
'''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


a__						:				Optional[Any]           =    "block_records.npy"


a__						:				Dict           =    logging.get_logger(__name__)





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     np.ndarray:







      """simple docstring"""
      import tensorflow.compat.va as tf

      __A    		=     tf.data.TFRecordDataset(UpperCAmelCase ,					buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4		)
      __A    		=     blocks_dataset.batch(UpperCAmelCase ,					drop_remainder=UpperCAmelCase		)
      __A    		=     next(blocks_dataset.take(1		).as_numpy_iterator()		)

      return np_record


class 	UpperCamelCase__					:






 def __init__(     self					:Optional[int]		,			_A					:List[str]		,			_A					:int		,			_A					:Optional[Any]=2		,			_A					:Tuple=1_000		,			_A					:List[Any]=100		,			_A					:str=100_000		,			)			->     List[str]:







       '''simple docstring'''



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

       __A    		=     Builder(db=_A		,			num_neighbors=_A		,			distance_measure='dot_product'     )
       __A    		=     builder.tree(
           num_leaves=_A		,			num_leaves_to_search=_A		,			training_sample_size=_A     )
       __A    		=     builder.score_ah(dimensions_per_block=_A     )

       __A    		=     builder.build()






 def 		lowercase_  (     self					:List[str]		,			_A					:List[str]     )			->     Optional[Any]:







       '''simple docstring'''



       __A		, __A    		=     self.searcher.search_batched(question_projection.detach().cpu()     )
       return retrieved_block_ids.astype('int64'     )


class 	UpperCamelCase__					:






 def __init__(     self					:List[Any]		,			_A					:List[str]		,			_A					:str     )			->     Tuple:







       '''simple docstring'''



       super().__init__()
       __A    		=     block_records
       __A    		=     tokenizer






 def __call__(     self					:Dict		,			_A					:Any		,			_A					:Tuple		,			_A					:Union[str, Any]		,			_A					:Dict=None		,			_A					:List[str]="pt"     )			->     Any:







       '''simple docstring'''



       __A    		=     np.take(self.block_records		,			indices=_A		,			axis=0     )

       __A    		=     self.tokenizer.decode(question_input_ids[0]		,			skip_special_tokens=_A     )

       __A    		=     []
       __A    		=     []
       for retrieved_block in retrieved_blocks:
             text.append(_A     )
             text_pair.append(retrieved_block.decode()     )

       __A    		=     self.tokenizer(
           _A		,			_A		,			padding=_A		,			truncation=_A		,			return_special_tokens_mask=_A		,			max_length=_A     )
       __A    		=     concat_inputs.convert_to_tensors(_A     )

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






 @classmethod
 def 		lowercase_  (     cls					:Optional[Any]		,			_A					:Optional[Union[str, os.PathLike]]		,			*_A					:List[str]		,			**_A					:List[str]     )			->     int:







       '''simple docstring'''



       if os.path.isdir(_A     ):
             __A    		=     os.path.join(_A		,			_REALM_BLOCK_RECORDS_FILENAME     )
       else:
             __A    		=     hf_hub_download(
                 repo_id=_A		,			filename=_REALM_BLOCK_RECORDS_FILENAME		,			**_A     )
       __A    		=     np.load(_A		,			allow_pickle=_A     )

       __A    		=     AutoTokenizer.from_pretrained(_A		,			*_A		,			**_A     )

       return cls(_A		,			_A     )






 def 		lowercase_  (     self					:List[Any]		,			_A					:int     )			->     Dict:







       '''simple docstring'''



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






 def 		lowercase_  (     self					:Tuple		,			_A					:List[Any]		,			_A					:List[Any]     )			->     Dict:







       '''simple docstring'''



       __A    		=     []
       __A    		=     []
       __A    		=     []
       __A    		=     0

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

             if len(start_pos[-1]     ) == 0:
                   has_answers.append(_A     )
             else:
                   has_answers.append(_A     )
                   if len(start_pos[-1]     ) > max_answers:
                         __A    		=     len(start_pos[-1]     )

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








'''simple docstring'''





import math
import os

import cva
import numpy as np

a__						:				List[Any]           =    255.0





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     float:







      """simple docstring"""
      __A    		=     np.mean((original - contrast) ** 2		)
      if mse == 0:
            return 1_0_0

      return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase		)		)





def     snake_case							(						)->     None:







      """simple docstring"""
      __A    		=     os.path.dirname(os.path.realpath(UpperCAmelCase		)		)
      # Loading images (original image and compressed image)
      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/original_image.png'		)		)
      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/compressed_image.png'		) ,					1		)

      __A    		=     cva.imread(os.path.join(UpperCAmelCase ,					'image_data/PSNR-example-base.png'		)		)
      __A    		=     cva.imread(
          os.path.join(UpperCAmelCase ,					'image_data/PSNR-example-comp-10.jpg'		) ,					1		)

      # Value expected: 29.73dB
      print('-- First Test --'		)
      print(f'PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase ,					UpperCAmelCase		)} dB'		)

      # # Value expected: 31.53dB (Wikipedia Example)
      print('\n-- Second Test --'		)
      print(f'PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase ,					UpperCAmelCase		)} dB'		)


if __name__ == "__main__":
 main()






