"""simple docstring"""
from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
 import numpy as np
 from decord import VideoReader


if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

lowercase__ 	=     logging.get_logger(__name__)




@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      *lowercase   ,      **lowercase)     ->      str:





        '''simple docstring'''





        super().__init__(*lowercase   ,      **lowercase)
        requires_backends(self   ,      'decord')
        self.check_model_type(lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase=None)     ->      List[Any]:





        '''simple docstring'''





        a__:					Dict      							=     {}
        if frame_sampling_rate is not None:
          a__:					Union[str, Any]      							=     frame_sampling_rate
        if num_frames is not None:
          a__:					Dict      							=     num_frames

        a__:					Union[str, Any]      							=     {}
        if top_k is not None:
          a__:					Optional[int]      							=     top_k
        return preprocess_params, {}, postprocess_params



      def __call__(						self   ,      lowercase   ,      **lowercase)     ->      Optional[int]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=None   ,      lowercase=1)     ->      List[Any]:





        '''simple docstring'''





        if num_frames is None:
          a__:					int      							=     self.model.config.num_frames

        if video.startswith('http://') or video.startswith('https://'):
          a__:					List[str]      							=     BytesIO(requests.get(lowercase).content)

        a__:					List[Any]      							=     VideoReader(lowercase)
        videoreader.seek(0)

        a__:					List[Any]      							=     0
        a__:					Union[str, Any]      							=     num_frames * frame_sampling_rate - 1
        a__:					Tuple      							=     np.linspace(lowercase   ,      lowercase   ,      num=lowercase   ,      dtype=np.intaa)

        a__:					Any      							=     videoreader.get_batch(lowercase).asnumpy()
        a__:					List[Any]      							=     list(lowercase)

        a__:					List[Any]      							=     self.image_processor(lowercase   ,      return_tensors=self.framework)
        return model_inputs



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Any:





        '''simple docstring'''





        a__:					Any      							=     self.model(**lowercase)
        return model_outputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=5)     ->      Union[str, Any]:





        '''simple docstring'''





        if top_k > self.model.config.num_labels:
          a__:					Optional[Any]      							=     self.model.config.num_labels

        if self.framework == "pt":
          a__:					Union[str, Any]      							=     model_outputs.logits.softmax(-1)[0]
          a__							,   a__:					Union[str, Any]      							=     probs.topk(lowercase)
        else:
          raise ValueError(f'Unsupported framework: {self.framework}')

        a__:					Tuple      							=     scores.tolist()
        a__:					str      							=     ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase   ,      lowercase)]



"""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


lowercase__ 	=     logging.get_logger(__name__)

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

lowercase__ 	=     {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowercase__ 	=     {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowercase__ 	=     {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

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


lowercase__ 	=     {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowercase__ 	=     {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowercase__ 	=     {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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

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


lowercase__ 	=     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(__lowerCAmelCase    )
class        __snake_case	:



      def __call__(						self   ,      lowercase   ,      lowercase = None   ,      lowercase = None   ,      lowercase = False   ,      lowercase = False   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      **lowercase   ,      )     ->      BatchEncoding:





        '''simple docstring'''





        if titles is None and texts is None:
          return super().__call__(
              lowercase   ,      padding=lowercase   ,      truncation=lowercase   ,      max_length=lowercase   ,      return_tensors=lowercase   ,      return_attention_mask=lowercase   ,      **lowercase   ,      )
        elif titles is None or texts is None:
          a__:					str      							=     titles if texts is None else texts
          return super().__call__(
              lowercase   ,      lowercase   ,      padding=lowercase   ,      truncation=lowercase   ,      max_length=lowercase   ,      return_tensors=lowercase   ,      return_attention_mask=lowercase   ,      **lowercase   ,      )
        a__:					Tuple      							=     titles if not isinstance(lowercase   ,      lowercase) else [titles]
        a__:					Optional[int]      							=     texts if not isinstance(lowercase   ,      lowercase) else [texts]
        a__:					Dict      							=     len(lowercase)
        a__:					Dict      							=     questions if not isinstance(lowercase   ,      lowercase) else [questions] * n_passages
        if len(lowercase) != len(lowercase):
          raise ValueError(
              f'There should be as many titles than texts but got {len(lowercase)} titles and {len(lowercase)} texts.')
        a__:					List[str]      							=     super().__call__(lowercase   ,      lowercase   ,      padding=lowercase   ,      truncation=lowercase)['input_ids']
        a__:					List[Any]      							=     super().__call__(lowercase   ,      add_special_tokens=lowercase   ,      padding=lowercase   ,      truncation=lowercase)['input_ids']
        a__:					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(lowercase   ,      lowercase)
            ]
        }
        if return_attention_mask is not False:
          a__:					Dict      							=     []
          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__:					int      							=     attention_mask
        return self.pad(lowercase   ,      padding=lowercase   ,      max_length=lowercase   ,      return_tensors=lowercase)



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





        '''simple docstring'''





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

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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      )     ->      List[DPRSpanPrediction]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        for start_index, start_score in enumerate(lowercase):
          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__:					str      							=     sorted(lowercase   ,      key=lambda lowercase: x[1]   ,      reverse=lowercase)
        a__:					Optional[Any]      							=     []
        for (start_index, end_index), score in scores:
          if start_index > end_index:
            raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]')
          a__:					str      							=     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(lowercase) == top_spans:
            break
        return chosen_span_intervals







@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		READER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		READER_PRETRAINED_INIT_CONFIGURATION
      a__				=		["""input_ids""", """attention_mask"""]


