






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 			__a  (    A__       ):
 _lowerCAmelCase       :       List[Any]        = (PNDMScheduler,)
 _lowerCAmelCase       :       Dict        = (('''num_inference_steps''', 5_0),)







 def 					__lowercase					(		self					:   List[str]							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  {
          "num_train_timesteps": 10_00,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**SCREAMING_SNAKE_CASE						)
      return config







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   str=0							,   **SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  dict(self.forward_default_kwargs						)
      UpperCamelCase__			:   int       				=  kwargs.pop("num_inference_steps"							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  self.dummy_sample
      UpperCamelCase__			:   Optional[int]       				=  0.1 * sample
      UpperCamelCase__			:   List[Any]       				=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__			:   Tuple       				=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Optional[Any]       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
           # copy over dummy past residuals
           UpperCamelCase__			:   Any       				=  dummy_past_residuals[:]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   Optional[int]       				=  scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE						)
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
                # copy over dummy past residuals
                UpperCamelCase__			:   str       				=  dummy_past_residuals[:]

           UpperCamelCase__			:   Any       				=  scheduler.step_prk(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__			:   Optional[int]       				=  new_scheduler.step_prk(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample

           assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

           UpperCamelCase__			:   Optional[Any]       				=  scheduler.step_plms(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__			:   Union[str, Any]       				=  new_scheduler.step_plms(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample

           assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Dict=0							,   **SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  dict(self.forward_default_kwargs						)
      UpperCamelCase__			:   Optional[int]       				=  kwargs.pop("num_inference_steps"							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  self.dummy_sample
      UpperCamelCase__			:   List[Any]       				=  0.1 * sample
      UpperCamelCase__			:   List[str]       				=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__			:   List[str]       				=  self.get_scheduler_config()
           UpperCamelCase__			:   Tuple       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

           # copy over dummy past residuals (must be after setting timesteps)
           UpperCamelCase__			:   List[Any]       				=  dummy_past_residuals[:]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   Optional[Any]       				=  scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE						)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

                # copy over dummy past residual (must be after setting timesteps)
                UpperCamelCase__			:   Optional[int]       				=  dummy_past_residuals[:]

           UpperCamelCase__			:   int       				=  scheduler.step_prk(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__			:   Dict       				=  new_scheduler.step_prk(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample

           assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

           UpperCamelCase__			:   int       				=  scheduler.step_plms(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__			:   str       				=  new_scheduler.step_plms(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample

           assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"







 def 					__lowercase					(		self					:   Union[str, Any]							,   **SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.scheduler_classes[0]
      UpperCamelCase__			:   Tuple       				=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  10
      UpperCamelCase__			:   Optional[Any]       				=  self.dummy_model()
      UpperCamelCase__			:   List[str]       				=  self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

      for i, t in enumerate(scheduler.prk_timesteps						):
           UpperCamelCase__			:   Union[str, Any]       				=  model(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Union[str, Any]       				=  scheduler.step_prk(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						).prev_sample

      for i, t in enumerate(scheduler.plms_timesteps						):
           UpperCamelCase__			:   Tuple       				=  model(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Optional[int]       				=  scheduler.step_plms(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						).prev_sample

      return sample







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  dict(self.forward_default_kwargs						)

      UpperCamelCase__			:   Optional[int]       				=  kwargs.pop("num_inference_steps"							,   SCREAMING_SNAKE_CASE						)

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__			:   Any       				=  self.get_scheduler_config()
           UpperCamelCase__			:   List[Any]       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   List[str]       				=  self.dummy_sample
           UpperCamelCase__			:   Optional[int]       				=  0.1 * sample

           if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE							,   "set_timesteps"						):
                scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
           elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE							,   "set_timesteps"						):
                UpperCamelCase__			:   List[Any]       				=  num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase__			:   List[Any]       				=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
           UpperCamelCase__			:   List[str]       				=  dummy_past_residuals[:]

           UpperCamelCase__			:   Tuple       				=  scheduler.step_prk(SCREAMING_SNAKE_CASE							,   0							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__			:   Optional[int]       				=  scheduler.step_prk(SCREAMING_SNAKE_CASE							,   1							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample

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

           UpperCamelCase__			:   Any       				=  scheduler.step_plms(SCREAMING_SNAKE_CASE							,   0							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__			:   str       				=  scheduler.step_plms(SCREAMING_SNAKE_CASE							,   1							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample

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







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      for timesteps in [1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  self.scheduler_classes[0]
      UpperCamelCase__			:   List[str]       				=  self.get_scheduler_config(steps_offset=1						)
      UpperCamelCase__			:   Dict       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)
      scheduler.set_timesteps(10						)
      assert torch.equal(
          scheduler.timesteps							,   torch.LongTensor(
              [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]						)							,   )







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]							,   [0.0_0_2, 0.0_2]						):
           self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE							,   beta_end=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      for t in [1, 5, 10]:
           self.check_over_forward(time_step=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      for t, num_inference_steps in zip([1, 5, 10]							,   [10, 50, 1_00]						):
           self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  27

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__			:   Union[str, Any]       				=  self.dummy_sample
           UpperCamelCase__			:   List[str]       				=  0.1 * sample

           UpperCamelCase__			:   Union[str, Any]       				=  self.get_scheduler_config()
           UpperCamelCase__			:   Optional[int]       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

           scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

           # before power of 3 fix, would error on first step, so we only need to do two
           for i, t in enumerate(scheduler.prk_timesteps[:2]						):
                UpperCamelCase__			:   List[Any]       				=  scheduler.step_prk(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						).prev_sample







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


      '''simple docstring'''






      with self.assertRaises(SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   List[str]       				=  self.scheduler_classes[0]
           UpperCamelCase__			:   List[Any]       				=  self.get_scheduler_config()
           UpperCamelCase__			:   Dict       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

           scheduler.step_plms(self.dummy_sample							,   1							,   self.dummy_sample						).prev_sample







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  self.full_loop()
      UpperCamelCase__			:   Any       				=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Optional[Any]       				=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_sum.item() - 1_9_8.1_3_1_8						) < 1e-2
      assert abs(result_mean.item() - 0.2_5_8_0						) < 1e-3







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.full_loop(prediction_type="v_prediction"						)
      UpperCamelCase__			:   List[str]       				=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   List[str]       				=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_sum.item() - 6_7.3_9_8_6						) < 1e-2
      assert abs(result_mean.item() - 0.0_8_7_8						) < 1e-3







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE							,   beta_start=0.0_1						)
      UpperCamelCase__			:   Tuple       				=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Tuple       				=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_sum.item() - 2_3_0.0_3_9_9						) < 1e-2
      assert abs(result_mean.item() - 0.2_9_9_5						) < 1e-3







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE							,   beta_start=0.0_1						)
      UpperCamelCase__			:   Optional[Any]       				=  torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Union[str, Any]       				=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_sum.item() - 1_8_6.9_4_8_2						) < 1e-2
      assert abs(result_mean.item() - 0.2_4_3_4						) < 1e-3







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     while b:
          UpperCamelCase__				,     UpperCamelCase__			:   int       				=  b, a % b
     return a







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase					,    a % b			)







def 		SCREAMING_SNAKE_CASE							(					) ->	str:
     print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3					,    5			)}'			)
     print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5					,    3			)}'			)
     print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1					,    3			)}'			)
     print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3					,    6			)}'			)
     print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6					,    3			)}'			)

     print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,    5			)}'			)
     print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,    3			)}'			)
     print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,    3			)}'			)
     print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,    6			)}'			)
     print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,    3			)}'			)


if __name__ == "__main__":
 main()