





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


__A							:			Optional[Any]         =			logging.get_logger(__name__)

__A							:			List[Any]         =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A							:			Tuple         =			{
    "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"
        ),
    },
}
__A							:			List[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"
        ),
    },
}
__A							:			List[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"
        ),
    },
}

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


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







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			VOCAB_FILES_NAMES
     __magic_name__   =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __magic_name__   =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__   =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
     __magic_name__   =			DPRContextEncoderTokenizer







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			VOCAB_FILES_NAMES
     __magic_name__   =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __magic_name__   =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__   =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
     __magic_name__   =			DPRQuestionEncoderTokenizer




__A							:			List[str]         =			collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

__A							:			Optional[int]         =			collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


__A							:			Union[str, Any]         =			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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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(__snake_case	)
class      lowerCamelCase:
     '''simple docstring'''


     def __call__(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         if titles is None and texts is None:
             return super().__call__(
                 snake_case_				,     padding=snake_case_				,     truncation=snake_case_				,     max_length=snake_case_				,     return_tensors=snake_case_				,     return_attention_mask=snake_case_				,     **snake_case_				,     )
         elif titles is None or texts is None:
             _A     =							titles if texts is None else texts
             return super().__call__(
                 snake_case_				,     snake_case_				,     padding=snake_case_				,     truncation=snake_case_				,     max_length=snake_case_				,     return_tensors=snake_case_				,     return_attention_mask=snake_case_				,     **snake_case_				,     )
         _A     =							titles if not isinstance(snake_case_				,     snake_case_							) else [titles]
         _A     =							texts if not isinstance(snake_case_				,     snake_case_							) else [texts]
         _A     =							len(snake_case_							)
         _A     =							questions if not isinstance(snake_case_				,     snake_case_							) else [questions] * n_passages
         assert len(snake_case_							) == len(
             snake_case_							), F"There should be as many titles than texts but got {len(snake_case_							)} titles and {len(snake_case_							)} texts."
         _A     =							super().__call__(snake_case_				,     snake_case_				,     padding=snake_case_				,     truncation=snake_case_							)['input_ids']
         _A     =							super().__call__(snake_case_				,     add_special_tokens=snake_case_				,     padding=snake_case_				,     truncation=snake_case_							)['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(snake_case_				,     snake_case_							)
             ]
         }
         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(snake_case_				,     padding=snake_case_				,     max_length=snake_case_				,     return_tensors=snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = 16				,     snake_case_ = 64				,     snake_case_ = 4				,     ):
         _A     =							reader_input['input_ids']
         _A,			_A,			_A     =							reader_output[:3]
         _A     =							len(snake_case_							)
         _A     =							sorted(range(snake_case_							)				,     reverse=snake_case_				,     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(snake_case_							)

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




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         _A     =							[]
         for start_index, start_score in enumerate(snake_case_							):
             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(snake_case_				,     key=lambda snake_case_							: x[1]				,     reverse=snake_case_							)
         _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(snake_case_							) == top_spans:
                 break
         return chosen_span_intervals







@add_end_docstrings(__snake_case	)
class      lowerCamelCase(						__snake_case			, __snake_case	):
     '''simple docstring'''


     __magic_name__   =			VOCAB_FILES_NAMES
     __magic_name__   =			READER_PRETRAINED_VOCAB_FILES_MAP
     __magic_name__   =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__   =			READER_PRETRAINED_INIT_CONFIGURATION
     __magic_name__   =			['input_ids', 'attention_mask']
     __magic_name__   =			DPRReaderTokenizer







"""simple docstring"""




from cva import destroyAllWindows, imread, imshow, waitKey




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase			,					_lowercase:      Union[str, Any]   			=  img.shape[0], img.shape[1]

  # converting each pixel's color to its negative
  for i in range(_UpperCamelCase    ):
    for j in range(_UpperCamelCase    ):
      _lowercase:      Dict   			=  [255, 255, 255] - img[i][j]

  return img


if __name__ == "__main__":
  # read original image
  A__ :       Optional[Any]				= imread('image_data/lena.jpg', 1)

  # convert to its negative
  A__ :       Dict				= convert_to_negative(img)

  # show result image
  imshow('negative of original image', img)
  waitKey(0)
  destroyAllWindows()
