

from __future__ import annotations







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):  # noqa: E741
						while r - l > 1:
												SCREAMING_SNAKE_CASE_: Tuple  		=       (l + r) // 2
												if v[m] >= key:
																		SCREAMING_SNAKE_CASE_: int  		=       m
												else:
																		SCREAMING_SNAKE_CASE_: Any  		=       m  # noqa: E741
						return r







def 			A_      (    _UpperCAmelCase    ):
						if len(_UpperCAmelCase    ) == 0:
												return 0

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [0] * len(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       1

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       v[0]

						for i in range(1				,	len(_UpperCAmelCase    )    ):
												if v[i] < tail[0]:
																		SCREAMING_SNAKE_CASE_: int  		=       v[i]
												elif v[i] > tail[length - 1]:
																		SCREAMING_SNAKE_CASE_: int  		=       v[i]
																		length += 1
												else:
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       v[i]

						return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()







'''simple docstring'''

import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast







@dataclass
class 				_lowercase     (				datasets.BuilderConfig			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[datasets.Features]      	=					None







class 				_lowercase     (				datasets.ArrowBasedBuilder			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					PandasConfig
 def a					(      self   :	Union[str, Any]							)  ->    int:
  return datasets.DatasetInfo(features=self.config.features							)
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    str:
  if not self.config.data_files:
   raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
  __lowerCAmelCase											=						dl_manager.download_and_extract(self.config.data_files							)
  if isinstance(SCREAMING_SNAKE_CASE__      ,       (str, list, tuple)							):
   __lowerCAmelCase											=						data_files
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,       gen_kwargs={"""files""": files}							)]
  __lowerCAmelCase											=						[]
  for split_name, files in data_files.items():
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__      ,       gen_kwargs={"""files""": files}							)							)
  return splits
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	pa.Table							)  ->    pa.Table:
  if self.config.features is not None:
   # more expensive cast to support nested features with keys in a different order
   # allows str <-> int/float or str to Audio for example
   __lowerCAmelCase											=						table_cast(SCREAMING_SNAKE_CASE__      ,       self.config.features.arrow_schema							)
  return pa_table





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    List[Any]:
  for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__							)							):
   with open(SCREAMING_SNAKE_CASE__      ,       """rb"""							) as f:
    __lowerCAmelCase											=						pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__							)							)
    yield i, self._cast_table(SCREAMING_SNAKE_CASE__							)






