





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase :    Tuple  							= {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase :    Any  							= [
      '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''OPTForCausalLM''',
      '''OPTModel''',
      '''OPTPreTrainedModel''',
      '''OPTForSequenceClassification''',
      '''OPTForQuestionAnswering''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase :    Optional[int]  							= ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase :    Dict  							= [
      '''FlaxOPTForCausalLM''',
      '''FlaxOPTModel''',
      '''FlaxOPTPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

  _lowerCamelCase :    Any  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













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




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  complex			,				SCREAMING_SNAKE_CASE__      :  str = "x"			,				SCREAMING_SNAKE_CASE__      :  float = 10**-10			,				SCREAMING_SNAKE_CASE__      :  int = 1			,				)	->    complex:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						symbols(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						lambdify(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						lambdify(SCREAMING_SNAKE_CASE__			,				diff(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       )

							SCREAMING_SNAKE_CASE__       :      Any     =						starting_point

							while True:
														if diff_function(SCREAMING_SNAKE_CASE__       ) != 0:
																					SCREAMING_SNAKE_CASE__       :      Any     =						prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE__       ) / diff_function(
																					    SCREAMING_SNAKE_CASE__       )
														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

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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', 1_0, precision=0.005)}",
		)

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






