


"""simple docstring"""
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					(CMStochasticIterativeScheduler,)
   __UpperCAmelCase							       =					10





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Dict:
      _lowerCamelCase	:					Tuple   =		{
          """num_train_timesteps""": 201,
          """sigma_min""": 0.0_02,
          """sigma_max""": 80.0,
      }

      config.update(**SCREAMING_SNAKE_CASE)
      return config





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					List[str]   =		10

      _lowerCamelCase	:					Tuple   =		self.get_scheduler_config()
      _lowerCamelCase	:					int   =		self.scheduler_classes[0](**SCREAMING_SNAKE_CASE)

      scheduler.set_timesteps(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		scheduler.timesteps[0]
      _lowerCamelCase	:					Union[str, Any]   =		scheduler.timesteps[1]

      _lowerCamelCase	:					int   =		self.dummy_sample
      _lowerCamelCase	:					Optional[Any]   =		0.1 * sample

      _lowerCamelCase	:					str   =		scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE).prev_sample
      _lowerCamelCase	:					List[str]   =		scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE).prev_sample

      self.assertEqual(output_a.shape      ,   sample.shape)
      self.assertEqual(output_a.shape      ,   output_a.shape)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Optional[int]   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Dict   =		self.get_scheduler_config()
      _lowerCamelCase	:					Optional[int]   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		1
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		scheduler.timesteps

      _lowerCamelCase	:					Optional[int]   =		torch.manual_seed(0)

      _lowerCamelCase	:					Optional[Any]   =		self.dummy_model()
      _lowerCamelCase	:					int   =		self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(SCREAMING_SNAKE_CASE):
         # 1. scale model input
         _lowerCamelCase	:					Tuple   =		scheduler.scale_model_input(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         # 2. predict noise residual
         _lowerCamelCase	:					Optional[int]   =		model(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         # 3. predict previous sample x_t-1
         _lowerCamelCase	:					Optional[Any]   =		scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE).prev_sample

         _lowerCamelCase	:					List[Any]   =		pred_prev_sample

      _lowerCamelCase	:					List[str]   =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
      _lowerCamelCase	:					Optional[int]   =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

      assert abs(result_sum.item() - 1_92.76_14) < 1e-2
      assert abs(result_mean.item() - 0.25_10) < 1e-3





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					Any   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Optional[Any]   =		self.get_scheduler_config()
      _lowerCamelCase	:					str   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		[106, 0]
      scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		scheduler.timesteps

      _lowerCamelCase	:					List[str]   =		torch.manual_seed(0)

      _lowerCamelCase	:					List[Any]   =		self.dummy_model()
      _lowerCamelCase	:					List[Any]   =		self.dummy_sample_deter * scheduler.init_noise_sigma

      for t in timesteps:
         # 1. scale model input
         _lowerCamelCase	:					Optional[Any]   =		scheduler.scale_model_input(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         # 2. predict noise residual
         _lowerCamelCase	:					Optional[Any]   =		model(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         # 3. predict previous sample x_t-1
         _lowerCamelCase	:					Dict   =		scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE).prev_sample

         _lowerCamelCase	:					Union[str, Any]   =		pred_prev_sample

      _lowerCamelCase	:					str   =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
      _lowerCamelCase	:					Optional[int]   =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

      assert abs(result_sum.item() - 3_47.63_57) < 1e-2
      assert abs(result_mean.item() - 0.45_27) < 1e-3





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					List[str]   =		self.scheduler_classes[0]
      _lowerCamelCase	:					List[str]   =		self.get_scheduler_config()
      _lowerCamelCase	:					Optional[Any]   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		[39, 30, 12, 15, 0]

      with self.assertRaises(SCREAMING_SNAKE_CASE      ,   msg="""`timesteps` must be in descending order."""):
         scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					Any   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Optional[int]   =		self.get_scheduler_config()
      _lowerCamelCase	:					List[str]   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		[39, 30, 12, 1, 0]
      _lowerCamelCase	:					Any   =		len(SCREAMING_SNAKE_CASE)

      with self.assertRaises(SCREAMING_SNAKE_CASE      ,   msg="""Can only pass one of `num_inference_steps` or `timesteps`."""):
         scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE      ,   timesteps=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					Union[str, Any]   =		self.scheduler_classes[0]
      _lowerCamelCase	:					Optional[int]   =		self.get_scheduler_config()
      _lowerCamelCase	:					Tuple   =		scheduler_class(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          SCREAMING_SNAKE_CASE      ,   msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""      ,   ):
         scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE)





"""simple docstring"""
from collections import defaultdict

def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		first_str.lower().strip()
   _lowerCamelCase	:					int   =		second_str.lower().strip()

   # Remove whitespace
   _lowerCamelCase	:					Any   =		first_str.replace(""" """						, """"""   )
   _lowerCamelCase	:					List[str]   =		second_str.replace(""" """						, """"""   )

   # Strings of different lengths are not anagrams
   if len(__snake_case   ) != len(__snake_case   ):
      return False

   # Default values for count should be 0
   _lowerCamelCase	:					defaultdict[str, int]   =		defaultdict(__snake_case   )

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(__snake_case   )   ):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()   )


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase				=						input("""Enter the first string """).strip()
 UpperCAmelCase				=						input("""Enter the second string """).strip()

 UpperCAmelCase				=						check_anagrams(input_a, input_b)
 print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')

