






'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class      __SCREAMING_SNAKE_CASE      (			A_				):

 '''simple docstring'''





 def __init__(      self	:							Tuple					,      __lowercase	:							List[Any]					,      __lowercase	:							List[str] = None					,      __lowercase	:							int = None					,      __lowercase	:							Tuple = None					,      __lowercase	:							Dict = False					,      __lowercase	:							Any = False					,      __lowercase	:							int = None					,      __lowercase	:							Optional[int] = None					,      **__lowercase	:							Union[str, Any]					,      )	->						int:
       super().__init__(
           __lowercase					,      split=__lowercase					,      features=__lowercase					,      cache_dir=__lowercase					,      keep_in_memory=__lowercase					,      streaming=__lowercase					,      num_proc=__lowercase					,      **__lowercase					,      )
       SCREAMING_SNAKE_CASE__     :				Any									=field
       SCREAMING_SNAKE_CASE__     :				List[Any]									=path_or_paths if isinstance(__lowercase					,      __lowercase      ) else {self.split: path_or_paths}
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=Json(
           cache_dir=__lowercase					,      data_files=__lowercase					,      features=__lowercase					,      field=__lowercase					,      **__lowercase					,      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[int]:
       # Build iterable dataset
       if self.streaming:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.builder.as_streaming_dataset(split=self.split      )
       # Build regular (map-style) dataset
       else:
             SCREAMING_SNAKE_CASE__     :				Dict									=None
             SCREAMING_SNAKE_CASE__     :				str									=None
             SCREAMING_SNAKE_CASE__     :				int									=None
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=None

             self.builder.download_and_prepare(
                 download_config=__lowercase					,      download_mode=__lowercase					,      verification_mode=__lowercase					,      base_path=__lowercase					,      num_proc=self.num_proc					,      )
             SCREAMING_SNAKE_CASE__     :				Any									=self.builder.as_dataset(
                 split=self.split					,      verification_mode=__lowercase					,      in_memory=self.keep_in_memory      )
       return dataset






class      __SCREAMING_SNAKE_CASE      :

 '''simple docstring'''





 def __init__(      self	:							Tuple					,      __lowercase	:							List[Any]					,      __lowercase	:							Tuple					,      __lowercase	:							Optional[int] = None					,      __lowercase	:							Optional[Any] = None					,      **__lowercase	:							Any					,      )	->						Dict:
       if num_proc is not None and num_proc <= 0:
             raise ValueError(F"num_proc {num_proc} must be an integer > 0."      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=dataset
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=path_or_buf
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       SCREAMING_SNAKE_CASE__     :				List[Any]									=num_proc
       SCREAMING_SNAKE_CASE__     :				List[str]									="""utf-8"""
       SCREAMING_SNAKE_CASE__     :				int									=to_json_kwargs




 def      __magic_name__	(      self	:							int      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.to_json_kwargs.pop('''path_or_buf'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.to_json_kwargs.pop('''orient'''					,      '''records'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.to_json_kwargs.pop('''lines'''					,      True if orient == '''records''' else False      )
       SCREAMING_SNAKE_CASE__     :				str									=self.to_json_kwargs.pop('''index'''					,      False if orient in ['''split''', '''table'''] else True      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.to_json_kwargs.pop('''compression'''					,      __lowercase      )

       if compression not in [None, "infer", "gzip", "bz2", "xz"]:
             raise NotImplementedError(F"`datasets` currently does not support {compression} compression"      )

       if isinstance(self.path_or_buf					,      (str, bytes, os.PathLike)      ):
             with fsspec.open(self.path_or_buf					,      '''wb'''					,      compression=__lowercase      ) as buffer:
                   SCREAMING_SNAKE_CASE__     :				str									=self._write(file_obj=__lowercase					,      orient=__lowercase					,      lines=__lowercase					,      index=__lowercase					,      **self.to_json_kwargs      )
       else:
             if compression:
                   raise NotImplementedError(
                       F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
                       ''' was passed. Please provide a local path instead.'''      )
             SCREAMING_SNAKE_CASE__     :				str									=self._write(
                 file_obj=self.path_or_buf					,      orient=__lowercase					,      lines=__lowercase					,      index=__lowercase					,      **self.to_json_kwargs      )
       return written




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Any      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=args

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=query_table(
           table=self.dataset.data					,      key=slice(__lowercase					,      offset + self.batch_size      )					,      indices=self.dataset._indices					,      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=batch.to_pandas().to_json(
           path_or_buf=__lowercase					,      orient=__lowercase					,      lines=__lowercase					,      index=__lowercase					,      **__lowercase      )
       if not json_str.endswith('''\n'''      ):
             json_str += "\n"
       return json_str.encode(self.encoding      )




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							Optional[int]					,      __lowercase	:							List[Any]					,      __lowercase	:							Tuple					,      **__lowercase	:							Optional[int]					,      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=0

       if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0					,      len(self.dataset      )					,      self.batch_size      )					,      unit='''ba'''					,      disable=not logging.is_progress_bar_enabled()					,      desc='''Creating json from Arrow format'''					,      ):
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=self._batch_json((offset, orient, lines, index, to_json_kwargs)      )
                   written += file_obj.write(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				int									=len(self.dataset      ), self.batch_size
             with multiprocessing.Pool(self.num_proc      ) as pool:
                   for json_str in logging.tqdm(
                       pool.imap(
                           self._batch_json					,      [(offset, orient, lines, index, to_json_kwargs) for offset in range(0					,      __lowercase					,      __lowercase      )]					,      )					,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,      unit='''ba'''					,      disable=not logging.is_progress_bar_enabled()					,      desc='''Creating json from Arrow format'''					,      ):
                         written += file_obj.write(__lowercase      )

       return written







'''simple docstring'''
def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=0
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=len(UpperCamelCase__							) - 1

      while left <= right:
            # avoid divided by 0 during interpolation
            if sorted_collection[left] == sorted_collection[right]:
                  if sorted_collection[left] == item:
                        return left
                  else:
                        return None

            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

            # out of range check
            if point < 0 or point >= len(UpperCamelCase__							):
                  return None

            SCREAMING_SNAKE_CASE__     :				Optional[int]									=sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left
                        SCREAMING_SNAKE_CASE__     :				Optional[Any]									=point
                  elif point > right:
                        SCREAMING_SNAKE_CASE__     :				Optional[int]									=right
                        SCREAMING_SNAKE_CASE__     :				Tuple									=point
                  else:
                        if item < current_item:
                              SCREAMING_SNAKE_CASE__     :				str									=point - 1
                        else:
                              SCREAMING_SNAKE_CASE__     :				Tuple									=point + 1
      return None





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      SCREAMING_SNAKE_CASE__     :				Dict									=left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(UpperCamelCase__							):
            return None

      if sorted_collection[point] == item:
            return point
      elif point < left:
            return interpolation_search_by_recursion(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
      elif point > right:
            return interpolation_search_by_recursion(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
      else:
            if sorted_collection[point] > item:
                  return interpolation_search_by_recursion(
                      UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	point - 1							)
            else:
                  return interpolation_search_by_recursion(
                      UpperCamelCase__,	UpperCamelCase__,	point + 1,	UpperCamelCase__							)





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      if collection != sorted(UpperCamelCase__							):
            raise ValueError('''Collection must be ascending sorted'''							)
      return True


if __name__ == "__main__":
  import sys
  a_					= 0
  if debug == 1:
    a_					= [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
    try:
      __assert_sorted(collection)
    except ValueError:
      sys.exit('Sequence must be ascending sorted to apply interpolation search')
    a_					= 6_7

  a_					= interpolation_search(collection, target)
  if result is not None:
    print(F'''{target} found at positions: {result}''')
  else:
    print('Not found')