_inputs_mask=UpperCamelCase )

             return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]



      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Any ):

             '''simple docstring'''







             lowercase__      			= noise_time
             if not torch.is_tensor(UpperCamelCase ):
                    lowercase__      			= torch.tensor([timesteps]   ,						dtype=torch.long   ,						device=input_tokens.device )
             elif torch.is_tensor(UpperCamelCase ) and len(timesteps.shape ) == 0:
                    lowercase__      			= timesteps[None].to(input_tokens.device )

             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowercase__      			= timesteps * torch.ones(input_tokens.shape[0]   ,						dtype=timesteps.dtype   ,						device=timesteps.device )

             lowercase__      			= self.decoder(
                 encodings_and_masks=UpperCamelCase   ,						decoder_input_tokens=UpperCamelCase   ,						decoder_noise_time=UpperCamelCase )
             return logits






      @torch.no_grad()
      def __call__(self    :    List[Any]   ,						UpperCamelCase    :    List[List[int]]   ,						UpperCamelCase    :    Optional[torch.Generator] = None   ,						UpperCamelCase    :    int = 100   ,						UpperCamelCase    :    bool = True   ,						UpperCamelCase    :    str = "numpy"   ,						UpperCamelCase    :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						UpperCamelCase    :    int = 1   ,						):

             '''simple docstring'''







             if (callback_steps is None) or (
                 callback_steps is not None and (not isinstance(UpperCamelCase   ,						UpperCamelCase ) or callback_steps <= 0)
             ):
                    raise ValueError(
                        f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                        f" {type(UpperCamelCase )}." )

             lowercase__      			= np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]   ,						dtype=np.floataa )
             lowercase__      			= np.zeros([1, 0, self.n_dims]   ,						np.floataa )
             lowercase__      			= torch.ones((1, TARGET_FEATURE_LENGTH)   ,						dtype=UpperCamelCase   ,						device=self.device )

             for i, encoder_input_tokens in enumerate(UpperCamelCase ):
                    if i == 0:
                           lowercase__      			= torch.from_numpy(pred_mel[:1].copy() ).to(
                               device=self.device   ,						dtype=self.decoder.dtype )
                           # The first chunk has no previous context.
                           lowercase__      			= torch.zeros((1, TARGET_FEATURE_LENGTH)   ,						dtype=UpperCamelCase   ,						device=self.device )
                    else:
                           # The full song pipeline does not feed in a context feature, so the mask
                           # will be all 0s after the feature converter. Because we know we're
                           # feeding in a full context chunk from the previous prediction, set it
                           # to all 1s.
                           lowercase__      			= ones

                    lowercase__      			= self.scale_features(
                        UpperCamelCase   ,						output_range=[-1.0, 1.0]   ,						clip=UpperCamelCase )

                    lowercase__      			= self.encode(
                        input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device )   ,						continuous_inputs=UpperCamelCase   ,						continuous_mask=UpperCamelCase   ,						)

                    # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
                    lowercase__      			= randn_tensor(
                        shape=encoder_continuous_inputs.shape   ,						generator=UpperCamelCase   ,						device=self.device   ,						dtype=self.decoder.dtype   ,						)

                    # set step values
                    self.scheduler.set_timesteps(UpperCamelCase )

                    # Denoising diffusion loop
                    for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                           lowercase__      			= self.decode(
                               encodings_and_masks=UpperCamelCase   ,						input_tokens=UpperCamelCase   ,						noise_time=t / self.scheduler.config.num_train_timesteps   ,						)

                           # Compute previous output: x_t -> x_t-1
                           lowercase__      			= self.scheduler.step(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						generator=UpperCamelCase ).prev_sample

                    lowercase__      			= self.scale_to_features(UpperCamelCase   ,						input_range=[-1.0, 1.0] )
                    lowercase__      			= mel[:1]
                    lowercase__      			= mel.cpu().float().numpy()

                    lowercase__      			= np.concatenate([full_pred_mel, pred_mel[:1]]   ,						axis=1 )

                    # call the callback, if provided
                    if callback is not None and i % callback_steps == 0:
                           callback(UpperCamelCase   ,						UpperCamelCase )

                    logger.info('''Generated segment'''   ,						UpperCamelCase )

             if output_type == "numpy" and not is_onnx_available():
                    raise ValueError(
                        '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
             elif output_type == "numpy" and self.melgan is None:
                    raise ValueError(
                        '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )

             if output_type == "numpy":
                    lowercase__      			= self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
             else:
                    lowercase__      			= full_pred_mel

             if not return_dict:
                    return (output,)

             return AudioPipelineOutput(audios=UpperCamelCase )









'''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


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

UpperCamelCase__       :	Any    = {'''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__       :	Optional[int]    = {
    '''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__       :	Optional[Any]    = {
    '''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__       :	Any    = {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
UpperCamelCase__       :	Optional[Any]    = {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
UpperCamelCase__       :	Dict    = {
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


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






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		str			    =		VOCAB_FILES_NAMES
			_A      :		str			    =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_A      :		Any			    =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		Optional[Any]			    =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			_A      :		Any			    =		DPRContextEncoderTokenizer






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Dict			    =		VOCAB_FILES_NAMES
			_A      :		Any			    =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			_A      :		Tuple			    =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		Union[str, Any]			    =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			_A      :		Dict			    =		DPRQuestionEncoderTokenizer
UpperCamelCase__       :	str    = collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

UpperCamelCase__       :	List[Any]    = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


UpperCamelCase__       :	Any    = 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(lowerCamelCase__    )
class 						_UpperCamelCase  :


			'''simple docstring'''

			def __call__(							self     : str							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Optional[str] = None							,     lowerCAmelCase__     : Optional[str] = None							,     lowerCAmelCase__     : Union[bool, str] = False							,     lowerCAmelCase__     : Union[bool, str] = False							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : Optional[Union[str, TensorType]] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     **lowerCAmelCase__     : str							,     ):





					"""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:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		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__							,     )
					__SCREAMING_SNAKE_CASE       :		Dict           =		titles if not isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else [titles]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		texts if not isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else [texts]
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		len(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		questions if not isinstance(lowerCAmelCase__							,     lowerCAmelCase__						) else [questions] * n_passages
					assert len(lowerCAmelCase__						) == len(
					    lowerCAmelCase__						), F"There should be as many titles than texts but got {len(lowerCAmelCase__						)} titles and {len(lowerCAmelCase__						)} texts."
					__SCREAMING_SNAKE_CASE       :		int           =		super().__call__(lowerCAmelCase__							,     lowerCAmelCase__							,     padding=lowerCAmelCase__							,     truncation=lowerCAmelCase__						)["""input_ids"""]
					__SCREAMING_SNAKE_CASE       :		str           =		super().__call__(lowerCAmelCase__							,     add_special_tokens=lowerCAmelCase__							,     padding=lowerCAmelCase__							,     truncation=lowerCAmelCase__						)["""input_ids"""]
					__SCREAMING_SNAKE_CASE       :		Optional[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(lowerCAmelCase__							,     lowerCAmelCase__						)
					    ]
					}
					if return_attention_mask is not False:
							__SCREAMING_SNAKE_CASE       :		int           =		[]
							for input_ids in encoded_inputs["input_ids"]:
									attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
							__SCREAMING_SNAKE_CASE       :		int           =		attention_mask
					return self.pad(lowerCAmelCase__							,     padding=lowerCAmelCase__							,     max_length=lowerCAmelCase__							,     return_tensors=lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : BatchEncoding							,     lowerCAmelCase__     : DPRReaderOutput							,     lowerCAmelCase__     : int = 1_6							,     lowerCAmelCase__     : int = 6_4							,     lowerCAmelCase__     : int = 4							,     ):





					"""simple docstring"""






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

							__SCREAMING_SNAKE_CASE       :		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     : str							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : int							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[]
					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)						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		sorted(lowerCAmelCase__							,     key=lambda lowerCAmelCase__						: x[1]							,     reverse=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[]
					for (start_index, end_index), score in scores:
							assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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(lowerCAmelCase__						) == top_spans:
									break
					return chosen_span_intervals






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


			'''simple docstring'''

			_A      :		Optional[int]			    =		VOCAB_FILES_NAMES
			_A      :		int			    =		READER_PRETRAINED_VOCAB_FILES_MAP
			_A      :		str			    =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		str			    =		READER_PRETRAINED_INIT_CONFIGURATION
			_A      :		Dict			    =		['''input_ids''', '''attention_mask''']
			_A      :		Tuple			    =		DPRReaderTokenizer