

"""simple docstring"""




import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__A	:       Dict        =						get_logger(__name__)


if TYPE_CHECKING:
	from .builder import DatasetBuilder
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ = None ):
					"""simple docstring"""
					A     =   importlib.import_module(lowercase__ )

					# TODO(QL): always update the module to add subsequent new authentication
					if hasattr(lowercase__     ,				"_patched_for_streaming" ) and module._patched_for_streaming:
										return

					def wrap_auth(lowercase__ ):
										@wraps(lowercase__ )
										def wrapper(*lowercase__     ,				**lowercase__ ):
															return function(*lowercase__     ,				use_auth_token=lowercase__     ,				**lowercase__ )

										A     =   "wrap_auth"
										return wrapper

					# open files in a streaming fashion
					patch_submodule(lowercase__     ,				"open"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"os.listdir"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"os.walk"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"glob.glob"     ,				wrap_auth(lowercase__ ) ).start()
					# allow to navigate in remote zip files
					patch_submodule(lowercase__     ,				"os.path.join"     ,				lowercase__ ).start()
					patch_submodule(lowercase__     ,				"os.path.dirname"     ,				lowercase__ ).start()
					patch_submodule(lowercase__     ,				"os.path.basename"     ,				lowercase__ ).start()
					patch_submodule(lowercase__     ,				"os.path.relpath"     ,				lowercase__ ).start()
					patch_submodule(lowercase__     ,				"os.path.split"     ,				lowercase__ ).start()
					patch_submodule(lowercase__     ,				"os.path.splitext"     ,				lowercase__ ).start()
					# allow checks on paths
					patch_submodule(lowercase__     ,				"os.path.exists"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"os.path.isdir"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"os.path.isfile"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"os.path.getsize"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"pathlib.Path"     ,				lowercase__ ).start()
					# file readers
					patch_submodule(lowercase__     ,				"gzip.open"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"numpy.load"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"pandas.read_csv"     ,				wrap_auth(lowercase__ )     ,				attrs=["__version__"] ).start()
					patch_submodule(lowercase__     ,				"pandas.read_excel"     ,				wrap_auth(lowercase__ )     ,				attrs=["__version__"] ).start()
					patch_submodule(lowercase__     ,				"scipy.io.loadmat"     ,				wrap_auth(lowercase__ )     ,				attrs=["__version__"] ).start()
					patch_submodule(lowercase__     ,				"xml.etree.ElementTree.parse"     ,				wrap_auth(lowercase__ ) ).start()
					patch_submodule(lowercase__     ,				"xml.dom.minidom.parse"     ,				wrap_auth(lowercase__ ) ).start()
					A     =   True
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					extend_module_for_streaming(builder.__module__     ,				use_auth_token=builder.use_auth_token )
					# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
					if not builder.__module__.startswith("datasets." ):  # check that it's not a packaged builder like csv
										for imports in get_imports(inspect.getfile(builder.__class__ ) ):
															if imports[0] == "internal":
																				A     =   imports[1]
																				A     =   ".".join(builder.__module__.split("." )[:-1] + [internal_import_name] )
																				extend_module_for_streaming(lowercase__     ,				use_auth_token=builder.use_auth_token )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
					from .builder import DatasetBuilder

					A     =   [
					    cls.__module__
					    for cls in type(lowercase__ ).__mro__[1:]  # make sure it's not the same module we've already patched
					    if issubclass(cls     ,				lowercase__ ) and cls.__module__ != DatasetBuilder.__module__
					]  # check it's not a standard builder from datasets.builder
					for module in parent_builder_modules:
										extend_module_for_streaming(lowercase__     ,				use_auth_token=builder.use_auth_token )












import numpy as np






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       str      )						->      Union[str, Any]:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							str			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[Any]			 =		abs(predict - actual      )
     lowerCAmelCase_       :							Dict			 =		difference.mean()

     return score






def 				_lowerCAmelCase			(	_a :       Union[str, Any]       , _a :       int      )						->      Tuple:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							int			 =		np.array(_a      )

     lowerCAmelCase_       :							Union[str, Any]			 =		predict - actual
     lowerCAmelCase_       :							Tuple			 =		np.square(_a      )

     lowerCAmelCase_       :							Tuple			 =		square_diff.mean()
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Tuple      )						->      Optional[int]:
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							int			 =		predict - actual
     lowerCAmelCase_       :							Any			 =		np.square(_a      )
     lowerCAmelCase_       :							Union[str, Any]			 =		square_diff.mean()
     lowerCAmelCase_       :							Optional[Any]			 =		np.sqrt(_a      )
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Optional[Any]      )						->      List[Any]:
     lowerCAmelCase_       :							Dict			 =		np.array(_a      )
     lowerCAmelCase_       :							List[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							str			 =		np.log(predict + 1      )
     lowerCAmelCase_       :							Any			 =		np.log(actual + 1      )

     lowerCAmelCase_       :							List[str]			 =		log_predict - log_actual
     lowerCAmelCase_       :							Dict			 =		np.square(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		square_diff.mean()

     lowerCAmelCase_       :							int			 =		np.sqrt(_a      )

     return score






def 				_lowerCAmelCase			(	_a :       str       , _a :       List[Any]      )						->      int:
     lowerCAmelCase_       :							Any			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[int]			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[int]			 =		predict - actual
     lowerCAmelCase_       :							str			 =		np.sum(_a      ) / len(_a      )
     lowerCAmelCase_       :							List[str]			 =		np.sum(_a      ) / len(_a      )
     # print(numerator, denumerator)
     lowerCAmelCase_       :							int			 =		float(_a      ) / denumerator * 1_00

     return score







def 				_lowerCAmelCase			(	_a :       Any       , _a :       Union[str, Any]      )						->      str:
     return np.mean(np.array(_a      ) == np.array(_a      )      )
