



'''simple docstring'''







import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 				__lowerCAmelCase(							a__      ):
			__snake_case  :		Dict		=      'config.json'






			@register_to_config
			def __init__(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=2      ,       SCREAMING_SNAKE_CASE  :							str=5      ,       SCREAMING_SNAKE_CASE  :							Tuple=(2, 5)      ,       SCREAMING_SNAKE_CASE  :							Tuple="for diffusion"      ,       SCREAMING_SNAKE_CASE  :							Tuple=[1, 3]      ,       ):




					"""simple docstring"""







					pass




class 				__lowerCAmelCase(							a__      ):
			__snake_case  :		Dict		=      'config.json'






			@register_to_config
			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[Any]=2      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=5      ,       SCREAMING_SNAKE_CASE  :							int=(2, 5)      ,       SCREAMING_SNAKE_CASE  :							str="for diffusion"      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=[1, 3]      ,       ):




					"""simple docstring"""







					pass




class 				__lowerCAmelCase(							a__      ):
			__snake_case  :		Dict		=      'config.json'






			@register_to_config
			def __init__(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=2      ,       SCREAMING_SNAKE_CASE  :							str=5      ,       SCREAMING_SNAKE_CASE  :							List[Any]=(2, 5)      ,       SCREAMING_SNAKE_CASE  :							int="for diffusion"      ,       SCREAMING_SNAKE_CASE  :							Any=[1, 3]      ,       SCREAMING_SNAKE_CASE  :							Dict=[1, 3]      ,       ):




					"""simple docstring"""







					pass




class 				__lowerCAmelCase(							a__      ):
			__snake_case  :		Union[str, Any]		=      'config.json'






			@register_to_config
			def __init__(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[Any]=2      ,       SCREAMING_SNAKE_CASE  :							List[Any]=5      ,       SCREAMING_SNAKE_CASE  :							Any=(2, 5)      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]="for diffusion"      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=[1, 5]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=[5, 4]      ,       ):




					"""simple docstring"""







					pass




class 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					with self.assertRaises(UpperCamelCase_	):
							ConfigMixin.load_config('dummy_path'	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      SampleObject()
					SCREAMING_SNAKE_CASE_						:Tuple										=      obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# init ignore private arguments
					SCREAMING_SNAKE_CASE_						:Any										=      SampleObject(_name_or_path='lalala'	)
					SCREAMING_SNAKE_CASE_						:str										=      obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# can override default
					SCREAMING_SNAKE_CASE_						:Tuple										=      SampleObject(c=6	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == 6
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# can use positional arguments.
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      SampleObject(1      ,       c=6	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      obj.config
					assert config["a"] == 1
					assert config["b"] == 5
					assert config["c"] == 6
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      SampleObject()
					SCREAMING_SNAKE_CASE_						:Dict										=      obj.config

					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					with tempfile.TemporaryDirectory() as tmpdirname:
							obj.save_config(UpperCamelCase_	)
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      SampleObject.from_config(SampleObject.load_config(UpperCamelCase_	)	)
							SCREAMING_SNAKE_CASE_						:List[Any]										=      new_obj.config

					# unfreeze configs
					SCREAMING_SNAKE_CASE_						:List[Any]										=      dict(UpperCamelCase_	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      dict(UpperCamelCase_	)

					assert config.pop('c'	) == (2, 5)  # instantiated as tuple
					assert new_config.pop('c'	) == [2, 5]  # saved & loaded as list because of json
					config.pop('_use_default_values'	)
					assert config == new_config






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      logging.get_logger('diffusers.configuration_utils'	)
					# 30 for warning
					logger.setLevel(30	)

					with CaptureLogger(UpperCamelCase_	) as cap_logger:
							SCREAMING_SNAKE_CASE_						:str										=      DDIMScheduler.from_pretrained(
							    'hf-internal-testing/tiny-stable-diffusion-torch'      ,       subfolder='scheduler'	)

					assert ddim.__class__ == DDIMScheduler
					# no warning should be thrown
					assert cap_logger.out == ""






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      logging.get_logger('diffusers.configuration_utils'	)
					# 30 for warning
					logger.setLevel(30	)

					with CaptureLogger(UpperCamelCase_	) as cap_logger:
							SCREAMING_SNAKE_CASE_						:Dict										=      EulerDiscreteScheduler.from_pretrained(
							    'hf-internal-testing/tiny-stable-diffusion-torch'      ,       subfolder='scheduler'	)

					assert euler.__class__ == EulerDiscreteScheduler
					# no warning should be thrown
					assert cap_logger.out == ""






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      logging.get_logger('diffusers.configuration_utils'	)
					# 30 for warning
					logger.setLevel(30	)

					with CaptureLogger(UpperCamelCase_	) as cap_logger:
							SCREAMING_SNAKE_CASE_						:List[str]										=      EulerAncestralDiscreteScheduler.from_pretrained(
							    'hf-internal-testing/tiny-stable-diffusion-torch'      ,       subfolder='scheduler'	)

					assert euler.__class__ == EulerAncestralDiscreteScheduler
					# no warning should be thrown
					assert cap_logger.out == ""






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      logging.get_logger('diffusers.configuration_utils'	)
					# 30 for warning
					logger.setLevel(30	)

					with CaptureLogger(UpperCamelCase_	) as cap_logger:
							SCREAMING_SNAKE_CASE_						:int										=      PNDMScheduler.from_pretrained(
							    'hf-internal-testing/tiny-stable-diffusion-torch'      ,       subfolder='scheduler'	)

					assert pndm.__class__ == PNDMScheduler
					# no warning should be thrown
					assert cap_logger.out == ""






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      logging.get_logger('diffusers.configuration_utils'	)
					# 30 for warning
					logger.setLevel(30	)

					with CaptureLogger(UpperCamelCase_	) as cap_logger:
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      DDPMScheduler.from_pretrained(
							    'hf-internal-testing/tiny-stable-diffusion-torch'      ,       subfolder='scheduler'      ,       prediction_type='sample'      ,       beta_end=8      ,       )

					with CaptureLogger(UpperCamelCase_	) as cap_logger_a:
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      DDPMScheduler.from_pretrained('google/ddpm-celebahq-256'      ,       beta_start=88	)

					assert ddpm.__class__ == DDPMScheduler
					assert ddpm.config.prediction_type == "sample"
					assert ddpm.config.beta_end == 8
					assert ddpm_a.config.beta_start == 88

					# no warning should be thrown
					assert cap_logger.out == ""
					assert cap_logger_a.out == ""






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      logging.get_logger('diffusers.configuration_utils'	)
					# 30 for warning
					logger.setLevel(30	)

					with CaptureLogger(UpperCamelCase_	) as cap_logger:
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      DPMSolverMultistepScheduler.from_pretrained(
							    'hf-internal-testing/tiny-stable-diffusion-torch'      ,       subfolder='scheduler'	)

					assert dpm.__class__ == DPMSolverMultistepScheduler
					# no warning should be thrown
					assert cap_logger.out == ""






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      SampleObject()

					SCREAMING_SNAKE_CASE_						:List[Any]										=      {k: v for k, v in config.config.items() if not k.startswith('_'	)}

					# make sure that default config has all keys in `_use_default_values`
					assert set(config_dict.keys()	) == set(config.config._use_default_values	)

					with tempfile.TemporaryDirectory() as tmpdirname:
							config.save_config(UpperCamelCase_	)

							# now loading it with SampleObject2 should put f into `_use_default_values`
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      SampleObjecta.from_config(UpperCamelCase_	)

							assert "f" in config._use_default_values
							assert config.f == [1, 3]

					# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
					# **BECAUSE** it is part of `config._use_default_values`
					SCREAMING_SNAKE_CASE_						:int										=      SampleObjecta.from_config(config.config	)
					assert new_config.f == [5, 4]

					config.config._use_default_values.pop()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      SampleObjecta.from_config(config.config	)
					assert new_config_a.f == [1, 3]

					# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
					assert new_config_a.e == [1, 3]




"""simple docstring"""





import math as m


def   _snake_case    (    _snake_case					:      List[Any]			, _snake_case					:      Tuple			, _snake_case					:      Tuple=0.001      ):
      return (f(a + h      ) - f(a - h      )) / (2 * h)


def   _snake_case    (    _snake_case					:      int			, _snake_case					:      List[Any]=0			, _snake_case					:      Any=100			, _snake_case					:      Any=0.0001			, _snake_case					:      Optional[Any]=1E-6			, _snake_case					:      Any=False      ):
      lowerCAmelCase       :						Optional[Any]							= xa  # set the initial guess
      lowerCAmelCase       :						str							= [a]
      lowerCAmelCase       :						List[str]							= abs(f(_snake_case      )      )
      lowerCAmelCase       :						Dict							= lambda _snake_case      : calc_derivative(_snake_case			, _snake_case			, h=_snake_case      )  # noqa: E731  Derivative of f(x)
      for _ in range(_snake_case      ):
            if fa(_snake_case      ) == 0:
                  raise ValueError('''No converging solution found'''      )
            lowerCAmelCase       :						Tuple							= a - f(_snake_case      ) / fa(_snake_case      )  # Calculate the next estimate
            if logsteps:
                  steps.append(_snake_case      )
            if error < maxerror:
                  break
      else:
            raise ValueError('''Iteration limit reached, no converging solution found'''      )
      if logsteps:
            # If logstep is true, then log intermediate steps
            return a, error, steps
      return a, error


if __name__ == "__main__":
 from matplotlib import pyplot as plt

 snake_case__		:  Any     					=					lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
 snake_case__					,  snake_case__					,  snake_case__		:  List[str]     					=					newton_raphson(
     f, xa=10, maxiter=1_000, step=1e-6, logsteps=True
 )
 plt.plot([abs(f(x)) for x in steps])
 plt.xlabel('''step''')
 plt.ylabel('''error''')
 plt.show()
 print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")


