



'''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


SCREAMING_SNAKE_CASE__     : Optional[int]				=   """block_records.npy"""


SCREAMING_SNAKE_CASE__     : int				=   logging.get_logger(__name__)







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		import tensorflow.compat.va as tf

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      tf.data.TFRecordDataset(__SCREAMING_SNAKE_CASE ,		buffer_size=512 * 1024 * 1024     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      blocks_dataset.batch(__SCREAMING_SNAKE_CASE ,		drop_remainder=__SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Any										=      next(blocks_dataset.take(1     ).as_numpy_iterator()     )

		return np_record




class 				__lowerCAmelCase:






			def __init__(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=2      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=1_000      ,       SCREAMING_SNAKE_CASE  :							Dict=100      ,       SCREAMING_SNAKE_CASE  :							Tuple=100_000      ,       ):




					"""simple docstring"""







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

					SCREAMING_SNAKE_CASE_						:Tuple										=      Builder(db=lowercase_      ,       num_neighbors=lowercase_      ,       distance_measure='dot_product'	)
					SCREAMING_SNAKE_CASE_						:str										=      builder.tree(
					    num_leaves=lowercase_      ,       num_leaves_to_search=lowercase_      ,       training_sample_size=lowercase_	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      builder.score_ah(dimensions_per_block=lowercase_	)

					SCREAMING_SNAKE_CASE_						:Any										=      builder.build()






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.searcher.search_batched(question_projection.detach().cpu()	)
					return retrieved_block_ids.astype('int64'	)




class 				__lowerCAmelCase:






			def __init__(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					super().__init__()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      block_records
					SCREAMING_SNAKE_CASE_						:int										=      tokenizer






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




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      np.take(self.block_records      ,       indices=lowercase_      ,       axis=0	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.tokenizer.decode(question_input_ids[0]      ,       skip_special_tokens=lowercase_	)

					SCREAMING_SNAKE_CASE_						:Any										=      []
					SCREAMING_SNAKE_CASE_						:int										=      []
					for retrieved_block in retrieved_blocks:
							text.append(lowercase_	)
							text_pair.append(retrieved_block.decode()	)

					SCREAMING_SNAKE_CASE_						:Any										=      self.tokenizer(
					    lowercase_      ,       lowercase_      ,       padding=lowercase_      ,       truncation=lowercase_      ,       return_special_tokens_mask=lowercase_      ,       max_length=lowercase_	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      concat_inputs.convert_to_tensors(lowercase_	)

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






			@classmethod
			def 				_lowercase	(   cls  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       *SCREAMING_SNAKE_CASE  :							List[str]      ,       **SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







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

					SCREAMING_SNAKE_CASE_						:List[str]										=      AutoTokenizer.from_pretrained(lowercase_      ,       *lowercase_      ,       **lowercase_	)

					return cls(lowercase_      ,       lowercase_	)






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      []
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      []
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      []
					SCREAMING_SNAKE_CASE_						:List[Any]										=      0

					for input_id in concat_inputs.input_ids:
							SCREAMING_SNAKE_CASE_						:Any										=      input_id.tolist()
							# Check answers between two [SEP] tokens
							SCREAMING_SNAKE_CASE_						:Dict										=      input_id_list.index(self.tokenizer.sep_token_id	)
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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      ,       lowercase_	):
											if answer[0] == input_id_list[idx]:
													if input_id_list[idx : idx + len(lowercase_	)] == answer:
															start_pos[-1].append(lowercase_	)
															end_pos[-1].append(idx + len(lowercase_	) - 1	)

							if len(start_pos[-1]	) == 0:
									has_answers.append(lowercase_	)
							else:
									has_answers.append(lowercase_	)
									if len(start_pos[-1]	) > max_answers:
											SCREAMING_SNAKE_CASE_						:int										=      len(start_pos[-1]	)

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


'''simple docstring'''
import numpy as np
from PIL import Image

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_      ,			snake_case_      ,			snake_case_			:     List[str]        =    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    return (gray > 1_2_7) & (gray <= 2_5_5)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Any        =    np.zeros_like(__SCREAMING_SNAKE_CASE							)
    snake_case_			:     Tuple        =    np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)							)

    # Copy image to padded image
    snake_case_			:     Union[str, Any]        =    image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]							):
        for y in range(image.shape[0]							):
            snake_case_			:     List[str]        =    (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            snake_case_			:     Dict        =    int(summation == 5							)
    return output


# kernel to be applied
a_           =    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
  # read original image
  a_           =    np.array(Image.open(R"..\image_data\lena.jpg"))
  # Apply erosion operation to a binary image
  a_           =    erosion(grayabinary(rgbagray(image)), structuring_element)
  # Save the output image
  a_           =    Image.fromarray(output).convert("RGB")
  pil_img.save("result_erosion.png")




