





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     (IPNDMScheduler,)
       lowerCAmelCase__      		=     (("""num_inference_steps""", 50),)




       def 							UpperCAmelCase__ (  self :    Dict							,				**A :    Any		):
         __snake_case:						Any   =						{"""num_train_timesteps""": 1_000}
         config.update(**A		)
         return config




       def 							UpperCAmelCase__ (  self :    Any							,				A :    Union[str, Any]=0							,				**A :    Dict		):
         __snake_case:						Any   =						dict(self.forward_default_kwargs		)
         __snake_case:						Dict   =						kwargs.pop("""num_inference_steps"""							,				A		)
         __snake_case:						Any   =						self.dummy_sample
         __snake_case:						Optional[int]   =						0.1 * sample
         __snake_case:						Tuple   =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           __snake_case:						List[str]   =						self.get_scheduler_config(**A		)
           __snake_case:						List[Any]   =						scheduler_class(**A		)
           scheduler.set_timesteps(A		)
           # copy over dummy past residuals
           __snake_case:						List[str]   =						dummy_past_residuals[:]

           if time_step is None:
             __snake_case:						Union[str, Any]   =						scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

           __snake_case:						int   =						scheduler.step(A							,				A							,				A							,				**A		).prev_sample
           __snake_case:						Optional[int]   =						new_scheduler.step(A							,				A							,				A							,				**A		).prev_sample

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

           __snake_case:						Union[str, Any]   =						scheduler.step(A							,				A							,				A							,				**A		).prev_sample
           __snake_case:						Optional[int]   =						new_scheduler.step(A							,				A							,				A							,				**A		).prev_sample

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




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         pass




       def 							UpperCAmelCase__ (  self :    str							,				A :    Tuple=0							,				**A :    Optional[int]		):
         __snake_case:						int   =						dict(self.forward_default_kwargs		)
         __snake_case:						Dict   =						kwargs.pop("""num_inference_steps"""							,				A		)
         __snake_case:						Any   =						self.dummy_sample
         __snake_case:						Optional[int]   =						0.1 * sample
         __snake_case:						Dict   =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           __snake_case:						str   =						self.get_scheduler_config()
           __snake_case:						Optional[Any]   =						scheduler_class(**A		)
           scheduler.set_timesteps(A		)

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

           if time_step is None:
             __snake_case:						Optional[int]   =						scheduler.timesteps[len(scheduler.timesteps		) // 2]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(A		)
             __snake_case:						Dict   =						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)
             __snake_case:						str   =						dummy_past_residuals[:]

           __snake_case:						Any   =						scheduler.step(A							,				A							,				A							,				**A		).prev_sample
           __snake_case:						int   =						new_scheduler.step(A							,				A							,				A							,				**A		).prev_sample

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

           __snake_case:						int   =						scheduler.step(A							,				A							,				A							,				**A		).prev_sample
           __snake_case:						List[str]   =						new_scheduler.step(A							,				A							,				A							,				**A		).prev_sample

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




       def 							UpperCAmelCase__ (  self :    List[Any]							,				**A :    Optional[Any]		):
         __snake_case:						Any   =						self.scheduler_classes[0]
         __snake_case:						str   =						self.get_scheduler_config(**A		)
         __snake_case:						Union[str, Any]   =						scheduler_class(**A		)

         __snake_case:						List[str]   =						10
         __snake_case:						int   =						self.dummy_model()
         __snake_case:						str   =						self.dummy_sample_deter
         scheduler.set_timesteps(A		)

         for i, t in enumerate(scheduler.timesteps		):
           __snake_case:						Any   =						model(A							,				A		)
           __snake_case:						str   =						scheduler.step(A							,				A							,				A		).prev_sample

         for i, t in enumerate(scheduler.timesteps		):
           __snake_case:						Optional[Any]   =						model(A							,				A		)
           __snake_case:						Tuple   =						scheduler.step(A							,				A							,				A		).prev_sample

         return sample




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						dict(self.forward_default_kwargs		)

         __snake_case:						Optional[int]   =						kwargs.pop("""num_inference_steps"""							,				A		)

         for scheduler_class in self.scheduler_classes:
           __snake_case:						int   =						self.get_scheduler_config()
           __snake_case:						int   =						scheduler_class(**A		)

           __snake_case:						Tuple   =						self.dummy_sample
           __snake_case:						List[str]   =						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"""		):
             __snake_case:						Optional[int]   =						num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           __snake_case:						Dict   =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
           __snake_case:						Optional[Any]   =						dummy_past_residuals[:]

           __snake_case:						Union[str, Any]   =						scheduler.timesteps[5]
           __snake_case:						Union[str, Any]   =						scheduler.timesteps[6]

           __snake_case:						List[str]   =						scheduler.step(A							,				A							,				A							,				**A		).prev_sample
           __snake_case:						Dict   =						scheduler.step(A							,				A							,				A							,				**A		).prev_sample

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

           __snake_case:						str   =						scheduler.step(A							,				A							,				A							,				**A		).prev_sample
           __snake_case:						Dict   =						scheduler.step(A							,				A							,				A							,				**A		).prev_sample

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




       def 							UpperCAmelCase__ (  self :    str		):
         for timesteps in [100, 1_000]:
           self.check_over_configs(num_train_timesteps=A							,				time_step=A		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         for t, num_inference_steps in zip([1, 5, 10]							,				[10, 50, 100]		):
           self.check_over_forward(num_inference_steps=A							,				time_step=A		)






       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[int]   =						self.full_loop()
         __snake_case:						Tuple   =						torch.mean(torch.abs(A		)		)

         assert abs(result_mean.item() - 2_540_529		) < 10













import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class     __snake_case       (							unittest.TestCase   ,			__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						Optional[int]   =						load_tool("""text-classification"""		)
         self.tool.setup()
         __snake_case:						Dict   =						load_tool("""text-classification"""							,				remote=A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						List[str]   =						self.tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						List[str]   =						self.remote_tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						self.tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						int   =						self.remote_tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






