


import matplotlib.pyplot as plt
import numpy as np






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Optional[Any]:




      """simple docstring"""

      snake_case_			= len(lowercase_   )  # Number of training samples
      snake_case_			= np.eye(lowercase_   )  # Initialize weights as identity matrix
      for j in range(lowercase_   ):
            snake_case_			= point - x_train[j]
            snake_case_			= np.exp(diff @ diff.T / (-2.0 * tau**2)   )

      return weights






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Optional[int]:




      """simple docstring"""

      snake_case_			= weight_matrix(lowercase_   ,   lowercase_   ,   lowercase_   )
      snake_case_			= np.linalg.inv(x_train.T @ weight_mat @ x_train   ) @ (
          x_train.T @ weight_mat @ y_train.T
      )

      return weight






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    int:




      """simple docstring"""

      snake_case_			= np.zeros(len(lowercase_   )   )  # Initialize array of predictions
      for i, item in enumerate(lowercase_   ):
            snake_case_			= item @ local_weight(lowercase_   ,   lowercase_   ,   lowercase_   ,   lowercase_   )

      return y_pred






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Dict:




      """simple docstring"""

      import seaborn as sns

      snake_case_			= sns.load_dataset(lowercase_   )
      snake_case_			= np.array(data[x_name]   )
      snake_case_			= np.array(data[y_name]   )

      snake_case_			= np.ones(len(lowercase_   )   )

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

      return x_train, x_data, y_data






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   )->    List[str]:




      """simple docstring"""

      snake_case_			= np.sort(lowercase_   ,   axis=0   )
      plt.scatter(lowercase_   ,   lowercase_   ,   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(lowercase_   )
      plt.ylabel(lowercase_   )
      plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod()

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





'''simple docstring'''







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCamelCase				:		str				      =    'src/transformers'

# Matches is_xxx_available()
_lowerCamelCase				:		str				      =    re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
_lowerCamelCase				:		Union[str, Any]				      =    re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
_lowerCamelCase				:		Dict				      =    re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


_lowerCamelCase				:		int				      =    '\n{0} = None\n'

_lowerCamelCase				:		List[Any]				      =    '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_lowerCamelCase				:		Any				      =    '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		if _re_test_backend.search(lowercase_    ) is None:
				return None
		_snake_case      			=			[b[0] for b in _re_backend.findall(lowercase_    )]
		backends.sort()
		return "_and_".join(lowercase_    )






def 						_lowercase						(      ):



		'''simple docstring'''







		with open(os.path.join(lowercase_		,				"__init__.py"    )		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
				_snake_case      			=			f.readlines()

		# Get to the point we do the actual imports for type checking
		_snake_case      			=			0
		while not lines[line_index].startswith("if TYPE_CHECKING"    ):
				line_index += 1

		_snake_case      			=			{}
		# Go through the end of the file
		while line_index < len(lowercase_    ):
				# If the line is an if is_backend_available, we grab all objects associated.
				_snake_case      			=			find_backend(lines[line_index]    )
				if backend is not None:
						while not lines[line_index].startswith("    else:"    ):
								line_index += 1
						line_index += 1

						_snake_case      			=			[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(" " * 8    ):
								_snake_case      			=			lines[line_index]
								_snake_case      			=			_re_single_line_import.search(lowercase_    )
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "    )    )
								elif line.startswith(" " * 12    ):
										objects.append(line[12:-2]    )
								line_index += 1

						_snake_case      			=			objects
				else:
						line_index += 1

		return backend_specific_objects






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if name.isupper():
				return DUMMY_CONSTANT.format(lowercase_    )
		elif name.islower():
				return DUMMY_FUNCTION.format(lowercase_		,				lowercase_    )
		else:
				return DUMMY_CLASS.format(lowercase_		,				lowercase_    )






def 						_lowercase						(      lowercase_=None    ):



		'''simple docstring'''







		if backend_specific_objects is None:
				_snake_case      			=			read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		_snake_case      			=			{}

		for backend, objects in backend_specific_objects.items():
				_snake_case      			=			"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"    )    ) + "]"
				_snake_case      			=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(lowercase_		,				lowercase_    ) for o in objects]    )
				_snake_case      			=			dummy_file

		return dummy_files






def 						_lowercase						(      lowercase_=False    ):



		'''simple docstring'''







		_snake_case      			=			create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		_snake_case      			=			{"torch": "pt"}

		# Locate actual dummy modules and read their content.
		_snake_case      			=			os.path.join(lowercase_		,				"utils"    )
		_snake_case      			=			{
		    backend: os.path.join(lowercase_		,				f'''dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py'''    )
		    for backend in dummy_files.keys()
		}

		_snake_case      			=			{}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(lowercase_    ):
						with open(lowercase_		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
								_snake_case      			=			f.read()
				else:
						_snake_case      			=			""

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    f'''Updating transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py as the main '''
								    "__init__ has new objects."    )
								with open(dummy_file_paths[backend]		,				"w"		,				encoding="utf-8"		,				newline="\n"    ) as f:
										f.write(dummy_files[backend]    )
						else:
								raise ValueError(
								    "The main __init__ has objects that are not present in "
								    f'''transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py. Run `make fix-copies` '''
								    "to fix this."    )


if __name__ == "__main__":
						_lowerCamelCase				:		int				      =    argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						_lowerCamelCase				:		List[Any]				      =    parser.parse_args()

						check_dummies(args.fix_and_overwrite)





