





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__       :			List[Any]			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       :			Optional[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"""
        ),
    },
}
SCREAMING_SNAKE_CASE__       :			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"""
        ),
    },
}
SCREAMING_SNAKE_CASE__       :			Tuple			      =						{
    """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__       :			Optional[Any]			      =						{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
SCREAMING_SNAKE_CASE__       :			Dict			      =						{
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						{
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


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







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						str     		=				VOCAB_FILES_NAMES
   _UpperCamelCase :						List[Any]     		=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase :						int     		=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase :						Union[str, Any]     		=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Tuple     		=				VOCAB_FILES_NAMES
   _UpperCamelCase :						Union[str, Any]     		=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase :						str     		=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase :						int     		=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







SCREAMING_SNAKE_CASE__       :			Any			      =						collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

SCREAMING_SNAKE_CASE__       :			List[str]			      =						collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


SCREAMING_SNAKE_CASE__       :			Tuple			      =						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)

    Returns:
        `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   __lowerCAmelCase			:





   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					,			)				->  BatchEncoding:
      """simple docstring"""





      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__	:						Any      		=  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__	:						str      		=  titles if not isinstance(snake_case					,			snake_case      ) else [titles]
      a__	:						Optional[Any]      		=  texts if not isinstance(snake_case					,			snake_case      ) else [texts]
      a__	:						str      		=  len(snake_case      )
      a__	:						Tuple      		=  questions if not isinstance(snake_case					,			snake_case      ) else [questions] * n_passages
      if len(snake_case      ) != len(snake_case      ):
         raise ValueError(
             F"""There should be as many titles than texts but got {len(snake_case      )} titles and {len(snake_case      )} texts."""      )
      a__	:						Any      		=  super().__call__(snake_case					,			snake_case					,			padding=snake_case					,			truncation=snake_case      )["input_ids"]
      a__	:						Optional[int]      		=  super().__call__(snake_case					,			add_special_tokens=snake_case					,			padding=snake_case					,			truncation=snake_case      )["input_ids"]
      a__	:						str      		=  {
          "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__	:						Any      		=  []
         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__	:						Any      		=  attention_mask
      return self.pad(snake_case					,			padding=snake_case					,			max_length=snake_case					,			return_tensors=snake_case      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case = 16					,			snake_case = 64					,			snake_case = 4					,			)				->  List[DPRSpanPrediction]:
      """simple docstring"""





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

         a__	:						str      		=  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 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  List[DPRSpanPrediction]:
      """simple docstring"""





      a__	:						List[str]      		=  []
      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__	:						Optional[int]      		=  sorted(snake_case					,			key=lambda snake_case      : x[1]					,			reverse=snake_case      )
      a__	:						Dict      		=  []
      for (start_index, end_index), score in scores:
         if start_index > end_index:
            raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""      )
         a__	:						List[Any]      		=  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(snake_case      ) == top_spans:
            break
      return chosen_span_intervals







@add_end_docstrings(_UpperCamelCase  )
class   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase  ):
   _UpperCamelCase :						Union[str, Any]     		=				VOCAB_FILES_NAMES
   _UpperCamelCase :						Optional[Any]     		=				READER_PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase :						Optional[int]     		=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase :						List[str]     		=				READER_PRETRAINED_INIT_CONFIGURATION
   _UpperCamelCase :						List[Any]     		=				["""input_ids""", """attention_mask"""]








from PIL import Image






def       _A  (      lowerCamelCase							, lowerCamelCase   ):

   def brightness(lowerCamelCase   ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError("level must be between -255.0 (black) and 255.0 (white)"   )
   return img.point(lowerCamelCase   )


if __name__ == "__main__":
  # Load image
  with Image.open("""image_data/lena.jpg""") as img:
    # Change brightness to 100
    SCREAMING_SNAKE_CASE__       :			List[str]			      =						change_brightness(img, 1_0_0)
    brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

