




'''simple docstring'''




import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


snake_case__      				=  """block_records.npy"""


snake_case__      				=  logging.get_logger(__name__)




def   snake_case__			(       lowerCamelCase__ :      Union[str, Any]	,						lowerCamelCase__ :      Dict	)      ->      Optional[Any]:
					import tensorflow.compat.va as tf

					A_       :   Union[str, Any]       =							tf.data.TFRecordDataset(_snake_case	,						buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4	)
					A_       :   Any       =							blocks_dataset.batch(_snake_case	,						drop_remainder=_snake_case	)
					A_       :   Union[str, Any]       =							next(blocks_dataset.take(1	).as_numpy_iterator()	)

					return np_record






class 			UpperCamelCase_   :







						"""simple docstring"""


						def __init__(   self					:				Optional[Any]							,       _lowerCamelCase					:				List[Any]							,       _lowerCamelCase					:				str							,       _lowerCamelCase					:				Union[str, Any]=2							,       _lowerCamelCase					:				Optional[int]=1000							,       _lowerCamelCase					:				Dict=100							,       _lowerCamelCase					:				Dict=100000							,       ):





											"""simple docstring"""







											from scann.scann_ops.py.scann_ops_pybind import builder as Builder

											A_       :   str       =							Builder(db=_lowerCamelCase							,       num_neighbors=_lowerCamelCase							,       distance_measure='''dot_product'''			)
											A_       :   List[str]       =							builder.tree(
											    num_leaves=_lowerCamelCase							,       num_leaves_to_search=_lowerCamelCase							,       training_sample_size=_lowerCamelCase			)
											A_       :   Union[str, Any]       =							builder.score_ah(dimensions_per_block=_lowerCamelCase			)

											A_       :   Union[str, Any]       =							builder.build()







						def 					_a			(   self					:				List[str]							,       _lowerCamelCase					:				int			):





											"""simple docstring"""







											A_       ,A_       :   Union[str, Any]       =							self.searcher.search_batched(question_projection.detach().cpu()			)
											return retrieved_block_ids.astype('''int64'''			)






class 			UpperCamelCase_   :







						"""simple docstring"""


						def __init__(   self					:				Tuple							,       _lowerCamelCase					:				str							,       _lowerCamelCase					:				Tuple			):





											"""simple docstring"""







											super().__init__()
											A_       :   int       =							block_records
											A_       :   Tuple       =							tokenizer







						def __call__(   self					:				Optional[Any]							,       _lowerCamelCase					:				Any							,       _lowerCamelCase					:				List[str]							,       _lowerCamelCase					:				Optional[Any]							,       _lowerCamelCase					:				Union[str, Any]=None							,       _lowerCamelCase					:				int="pt"			):





											"""simple docstring"""







											A_       :   Union[str, Any]       =							np.take(self.block_records							,       indices=_lowerCamelCase							,       axis=0			)

											A_       :   Optional[Any]       =							self.tokenizer.decode(question_input_ids[0]							,       skip_special_tokens=_lowerCamelCase			)

											A_       :   int       =							[]
											A_       :   Tuple       =							[]
											for retrieved_block in retrieved_blocks:
																text.append(_lowerCamelCase			)
																text_pair.append(retrieved_block.decode()			)

											A_       :   Dict       =							self.tokenizer(
											    _lowerCamelCase							,       _lowerCamelCase							,       padding=_lowerCamelCase							,       truncation=_lowerCamelCase							,       return_special_tokens_mask=_lowerCamelCase							,       max_length=_lowerCamelCase			)
											A_       :   Tuple       =							concat_inputs.convert_to_tensors(_lowerCamelCase			)

											if answer_ids is not None:
																return self.block_has_answer(_lowerCamelCase							,       _lowerCamelCase			) + (concat_inputs_tensors,)
											else:
																return (None, None, None, concat_inputs_tensors)







						@classmethod
						def 					_a			(   cls					:				Optional[int]							,       _lowerCamelCase					:				Union[str, Any]							,       *_lowerCamelCase					:				Optional[Any]							,       **_lowerCamelCase					:				Optional[Any]			):





											"""simple docstring"""







											if os.path.isdir(_lowerCamelCase			):
																A_       :   Optional[Any]       =							os.path.join(_lowerCamelCase							,       _REALM_BLOCK_RECORDS_FILENAME			)
											else:
																A_       :   List[str]       =							hf_hub_download(
																    repo_id=_lowerCamelCase							,       filename=_REALM_BLOCK_RECORDS_FILENAME							,       **_lowerCamelCase			)
											A_       :   Optional[int]       =							np.load(_lowerCamelCase							,       allow_pickle=_lowerCamelCase			)

											A_       :   Dict       =							AutoTokenizer.from_pretrained(_lowerCamelCase							,       *_lowerCamelCase							,       **_lowerCamelCase			)

											return cls(_lowerCamelCase							,       _lowerCamelCase			)







						def 					_a			(   self					:				str							,       _lowerCamelCase					:				Any			):





											"""simple docstring"""







											np.save(os.path.join(_lowerCamelCase							,       _REALM_BLOCK_RECORDS_FILENAME			)							,       self.block_records			)
											# save tokenizer
											self.tokenizer.save_pretrained(_lowerCamelCase			)







						def 					_a			(   self					:				Union[str, Any]							,       _lowerCamelCase					:				Any							,       _lowerCamelCase					:				Optional[int]			):





											"""simple docstring"""







											A_       :   Union[str, Any]       =							[]
											A_       :   Optional[Any]       =							[]
											A_       :   List[Any]       =							[]
											A_       :   Any       =							0

											for input_id in concat_inputs.input_ids:
																A_       :   int       =							input_id.tolist()
																# Check answers between two [SEP] tokens
																A_       :   Union[str, Any]       =							input_id_list.index(self.tokenizer.sep_token_id			)
																A_       :   str       =							first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id			)

																start_pos.append([]			)
																end_pos.append([]			)
																for answer in answer_ids:
																					for idx in range(first_sep_idx + 1							,       _lowerCamelCase			):
																										if answer[0] == input_id_list[idx]:
																															if input_id_list[idx : idx + len(_lowerCamelCase			)] == answer:
																																				start_pos[-1].append(_lowerCamelCase			)
																																				end_pos[-1].append(idx + len(_lowerCamelCase			) - 1			)

																if len(start_pos[-1]			) == 0:
																					has_answers.append(_lowerCamelCase			)
																else:
																					has_answers.append(_lowerCamelCase			)
																					if len(start_pos[-1]			) > max_answers:
																										A_       :   List[Any]       =							len(start_pos[-1]			)

        # Pad -1 to max_answers
											for start_pos_, end_pos_ in zip(_lowerCamelCase							,       _lowerCamelCase			):
																if len(start_pos_			) < max_answers:
																					A_       :   Optional[Any]       =							[-1] * (max_answers - len(start_pos_			))
																					start_pos_ += padded
																					end_pos_ += padded
											return has_answers, start_pos, end_pos











"""simple docstring"""







import cva
import numpy as np

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      try:
            return int(image[x_coordinate][y_coordinate] >= center							)
      except (IndexError, TypeError):
            return 0

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       image[x_coordinate][y_coordinate]
      UpperCAmelCase							=       [1, 2, 4, 8, 16, 32, 64, 128]

      # skip get_neighbors_pixel if center is null
      if center is None:
            return 0

      # Starting from the top right, assigning value to pixels clockwise
      UpperCAmelCase							=       [
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       _snake_case      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       _snake_case      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       _snake_case      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       y_coordinate - 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       _snake_case      ,       y_coordinate - 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       y_coordinate - 1      ,       _snake_case							),
      ]

      # Converting the binary value to decimal.
      return sum(
          binary_value * power for binary_value, power in zip(_snake_case      ,       _snake_case							)							)


if __name__ == "__main__":
  # Reading the image and converting it to grayscale.
  _UpperCamelCase     			=  cva.imread(
      """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
  )

  # Create a numpy array as the same height and width of read image
  _UpperCamelCase     			=  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]):
      _UpperCamelCase     			=  local_binary_value(image, i, j)

  cva.imshow("""local binary pattern""", lbp_image)
  cva.waitKey(0)
  cva.destroyAllWindows()



