


from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def 	__lowerCamelCase						(							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						complex  ,							lowerCamelCase__		:						str = "x"  ,							lowerCamelCase__		:						float = 10**-10  ,							lowerCamelCase__		:						int = 1  ,							):





			'''simple docstring'''




			lowerCamelCase    		=  symbols(lowerCamelCase__      )
			lowerCamelCase    		=  lambdify(lowerCamelCase__  ,							lowerCamelCase__      )
			lowerCamelCase    		=  lambdify(lowerCamelCase__  ,							diff(lowerCamelCase__  ,							lowerCamelCase__      )      )

			lowerCamelCase    		=  starting_point

			while True:
						if diff_function(lowerCamelCase__      ) != 0:
									lowerCamelCase    		=  prev_guess - multiplicity * func(lowerCamelCase__      ) / diff_function(
									    lowerCamelCase__      )
						else:
									raise ZeroDivisionError("""Could not find root"""      ) from None

						# Precision is checked by comparing the difference of consecutive guesses
						if abs(next_guess - prev_guess      ) < precision:
									return next_guess

						lowerCamelCase    		=  next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

		# Find root of polynomial
		# Find fourth Root of 5
		print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    f"""{newton_raphson("exp(x) - 1", 10, precision=0.0_0_5)}""",
		)

		# Find root of cos(x)
		print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

