


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     (IPNDMScheduler,)
						lowerCamelCase_         =     (('num_inference_steps', 50),)




						def  _snake_case					(	self						:   Tuple   ,     **__A						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									a__ :List[str]						  =    {"num_train_timesteps": 1000}
									config.update(**__A						)
									return config




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   Any=0   ,     **__A						:   List[str]						)      ->Any:






									"""simple docstring"""






									a__ :Optional[int]						  =    dict(self.forward_default_kwargs						)
									a__ :Union[str, Any]						  =    kwargs.pop("num_inference_steps"   ,     __A						)
									a__ :Optional[int]						  =    self.dummy_sample
									a__ :List[Any]						  =    0.1 * sample
									a__ :Tuple						  =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

												if time_step is None:
															a__ :Any						  =    scheduler.timesteps[len(scheduler.timesteps						) // 2]

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

												a__ :str						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :Dict						  =    new_scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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

												a__ :Optional[int]						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :Dict						  =    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  _snake_case					(	self						:   List[Any]						)      ->Optional[Any]:






									"""simple docstring"""






									pass




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   Tuple=0   ,     **__A						:   Optional[int]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Optional[int]						  =    dict(self.forward_default_kwargs						)
									a__ :Tuple						  =    kwargs.pop("num_inference_steps"   ,     __A						)
									a__ :str						  =    self.dummy_sample
									a__ :int						  =    0.1 * sample
									a__ :int						  =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												a__ :int						  =    self.get_scheduler_config()
												a__ :Any						  =    scheduler_class(**__A						)
												scheduler.set_timesteps(__A						)

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

												if time_step is None:
															a__ :Tuple						  =    scheduler.timesteps[len(scheduler.timesteps						) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__A						)
															a__ :List[str]						  =    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)
															a__ :Optional[int]						  =    dummy_past_residuals[:]

												a__ :Dict						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :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"

												a__ :Any						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :Union[str, Any]						  =    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  _snake_case					(	self						:   List[Any]   ,     **__A						:   Optional[int]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    self.scheduler_classes[0]
									a__ :str						  =    self.get_scheduler_config(**__A						)
									a__ :int						  =    scheduler_class(**__A						)

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

									for i, t in enumerate(scheduler.timesteps						):
												a__ :List[Any]						  =    model(__A   ,     __A						)
												a__ :Any						  =    scheduler.step(__A   ,     __A   ,     __A						).prev_sample

									for i, t in enumerate(scheduler.timesteps						):
												a__ :Union[str, Any]						  =    model(__A   ,     __A						)
												a__ :Dict						  =    scheduler.step(__A   ,     __A   ,     __A						).prev_sample

									return sample




						def  _snake_case					(	self						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									a__ :List[Any]						  =    dict(self.forward_default_kwargs						)

									a__ :str						  =    kwargs.pop("num_inference_steps"   ,     __A						)

									for scheduler_class in self.scheduler_classes:
												a__ :List[str]						  =    self.get_scheduler_config()
												a__ :Optional[int]						  =    scheduler_class(**__A						)

												a__ :str						  =    self.dummy_sample
												a__ :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"						):
															a__ :Dict						  =    num_inference_steps

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

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

												a__ :str						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :int						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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

												a__ :Optional[int]						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :List[Any]						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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




						def  _snake_case					(	self						:   Optional[Any]						)      ->str:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Optional[int]						)      ->str:






									"""simple docstring"""






									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  _snake_case					(	self						:   Union[str, Any]						)      ->str:






									"""simple docstring"""






									a__ :Optional[Any]						  =    self.full_loop()
									a__ :Optional[Any]						  =    torch.mean(torch.abs(__A						)						)

									assert abs(result_mean.item() - 2540529						) < 10






import os
import time

import numpy as np
import onnxruntime as ort


snake_case__			 =						'''1'''
snake_case__			 =						'''0'''
snake_case__			 =						'''1'''

snake_case__			 =						ort.SessionOptions()
snake_case__			 =						ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case__			 =						['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case__			 =						ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case__			 =						ort.RunOptions()

snake_case__			 =						128
snake_case__			 =						1
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
snake_case__			 =						time.time()
snake_case__			 =						2000
snake_case__			 =						{}
for iter in range(max_iters):
		snake_case__			 =						sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))


