
'''simple docstring'''


import numpy as np




class 						__snake_case :




		def __init__(     self				:List[str] ,						_SCREAMING_SNAKE_CASE				:str=5 ,						_SCREAMING_SNAKE_CASE				:Tuple=5)				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  Tuple				    =    depth
								UpperCamelCase_    :  Tuple				    =    0
								UpperCamelCase_    :  Optional[Any]				    =    None
								UpperCamelCase_    :  Tuple				    =    None
								UpperCamelCase_    :  Optional[Any]				    =    min_leaf_size
								UpperCamelCase_    :  str				    =    None




		def   _snake_case  (     self				:int ,						_SCREAMING_SNAKE_CASE				:Tuple ,						_SCREAMING_SNAKE_CASE				:Optional[Any])				-> Any:






								'''simple docstring'''






								if labels.ndim != 1:
														print("""Error: Input labels must be one dimensional""")

								return np.mean((labels - prediction) ** 2)




		def   _snake_case  (     self				:Optional[int] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:Dict)				-> str:






								'''simple docstring'''






								if x.ndim != 1:
														print("""Error: Input data set must be one dimensional""")
														return
								if len(_SCREAMING_SNAKE_CASE) != len(_SCREAMING_SNAKE_CASE):
														print("""Error: X and y have different lengths""")
														return
								if y.ndim != 1:
														print("""Error: Data set labels must be one dimensional""")
														return

								if len(_SCREAMING_SNAKE_CASE) < 2 * self.min_leaf_size:
														UpperCamelCase_    :  List[Any]				    =    np.mean(_SCREAMING_SNAKE_CASE)
														return

								if self.depth == 1:
														UpperCamelCase_    :  str				    =    np.mean(_SCREAMING_SNAKE_CASE)
														return

								UpperCamelCase_    :  Tuple				    =    0
								UpperCamelCase_    :  Tuple				    =    self.mean_squared_error(_SCREAMING_SNAKE_CASE ,						np.mean(_SCREAMING_SNAKE_CASE)) * 2
								for i in range(len(_SCREAMING_SNAKE_CASE)):
														if len(x[:i]) < self.min_leaf_size:
																				continue
														elif len(x[i:]) < self.min_leaf_size:
																				continue
														else:
																				UpperCamelCase_    :  Any				    =    self.mean_squared_error(x[:i] ,						np.mean(y[:i]))
																				UpperCamelCase_    :  int				    =    self.mean_squared_error(x[i:] ,						np.mean(y[i:]))
																				UpperCamelCase_    :  Any				    =    error_left + error_right
																				if error < min_error:
																										UpperCamelCase_    :  Any				    =    i
																										UpperCamelCase_    :  Dict				    =    error

								if best_split != 0:
														UpperCamelCase_    :  Union[str, Any]				    =    x[:best_split]
														UpperCamelCase_    :  List[str]				    =    y[:best_split]
														UpperCamelCase_    :  Optional[Any]				    =    x[best_split:]
														UpperCamelCase_    :  List[Any]				    =    y[best_split:]

														UpperCamelCase_    :  Tuple				    =    x[best_split]
														UpperCamelCase_    :  str				    =    DecisionTree(
														    depth=self.depth - 1 ,						min_leaf_size=self.min_leaf_size)
														UpperCamelCase_    :  Dict				    =    DecisionTree(
														    depth=self.depth - 1 ,						min_leaf_size=self.min_leaf_size)
														self.left.train(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
														self.right.train(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								else:
														UpperCamelCase_    :  List[Any]				    =    np.mean(_SCREAMING_SNAKE_CASE)

								return





		def   _snake_case  (     self				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any])				-> List[Any]:






								'''simple docstring'''






								if self.prediction is not None:
														return self.prediction
								elif self.left or self.right is not None:
														if x >= self.decision_boundary:
																				return self.right.predict(_SCREAMING_SNAKE_CASE)
														else:
																				return self.left.predict(_SCREAMING_SNAKE_CASE)
								else:
														print("""Error: Decision tree not yet trained""")
														return None







class 						__snake_case :




		@staticmethod
		def   _snake_case  (     _SCREAMING_SNAKE_CASE				:Tuple ,						_SCREAMING_SNAKE_CASE				:Optional[Any])				-> Tuple:






								'''simple docstring'''






								UpperCamelCase_    :  Union[str, Any]				    =    float(0)
								for label in labels:
														squared_error_sum += (label - prediction) ** 2

								return float(squared_error_sum / labels.size)







def 				__magic_name__(					):
						UpperCamelCase_    :  Optional[int]				    =    np.arange(-1.0       ,							1.0       ,							0.005  )
						UpperCamelCase_    :  Union[str, Any]				    =    np.sin(UpperCAmelCase__  )

						UpperCamelCase_    :  str				    =    DecisionTree(depth=10       ,							min_leaf_size=10  )
						tree.train(UpperCAmelCase__       ,							UpperCAmelCase__  )

						UpperCamelCase_    :  Dict				    =    (np.random.rand(10  ) * 2) - 1
						UpperCamelCase_    :  Tuple				    =    np.array([tree.predict(UpperCAmelCase__  ) for x in test_cases]  )
						UpperCamelCase_    :  Tuple				    =    np.mean((predictions - test_cases) ** 2  )

						print("""Test values: """ + str(UpperCAmelCase__  )  )
						print("""Predictions: """ + str(UpperCAmelCase__  )  )
						print("""Average error: """ + str(UpperCAmelCase__  )  )


if __name__ == "__main__":
	main()
	import doctest

	doctest.testmod(name="mean_squarred_error", verbose=True)






'''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
__SCREAMING_SNAKE_CASE  =			"src/transformers"

# Matches is_xxx_available()
__SCREAMING_SNAKE_CASE  =			re.compile(r"is\_([a-z_]*)_available()")
# Matches from xxx import bla
__SCREAMING_SNAKE_CASE  =			re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
__SCREAMING_SNAKE_CASE  =			re.compile(r"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


__SCREAMING_SNAKE_CASE  =			"\n{0} = None\n"

__SCREAMING_SNAKE_CASE  =			"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


__SCREAMING_SNAKE_CASE  =			"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"
def 				__magic_name__(					UpperCAmelCase__   :					List[Any]  ):
						if _re_test_backend.search(UpperCAmelCase__  ) is None:
												return None
						UpperCamelCase_    :  str				    =    [b[0] for b in _re_backend.findall(UpperCAmelCase__  )]
						backends.sort()
						return "_and_".join(UpperCAmelCase__  )
def 				__magic_name__(					):
						with open(os.path.join(UpperCAmelCase__       ,							"""__init__.py"""  )       ,							"""r"""       ,							encoding="""utf-8"""       ,							newline="""\n"""  ) as f:
												UpperCamelCase_    :  Dict				    =    f.readlines()

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

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

																		UpperCamelCase_    :  Dict				    =    []
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]  ) <= 1 or lines[line_index].startswith(""" """ * 8  ):
																								UpperCamelCase_    :  Any				    =    lines[line_index]
																								UpperCamelCase_    :  str				    =    _re_single_line_import.search(UpperCAmelCase__  )
																								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

																		UpperCamelCase_    :  List[Any]				    =    objects
												else:
																		line_index += 1

						return backend_specific_objects
def 				__magic_name__(					UpperCAmelCase__   :					Any       ,							UpperCAmelCase__   :					Optional[Any]  ):
						if name.isupper():
												return DUMMY_CONSTANT.format(UpperCAmelCase__  )
						elif name.islower():
												return DUMMY_FUNCTION.format(UpperCAmelCase__       ,							UpperCAmelCase__  )
						else:
												return DUMMY_CLASS.format(UpperCAmelCase__       ,							UpperCAmelCase__  )
def 				__magic_name__(					UpperCAmelCase__   :					List[Any]=None  ):
						if backend_specific_objects is None:
												UpperCamelCase_    :  Optional[Any]				    =    read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						UpperCamelCase_    :  List[str]				    =    {}

						for backend, objects in backend_specific_objects.items():
												UpperCamelCase_    :  Optional[Any]				    =    """[""" + """, """.join(f"""\"{b}\"""" for b in backend.split("""_and_"""  )  ) + """]"""
												UpperCamelCase_    :  Union[str, Any]				    =    """# 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(UpperCAmelCase__       ,							UpperCAmelCase__  ) for o in objects]  )
												UpperCamelCase_    :  List[Any]				    =    dummy_file

						return dummy_files
def 				__magic_name__(					UpperCAmelCase__   :					List[Any]=False  ):
						UpperCamelCase_    :  int				    =    create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						UpperCamelCase_    :  Union[str, Any]				    =    {"""torch""": """pt"""}

						# Locate actual dummy modules and read their content.
						UpperCamelCase_    :  Optional[int]				    =    os.path.join(UpperCAmelCase__       ,							"""utils"""  )
						UpperCamelCase_    :  str				    =    {
						    backend: os.path.join(UpperCAmelCase__       ,							f"""dummy_{short_names.get(UpperCAmelCase__       ,							UpperCAmelCase__  )}_objects.py"""  )
						    for backend in dummy_files.keys()
						}

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

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    f"""Updating transformers.utils.dummy_{short_names.get(UpperCAmelCase__       ,							UpperCAmelCase__  )}_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(UpperCAmelCase__       ,							UpperCAmelCase__  )}_objects.py. Run `make fix-copies` """
																								    """to fix this."""  )


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE  =			argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	__SCREAMING_SNAKE_CASE  =			parser.parse_args()

	check_dummies(args.fix_and_overwrite)




