"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    str	      =	(PNDMScheduler,)
     _UpperCamelCase							:    Optional[Any]	      =	(("num_inference_steps", 50),)






     def 					__A						(	self       ,  **a__						):
       _lowerCAmelCase		: Optional[int]						    =  {
           """num_train_timesteps""": 1000,
           """beta_start""": 0.0_0_0_1,
           """beta_end""": 0.0_2,
           """beta_schedule""": """linear""",
       }

       config.update(**a__						)
       return config






     def 					__A						(	self       ,  a__=0       ,  **a__						):
       _lowerCAmelCase		: str						    =  dict(self.forward_default_kwargs						)
       _lowerCAmelCase		: str						    =  kwargs.pop("""num_inference_steps"""       ,  a__						)
       _lowerCAmelCase		: Tuple						    =  self.dummy_sample
       _lowerCAmelCase		: Dict						    =  0.1 * sample
       _lowerCAmelCase		: Optional[Any]						    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(a__						)
           _lowerCAmelCase		: Any						    =  scheduler_class.from_pretrained(a__						)
           new_scheduler.set_timesteps(a__						)
           # copy over dummy past residuals
           _lowerCAmelCase		: str						    =  dummy_past_residuals[:]

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

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

         _lowerCAmelCase		: Tuple						    =  scheduler.step_plms(a__       ,  a__       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: Optional[int]						    =  new_scheduler.step_plms(a__       ,  a__       ,  a__       ,  **a__						).prev_sample

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






     def 					__A						(	self						):
       pass






     def 					__A						(	self       ,  a__=0       ,  **a__						):
       _lowerCAmelCase		: List[str]						    =  dict(self.forward_default_kwargs						)
       _lowerCAmelCase		: Any						    =  kwargs.pop("""num_inference_steps"""       ,  a__						)
       _lowerCAmelCase		: Any						    =  self.dummy_sample
       _lowerCAmelCase		: str						    =  0.1 * sample
       _lowerCAmelCase		: List[Any]						    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase		: Optional[int]						    =  self.get_scheduler_config()
         _lowerCAmelCase		: str						    =  scheduler_class(**a__						)
         scheduler.set_timesteps(a__						)

         # copy over dummy past residuals (must be after setting timesteps)
         _lowerCAmelCase		: Tuple						    =  dummy_past_residuals[:]

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

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

         _lowerCAmelCase		: int						    =  scheduler.step_prk(a__       ,  a__       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: int						    =  new_scheduler.step_prk(a__       ,  a__       ,  a__       ,  **a__						).prev_sample

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

         _lowerCAmelCase		: List[str]						    =  scheduler.step_plms(a__       ,  a__       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: Optional[Any]						    =  new_scheduler.step_plms(a__       ,  a__       ,  a__       ,  **a__						).prev_sample

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






     def 					__A						(	self       ,  **a__						):
       _lowerCAmelCase		: List[Any]						    =  self.scheduler_classes[0]
       _lowerCAmelCase		: Dict						    =  self.get_scheduler_config(**a__						)
       _lowerCAmelCase		: Tuple						    =  scheduler_class(**a__						)

       _lowerCAmelCase		: List[Any]						    =  10
       _lowerCAmelCase		: Optional[int]						    =  self.dummy_model()
       _lowerCAmelCase		: int						    =  self.dummy_sample_deter
       scheduler.set_timesteps(a__						)

       for i, t in enumerate(scheduler.prk_timesteps						):
         _lowerCAmelCase		: int						    =  model(a__       ,  a__						)
         _lowerCAmelCase		: List[Any]						    =  scheduler.step_prk(a__       ,  a__       ,  a__						).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps						):
         _lowerCAmelCase		: Dict						    =  model(a__       ,  a__						)
         _lowerCAmelCase		: Tuple						    =  scheduler.step_plms(a__       ,  a__       ,  a__						).prev_sample

       return sample






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  dict(self.forward_default_kwargs						)

       _lowerCAmelCase		: Optional[Any]						    =  kwargs.pop("""num_inference_steps"""       ,  a__						)

       for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase		: Any						    =  self.get_scheduler_config()
         _lowerCAmelCase		: Dict						    =  scheduler_class(**a__						)

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

         if num_inference_steps is not None and hasattr(a__       ,  """set_timesteps"""						):
           scheduler.set_timesteps(a__						)
         elif num_inference_steps is not None and not hasattr(a__       ,  """set_timesteps"""						):
           _lowerCAmelCase		: Optional[int]						    =  num_inference_steps

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

         _lowerCAmelCase		: Union[str, Any]						    =  scheduler.step_prk(a__       ,  0       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: Any						    =  scheduler.step_prk(a__       ,  1       ,  a__       ,  **a__						).prev_sample

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

         _lowerCAmelCase		: Tuple						    =  scheduler.step_plms(a__       ,  0       ,  a__       ,  **a__						).prev_sample
         _lowerCAmelCase		: List[Any]						    =  scheduler.step_plms(a__       ,  1       ,  a__       ,  **a__						).prev_sample

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






     def 					__A						(	self						):
       for timesteps in [100, 1000]:
         self.check_over_configs(num_train_timesteps=a__						)






     def 					__A						(	self						):
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=a__						)

       _lowerCAmelCase		: int						    =  self.scheduler_classes[0]
       _lowerCAmelCase		: Optional[int]						    =  self.get_scheduler_config(steps_offset=1						)
       _lowerCAmelCase		: Optional[Any]						    =  scheduler_class(**a__						)
       scheduler.set_timesteps(10						)
       assert torch.equal(
           scheduler.timesteps       ,  torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]						)       ,  )






     def 					__A						(	self						):
       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=a__       ,  beta_end=a__						)






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






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






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






     def 					__A						(	self						):
       for t, num_inference_steps in zip([1, 5, 10]       ,  [10, 50, 100]						):
         self.check_over_forward(num_inference_steps=a__						)






     def 					__A						(	self						):
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       _lowerCAmelCase		: Dict						    =  27

       for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase		: Optional[int]						    =  self.dummy_sample
         _lowerCAmelCase		: Union[str, Any]						    =  0.1 * sample

         _lowerCAmelCase		: Union[str, Any]						    =  self.get_scheduler_config()
         _lowerCAmelCase		: int						    =  scheduler_class(**a__						)

         scheduler.set_timesteps(a__						)

         # 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]						):
           _lowerCAmelCase		: Optional[Any]						    =  scheduler.step_prk(a__       ,  a__       ,  a__						).prev_sample






     def 					__A						(	self						):
       with self.assertRaises(a__						):
         _lowerCAmelCase		: List[Any]						    =  self.scheduler_classes[0]
         _lowerCAmelCase		: List[Any]						    =  self.get_scheduler_config()
         _lowerCAmelCase		: Optional[Any]						    =  scheduler_class(**a__						)

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






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  self.full_loop()
       _lowerCAmelCase		: Union[str, Any]						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: int						    =  torch.mean(torch.abs(a__						)						)

       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 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  self.full_loop(prediction_type="""v_prediction"""						)
       _lowerCAmelCase		: Optional[int]						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: int						    =  torch.mean(torch.abs(a__						)						)

       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 					__A						(	self						):
       # We specify different beta, so that the first alpha is 0.99
       _lowerCAmelCase		: str						    =  self.full_loop(set_alpha_to_one=a__       ,  beta_start=0.0_1						)
       _lowerCAmelCase		: Dict						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: Tuple						    =  torch.mean(torch.abs(a__						)						)

       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 					__A						(	self						):
       # We specify different beta, so that the first alpha is 0.99
       _lowerCAmelCase		: Dict						    =  self.full_loop(set_alpha_to_one=a__       ,  beta_start=0.0_1						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: Union[str, Any]						    =  torch.mean(torch.abs(a__						)						)

       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





"""simple docstring"""




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('>=', '4.25.0')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel




