






'''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 import BertTokenizer


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE 							=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE 							=						{
    '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 							=						{
    '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 							=						{
    '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 							=						{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
SCREAMING_SNAKE_CASE 							=						{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
SCREAMING_SNAKE_CASE 							=						{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


SCREAMING_SNAKE_CASE 							=						{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
SCREAMING_SNAKE_CASE 							=						{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
SCREAMING_SNAKE_CASE 							=						{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class 							A_    (						_A			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Tuple =					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE							:			List[str] =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE							:			Tuple =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class 							A_    (						_A			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Any =					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE							:			List[Any] =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE							:			List[Any] =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE							:			List[Any] =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



SCREAMING_SNAKE_CASE 							=						collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

SCREAMING_SNAKE_CASE 							=						collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


SCREAMING_SNAKE_CASE 							=						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 							A_    :






 '''simple docstring'''




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


 def  snake_case__				(	self							,  _A							,  _A							,  _A = 16							,  _A = 64							,  _A = 4							,  ) ->		List[DPRSpanPrediction]:

     """simple docstring"""



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

         _UpperCAmelCase				:      Optional[int]			  =				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  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  ) ->		List[DPRSpanPrediction]:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				[]
     for start_index, start_score in enumerate(UpperCamelCase__):
         for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
             scores.append(((start_index, start_index + answer_length), start_score + end_score))
     _UpperCAmelCase				:      List[Any]			  =				sorted(UpperCamelCase__							,  key=lambda _A: x[1]							,  reverse=UpperCamelCase__)
     _UpperCAmelCase				:      Union[str, Any]			  =				[]
     for (start_index, end_index), score in scores:
         if start_index > end_index:
             raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]''')
         _UpperCAmelCase				:      Tuple			  =				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(UpperCamelCase__) == top_spans:
             break
     return chosen_span_intervals



@add_end_docstrings(_A			)
class 							A_    (						_A ,   _A			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			List[str] =					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					READER_PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE							:			str =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE							:			List[str] =					READER_PRETRAINED_INIT_CONFIGURATION
 _SCREAMING_SNAKE_CASE							:			List[str] =					["input_ids", "attention_mask"]












from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)
@add_end_docstrings(__lowercase			)
class 							A_    (						__lowercase			):






 '''simple docstring'''




 def __init__(	self							,  **_A) ->		int:

     """simple docstring"""



     super().__init__(**_A)

     if self.framework != "pt":
         raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')
     # No specific FOR_XXX available yet


 def __call__(	self							,  _A							,  **_A) ->		List[str]:

     """simple docstring"""



     return super().__call__(_A							,  **_A)


 def  snake_case__				(	self							,  **_A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				{}
     if "candidate_labels" in kwargs:
         _UpperCAmelCase				:      Union[str, Any]			  =				kwargs['''candidate_labels''']
     if "hypothesis_template" in kwargs:
         _UpperCAmelCase				:      Dict			  =				kwargs['''hypothesis_template''']

     return preprocess_params, {}, {}


 def  snake_case__				(	self							,  _A							,  _A=None							,  _A="This is a sound of {}.") ->		Tuple:

     """simple docstring"""



     if isinstance(_A							,  _A):
         if audio.startswith('''http://''') or audio.startswith('''https://'''):
             # We need to actually check for a real protocol, otherwise it's impossible to use a local file
             # like http_huggingface_co.png
             _UpperCAmelCase				:      List[Any]			  =				requests.get(_A).content
         else:
             with open(_A							,  '''rb''') as f:
                 _UpperCAmelCase				:      List[str]			  =				f.read()

     if isinstance(_A							,  _A):
         _UpperCAmelCase				:      List[Any]			  =				ffmpeg_read(_A							,  self.feature_extractor.sampling_rate)

     if not isinstance(_A							,  np.ndarray):
         raise ValueError('''We expect a numpy ndarray as input''')
     if len(audio.shape) != 1:
         raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''')

     _UpperCAmelCase				:      Optional[Any]			  =				self.feature_extractor(
         [audio]							,  sampling_rate=self.feature_extractor.sampling_rate							,  return_tensors='''pt''')
     _UpperCAmelCase				:      Dict			  =				candidate_labels
     _UpperCAmelCase				:      List[str]			  =				[hypothesis_template.format(_A) for x in candidate_labels]
     _UpperCAmelCase				:      Optional[Any]			  =				self.tokenizer(_A							,  return_tensors=self.framework							,  padding=_A)
     _UpperCAmelCase				:      str			  =				[text_inputs]
     return inputs


 def  snake_case__				(	self							,  _A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				model_inputs.pop('''candidate_labels''')
     _UpperCAmelCase				:      Union[str, Any]			  =				model_inputs.pop('''text_inputs''')
     if isinstance(text_inputs[0]							,  _A):
         _UpperCAmelCase				:      int			  =				text_inputs[0]
     else:
         # Batching case.
         _UpperCAmelCase				:      Dict			  =				text_inputs[0][0]

     _UpperCAmelCase				:      str			  =				self.model(**_A							,  **_A)

     _UpperCAmelCase				:      str			  =				{
         '''candidate_labels''': candidate_labels,
         '''logits''': outputs.logits_per_audio,
     }
     return model_outputs


 def  snake_case__				(	self							,  _A) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				model_outputs.pop('''candidate_labels''')
     _UpperCAmelCase				:      str			  =				model_outputs['''logits'''][0]

     if self.framework == "pt":
         _UpperCAmelCase				:      int			  =				logits.softmax(dim=0)
         _UpperCAmelCase				:      Optional[Any]			  =				probs.tolist()
     else:
         raise ValueError('''`tf` framework not supported.''')

     _UpperCAmelCase				:      List[str]			  =				[
         {'''score''': score, '''label''': candidate_label}
         for score, candidate_label in sorted(zip(_A							,  _A)							,  key=lambda _A: -x[0])
     ]
     return result




