




"""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


UpperCamelCase__    =      logging.get_logger(__name__)

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

UpperCamelCase__    =      {
    '''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__    =      {
    '''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__    =      {
    '''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__    =      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
UpperCamelCase__    =      {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
UpperCamelCase__    =      {
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


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




class 	a__      (							snake_case_	):
						snake_case__							   =			VOCAB_FILES_NAMES
						snake_case__							   =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						snake_case__							   =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__							   =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 	a__      (							snake_case_	):
						snake_case__							   =			VOCAB_FILES_NAMES
						snake_case__							   =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						snake_case__							   =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__							   =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

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


UpperCamelCase__    =      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(snake_case_	)
class 	a__      :





						def __call__(				self   :						List[str]  ,a__   :						Optional[int]  ,a__   :						Optional[str] = None  ,a__   :						Optional[str] = None  ,a__   :						Union[bool, str] = False  ,a__   :						Union[bool, str] = False  ,a__   :						Optional[int] = None  ,a__   :						Optional[Union[str, TensorType]] = None  ,a__   :						Optional[bool] = None  ,**a__   :						Optional[int]  ,)    ->							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:
																_lowerCAmelCase:Optional[int]						=	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  ,)
											_lowerCAmelCase:Tuple						=	titles if not isinstance(__snake_case  ,__snake_case) else [titles]
											_lowerCAmelCase:Tuple						=	texts if not isinstance(__snake_case  ,__snake_case) else [texts]
											_lowerCAmelCase:Union[str, Any]						=	len(__snake_case)
											_lowerCAmelCase:Union[str, Any]						=	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.')
											_lowerCAmelCase:str						=	super().__call__(__snake_case  ,__snake_case  ,padding=__snake_case  ,truncation=__snake_case)['''input_ids''']
											_lowerCAmelCase:Union[str, Any]						=	super().__call__(__snake_case  ,add_special_tokens=__snake_case  ,padding=__snake_case  ,truncation=__snake_case)['''input_ids''']
											_lowerCAmelCase:int						=	{
											    '''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:
																_lowerCAmelCase:Optional[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])
																_lowerCAmelCase:List[Any]						=	attention_mask
											return self.pad(__snake_case  ,padding=__snake_case  ,max_length=__snake_case  ,return_tensors=__snake_case)





						def    __UpperCamelCase  (				self   :						Optional[Any]  ,a__   :						BatchEncoding  ,a__   :						DPRReaderOutput  ,a__   :						int = 16  ,a__   :						int = 64  ,a__   :						int = 4  ,)    ->							List[DPRSpanPrediction]:




											"""simple docstring"""







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

																_lowerCAmelCase:Any						=	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    __UpperCamelCase  (				self   :						str  ,a__   :						List[int]  ,a__   :						List[int]  ,a__   :						int  ,a__   :						int  ,)    ->							List[DPRSpanPrediction]:




											"""simple docstring"""







											_lowerCAmelCase:Dict						=	[]
											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))
											_lowerCAmelCase:List[Any]						=	sorted(__snake_case  ,key=lambda a__: x[1]  ,reverse=__snake_case)
											_lowerCAmelCase: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}]')
																_lowerCAmelCase:Union[str, 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(snake_case_	)
class 	a__      (							snake_case_       ,						snake_case_	):
						snake_case__							   =			VOCAB_FILES_NAMES
						snake_case__							   =			READER_PRETRAINED_VOCAB_FILES_MAP
						snake_case__							   =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__							   =			READER_PRETRAINED_INIT_CONFIGURATION
						snake_case__							   =			['''input_ids''', '''attention_mask''']













'''simple docstring'''






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__							:  List[str]    =imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__							:  Union[str, Any]    =cvtColor(img, COLOR_BGR2GRAY)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				cn.convert_to_negative(lowerCAmelCase    )
      # assert negative_img array for at least one True
      assert negative_img.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""    ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowerCAmelCase				,      1_10    )    ).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""    )
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				canny.gen_gaussian_kernel(9				,      sigma=1.4    )
      # Assert ambiguous array
      assert resp.all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				imread("""digital_image_processing/image_data/lena_small.jpg"""				,      0    )
      # assert ambiguous array for all == True
      assert canny_img.all()
      _lowerCAmelCase					=				canny.canny(lowerCAmelCase    )
      # assert canny array for at least one True
      assert canny_array.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert gg.gaussian_filter(lowerCAmelCase				,      5				,      sigma=0.9    ).all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]    )
      _lowerCAmelCase					=				conv.img_convolve(lowerCAmelCase				,      lowerCAmelCase    ).astype(lowerCAmelCase    )
      assert res.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert med.median_filter(lowerCAmelCase				,      3    ).any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				sob.sobel_filter(lowerCAmelCase    )
      assert grad.any() and theta.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				sp.make_sepia(lowerCAmelCase				,      20    )
      assert sepia.all()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"    ):





      """simple docstring"""


      _lowerCAmelCase					=				bs.Burkes(imread(lowerCAmelCase				,      1    )				,      1_20    )
      burkes.process()
      assert burkes.output_img.any()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"				,      ):





      """simple docstring"""


      _lowerCAmelCase					=				rs.NearestNeighbour(imread(lowerCAmelCase				,      1    )				,      4_00				,      2_00    )
      nn.process()
      assert nn.output.any()





def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      _lowerCAmelCase					=				imread(lowerCAmelCase				,      0    )

      # Test for get_neighbors_pixel function() return not None
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				image[x_coordinate][y_coordinate]

      _lowerCAmelCase					=				lbp.get_neighbors_pixel(
          lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      _lowerCAmelCase					=				np.zeros((image.shape[0], image.shape[1])    )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0				,      image.shape[0]    ):
            for j in range(0				,      image.shape[1]    ):
                  _lowerCAmelCase					=				lbp.local_binary_value(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert lbp_image.any()






