



"""simple docstring"""






import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler



class      A__  :




	'''simple docstring'''





	def __init__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			int:

				"""simple docstring"""

				if isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
							# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
							# modified it, it will not be accepted here again, since `auto` values would have been overridden
							__lowerCAmelCase :    Dict				=			deepcopy(_SCREAMING_SNAKE_CASE)
				elif os.path.exists(_SCREAMING_SNAKE_CASE):
							with io.open(_SCREAMING_SNAKE_CASE			,     "r"			,     encoding="utf-8") as f:
										__lowerCAmelCase :    Optional[Any]				=			json.load(_SCREAMING_SNAKE_CASE)
				else:
							try:
										__lowerCAmelCase :    int				=			baseaa.urlsafe_baadecode(_SCREAMING_SNAKE_CASE).decode("utf-8")
										__lowerCAmelCase :    Union[str, Any]				=			json.loads(_SCREAMING_SNAKE_CASE)
							except (UnicodeDecodeError, AttributeError, ValueError):
										raise ValueError(
										    F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}""")

				__lowerCAmelCase :    Optional[Any]				=			config

				self.set_stage_and_offload()

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			self.get_value("zero_optimization.stage"			,     -1)

				# offload
				__lowerCAmelCase :    Optional[int]				=			False
				if self.is_zeroa() or self.is_zeroa():
							__lowerCAmelCase :    List[str]				=			set(["cpu", "nvme"])
							__lowerCAmelCase :    Optional[int]				=			set(
							    [
							        self.get_value("zero_optimization.offload_optimizer.device"),
							        self.get_value("zero_optimization.offload_param.device"),
							    ])
							if len(offload_devices & offload_devices_valid) > 0:
										__lowerCAmelCase :    Any				=			True

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			self.config

				# find the config node of interest if it exists
				__lowerCAmelCase :    Dict				=			ds_key_long.split(".")
				__lowerCAmelCase :    Dict				=			nodes.pop()
				for node in nodes:
							__lowerCAmelCase :    Optional[Any]				=			config.get(_SCREAMING_SNAKE_CASE)
							if config is None:
										return None, ds_key

				return config, ds_key

	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[str]=None)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Any				=			self.find_config_node(_SCREAMING_SNAKE_CASE)
				if config is None:
							return default
				return config.get(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=False)							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			self.config

				# find the config node of interest if it exists
				__lowerCAmelCase :    Tuple				=			ds_key_long.split(".")
				for node in nodes:
							__lowerCAmelCase :    Dict				=			config
							__lowerCAmelCase :    Any				=			config.get(_SCREAMING_SNAKE_CASE)
							if config is None:
										if must_exist:
													raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}""")
										else:
													return

        # if found remove it
				if parent_config is not None:
							parent_config.pop(_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			self.get_value(_SCREAMING_SNAKE_CASE)
				return False if value is None else bool(_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.get_value(_SCREAMING_SNAKE_CASE)
				return False if value is None else not bool(_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			List[Any]:

				"""simple docstring"""

				return self._stage == 2

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			List[Any]:

				"""simple docstring"""

				return self._stage == 3







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			List[Any]:

				"""simple docstring"""

				return self._offload






class      A__  :




	'''simple docstring'''





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

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			engine







	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			int:

				"""simple docstring"""

				self.engine.backward(_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				# Deepspeed's `engine.step` performs the following operations:
				# - gradient accumulation check
				# - gradient clipping
				# - optimizer step
				# - zero grad
				# - checking overflow
				# - lr_scheduler step (only if engine.lr_scheduler is not None)
				self.engine.step()
				# and this plugin overrides the above calls with no-ops when Accelerate runs under
				# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
				# training loop that works transparently under many training regimes.






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Dict:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE			,     device_placement=_SCREAMING_SNAKE_CASE			,     scaler=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			hasattr(self.optimizer			,     "overflow")

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Any=None)							->			Optional[Any]:

				"""simple docstring"""

				pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			str:

				"""simple docstring"""

				pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Optional[int]:

				"""simple docstring"""

				if self.__has_overflow__:
							return self.optimizer.overflow
				return False






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Any:

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Tuple:

				"""simple docstring"""

				pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class      A__  :




	'''simple docstring'''





	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Any=0.001			,     _SCREAMING_SNAKE_CASE:						Tuple=0			,     **_SCREAMING_SNAKE_CASE:						Optional[Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			params
				__lowerCAmelCase :    Any				=			lr
				__lowerCAmelCase :    List[str]				=			weight_decay
				__lowerCAmelCase :    Dict				=			kwargs






class      A__  :




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						str=0			,     **_SCREAMING_SNAKE_CASE:						List[Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			optimizer
				__lowerCAmelCase :    Optional[int]				=			total_num_steps
				__lowerCAmelCase :    Optional[Any]				=			warmup_num_steps
				__lowerCAmelCase :    List[Any]				=			kwargs




"""simple docstring"""






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__snake_case				:  Optional[Any] =       pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__snake_case				:  int =       dataset.iloc[:, 1:2].values
__snake_case				:  Tuple =       dataset.iloc[:, 2].values


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				:  Tuple =       PolynomialFeatures(degree=4)
__snake_case				:  Any =       poly_reg.fit_transform(X)
__snake_case				:  List[str] =       LinearRegression()
pol_reg.fit(X_poly, y)


def      _lowercase							(    )		->  List[Any]:
			plt.scatter(__snake_case   ,__snake_case   ,color="red"      )
			plt.plot(__snake_case   ,pol_reg.predict(poly_reg.fit_transform(__snake_case      )      )   ,color="blue"      )
			plt.title("Truth or Bluff (Linear Regression)"      )
			plt.xlabel("Position level"      )
			plt.ylabel("Salary"      )
			plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003