





"""simple docstring"""


a__       :  Dict       		=					1_0_0_0





def 				a__      (	_UpperCAmelCase						)     ->	float:

 '''simple docstring'''






 return x * x





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase = 4						)     ->	float:

 '''simple docstring'''






 assert callable(
     snake_case__						), f"the function(object) passed should be callable your input : {function}"
 assert isinstance(snake_case__						,  (float, int)						), f"a should be float or integer your input : {a}"
 assert isinstance(function(snake_case__						)						,  (float, int)						), (
     "the function should return integer or float return type of your function, "
     f"{type(snake_case__						)}"
 )
 assert isinstance(snake_case__						,  (float, int)						), f"b should be float or integer your input : {b}"
 assert (
     isinstance(snake_case__						,  snake_case__						) and precision > 0
 ), f"precision should be positive integer your input : {precision}"

 # just applying the formula of simpson for approximate integration written in
 # mentioned article in first comment of this file and above this function

 snake_case__     				=					(b - a) / N_STEPS
 snake_case__     				=					function(snake_case__						) + function(snake_case__						)

 for i in range(1						,  snake_case__						):
  snake_case__     				=					a + h * i
  result += function(snake_case__						) * (4 if i % 2 else 2)

 result *= h / 3
 return round(snake_case__						,  snake_case__						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.27.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import (
		    AudioLDMPipeline,
		)
else:
		from .pipeline_audioldm import AudioLDMPipeline

