



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__A       =						subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
__A       =						subprocess.check_output(f'git diff --name-only {fork_point_sha}'.split()).decode('''utf-8''').split()

__A       =						'''|'''.join(sys.argv[1:])
__A       =						re.compile(rf'^({joined_dirs}).*?\.py$')

__A       =						[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						pyspark.sql.DataFrame						,  __UpperCAmelCase     :						Optional[NamedSplit] = None						,  __UpperCAmelCase     :						Optional[Features] = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = "arrow"						,  **__UpperCAmelCase     :						str						,  )      ->			Dict:
							super().__init__(
							    split=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  keep_in_memory=__UpperCAmelCase						,  streaming=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
							UpperCAmelCase_=       load_from_cache_file
							UpperCAmelCase_=       file_format
							UpperCAmelCase_=       Spark(
							    df=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  working_dir=__UpperCAmelCase						,  **__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							if self.streaming:
													return self.builder.as_streaming_dataset(split=self.split					)
							UpperCAmelCase_=       None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
							self.builder.download_and_prepare(
							    download_mode=__UpperCAmelCase						,  file_format=self._file_format						,  )
							return self.builder.as_dataset(split=self.split					)




