





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


_UpperCamelCase       = get_logger(__name__)


if TYPE_CHECKING:
     from .builder import DatasetBuilder







def       lowerCAmelCase__(	lowercase       :    int   ,     lowercase       :    Optional[Union[str, bool]] = None				) ->    Optional[Any]:
  __snake_case   :				int        =       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       :    List[str]				):
    @wraps(lowercase				)
    def wrapper(*lowercase       :    Union[str, Any]   ,     **lowercase       :    Optional[int]				):
      return function(*lowercase   ,     use_auth_token=lowercase   ,     **lowercase				)

    __snake_case   :				Any        =       "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()
  __snake_case   :				Any        =       True







def       lowerCAmelCase__(	lowercase       :    "DatasetBuilder"				) ->    List[Any]:
  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":
        __snake_case   :				int        =       imports[1]
        __snake_case   :				Optional[Any]        =       ".".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

  __snake_case   :				Optional[Any]        =       [
      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 torch
from torch import autograd




class 				_lowerCamelCase (    autograd.Function    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Dict        =       inputs.numel()
        __snake_case   :				Optional[int]        =       int(0.005 * nb_elems   ) + 1
        if sigmoid:
          __snake_case   :				Any        =       (torch.sigmoid(UpperCAmelCase   ) > threshold).type(inputs.type()   )
        else:
          __snake_case   :				Optional[int]        =       (inputs > threshold).type(inputs.type()   )
        if mask.sum() < nb_min:
          # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
          __snake_case   :				List[str]        =       inputs.flatten().kthvalue(max(nb_elems - nb_min				,						1   )   ).values
          __snake_case   :				Union[str, Any]        =       (inputs > k_threshold).type(inputs.type()   )
        return mask







      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        return gradOutput, None, None




class 				_lowerCamelCase (    autograd.Function    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        __snake_case   :				Optional[int]        =       inputs.clone()
        __snake_case    ,   __snake_case   :				int        =       inputs.flatten().sort(descending=UpperCAmelCase   )
        __snake_case   :				Tuple        =       int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        __snake_case   :				Optional[int]        =       mask.flatten()
        __snake_case   :				Union[str, Any]        =       0
        __snake_case   :				Any        =       1
        return mask







      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:






        '''simple docstring'''






        return gradOutput, None




class 				_lowerCamelCase (    a    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:






        '''simple docstring'''






        __snake_case   :				Union[str, Any]        =       inputs.clone()
        __snake_case    ,   __snake_case   :				Optional[Any]        =       inputs.abs().flatten().sort(descending=UpperCAmelCase   )
        __snake_case   :				List[Any]        =       int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        __snake_case   :				Optional[Any]        =       mask.flatten()
        __snake_case   :				Tuple        =       0
        __snake_case   :				Any        =       1
        return mask

