






import os
import time

import numpy as np
import onnxruntime as ort


__UpperCamelCase      :      List[str]     						=						"""1"""
__UpperCamelCase      :      Any     						=						"""0"""
__UpperCamelCase      :      Dict     						=						"""1"""

__UpperCamelCase      :      int     						=						ort.SessionOptions()
__UpperCamelCase      :      Optional[int]     						=						ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
__UpperCamelCase      :      Dict     						=						["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
__UpperCamelCase      :      str     						=						ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
__UpperCamelCase      :      str     						=						ort.RunOptions()

__UpperCamelCase      :      List[str]     						=						128
__UpperCamelCase      :      Any     						=						1
__UpperCamelCase      :      int     						=						np.ones((batch, sequence), dtype=np.intaa)
__UpperCamelCase      :      Any     						=						np.ones((batch, sequence), dtype=np.intaa)
__UpperCamelCase      :      Tuple     						=						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...""")
__UpperCamelCase      :      Dict     						=						time.time()
__UpperCamelCase      :      Optional[Any]     						=						2000
__UpperCamelCase      :      List[Any]     						=						{}
for iter in range(max_iters):
						__UpperCamelCase      :      Any     						=						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))









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       (UnCLIPScheduler,)







	def  _a   (  self      :							Optional[int]       ,				**_lowerCAmelCase      :							Any			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**_lowerCAmelCase			)
		return config







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		for variance in ["fixed_small_log", "learned_range"]:
			self.check_over_configs(variance_type=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Any:
		"""simple docstring"""







		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Tuple:
		"""simple docstring"""







		for prediction_type in ["epsilon", "sample"]:
			self.check_over_configs(prediction_type=_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		for time_step in [0, 500, 999]:
			for prev_timestep in [None, 5, 100, 250, 500, 750]:
				if prev_timestep is not None and prev_timestep >= time_step:
					continue

				self.check_over_forward(time_step=_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""fixed_small_log"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_549_625			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_994_987			)			) < 1e-5







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""learned_range"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  0.5

		assert scheduler._get_variance(1       ,				predicted_variance=_lowerCAmelCase			) - -10.1_712_790 < 1e-5
		assert scheduler._get_variance(487       ,				predicted_variance=_lowerCAmelCase			) - -5.7_998_052 < 1e-5
		assert scheduler._get_variance(999       ,				predicted_variance=_lowerCAmelCase			) - -0.0_010_011 < 1e-5







	def  _a   (  self      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 252.2_682_495			) < 1e-2
		assert abs(result_mean.item() - 0.3_284_743			) < 1e-3







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		scheduler.set_timesteps(25			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			if i + 1 == timesteps.shape[0]:
				__lowercase             =  None
			else:
				__lowercase             =  timesteps[i + 1]

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(
			    _lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 258.2_044_983			) < 1e-2
		assert abs(result_mean.item() - 0.3_362_038			) < 1e-3







	def  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> List[str]:
		"""simple docstring"""







		pass

