


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


__lowerCamelCase   	=  get_logger(__name__)


if TYPE_CHECKING:
       from .builder import DatasetBuilder



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase = None			):
     a_     :  int				      =     importlib.import_module(__UpperCamelCase			)

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

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

          a_     :  str				      =     """wrap_auth"""
          return wrapper

     # open files in a streaming fashion
     patch_submodule(__UpperCamelCase	,	"""open"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.listdir"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.walk"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""glob.glob"""	,	wrap_auth(__UpperCamelCase			)			).start()
     # allow to navigate in remote zip files
     patch_submodule(__UpperCamelCase	,	"""os.path.join"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.dirname"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.basename"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.relpath"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.split"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.splitext"""	,	__UpperCamelCase			).start()
     # allow checks on paths
     patch_submodule(__UpperCamelCase	,	"""os.path.exists"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.isdir"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.isfile"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.getsize"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""pathlib.Path"""	,	__UpperCamelCase			).start()
     # file readers
     patch_submodule(__UpperCamelCase	,	"""gzip.open"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""numpy.load"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""pandas.read_csv"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""pandas.read_excel"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""scipy.io.loadmat"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""xml.etree.ElementTree.parse"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""xml.dom.minidom.parse"""	,	wrap_auth(__UpperCamelCase			)			).start()
     a_     :  Optional[int]				      =     True



def 			_a      (    __UpperCamelCase			):
     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_     :  int				      =     imports[1]
                    a_     :  int				      =     """.""".join(builder.__module__.split("""."""			)[:-1] + [internal_import_name]			)
                    extend_module_for_streaming(__UpperCamelCase	,	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_     :  Optional[int]				      =     [
         cls.__module__
         for cls in type(__UpperCamelCase			).__mro__[1:]  # make sure it's not the same module we've already patched
         if issubclass(cls	,	__UpperCamelCase			) 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(__UpperCamelCase	,	use_auth_token=builder.use_auth_token			)






import matplotlib.pyplot as plt
import numpy as np
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	len(__A      )  # Number of training samples
  UpperCAmelCase          =	np.eye(__A      )  # Initialize weights as identity matrix
  for j in range(__A      ):
    UpperCAmelCase          =	point - x_train[j]
    UpperCAmelCase          =	np.exp(diff @ diff.T / (-2.0 * tau**2)      )

  return weights
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A      ):
  UpperCAmelCase          =	weight_matrix(__A ,							__A ,							__A      )
  UpperCAmelCase          =	np.linalg.inv(x_train.T @ weight_mat @ x_train      ) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	np.zeros(len(__A      )      )  # Initialize array of predictions
  for i, item in enumerate(__A      ):
    UpperCAmelCase          =	item @ local_weight(__A ,							__A ,							__A ,							__A      )

  return y_pred
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  import seaborn as sns

  UpperCAmelCase          =	sns.load_dataset(__A      )
  UpperCAmelCase          =	np.array(data[x_name]      )
  UpperCAmelCase          =	np.array(data[y_name]      )

  UpperCAmelCase          =	np.ones(len(__A      )      )

  # pairing elements of one and x_data
  UpperCAmelCase          =	np.column_stack((one, x_data)      )

  return x_train, x_data, y_data
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A ,							__A ,							__A ,							):
  UpperCAmelCase          =	np.sort(__A ,							axis=0      )
  plt.scatter(__A ,							__A ,							color="blue"      )
  plt.plot(
      x_train_sorted[:, 1] ,							preds[x_train[:, 1].argsort(0      )] ,							color="yellow" ,							linewidth=5 ,							)
  plt.title("Local Weighted Regression"      )
  plt.xlabel(__A      )
  plt.ylabel(__A      )
  plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Demo with a dataset from the seaborn module
      lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							=			load_data("tips", "total_bill", "tip")
      lowerCAmelCase__							=			local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")



