from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  List[str]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Any							):



    """simple docstring"""



    super().__init__()

    # make sure scheduler can always be converted to DDIM
    _UpperCamelCase:     int							= DDIMScheduler.from_config(scheduler.config							)

    self.register_modules(unet=_lowercase		,  scheduler=_lowercase							)



   @torch.no_grad()
   def __call__(	self					:  List[Any]		,  _lowercase					:  int = 1		,  _lowercase					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None		,  _lowercase					:  float = 0.0		,  _lowercase					:  int = 50		,  _lowercase					:  Optional[bool] = None		,  _lowercase					:  Optional[str] = "pil"		,  _lowercase					:  bool = True		,  ):



    """simple docstring"""



    if isinstance(self.unet.config.sample_size		,  _lowercase							):
     _UpperCamelCase:     str							= (
         batch_size,
         self.unet.config.in_channels,
         self.unet.config.sample_size,
         self.unet.config.sample_size,
     )
    else:
     _UpperCamelCase:     Tuple							= (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

    if isinstance(_lowercase		,  _lowercase							) and len(_lowercase							) != batch_size:
     raise ValueError(
         f"""You have passed a list of generators of length {len(_lowercase							)}, but requested an effective batch"""
         f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

    _UpperCamelCase:     Any							= randn_tensor(_lowercase		,  generator=_lowercase		,  device=self.device		,  dtype=self.unet.dtype							)

    # set step values
    self.scheduler.set_timesteps(_lowercase							)

    for t in self.progress_bar(self.scheduler.timesteps							):
     # 1. predict noise model_output
     _UpperCamelCase:     Tuple							= self.unet(_lowercase		,  _lowercase							).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     _UpperCamelCase:     Union[str, Any]							= self.scheduler.step(
         _lowercase		,  _lowercase		,  _lowercase		,  eta=_lowercase		,  use_clipped_model_output=_lowercase		,  generator=_lowercase							).prev_sample

    _UpperCamelCase:     Dict							= (image / 2 + 0.5).clamp(0		,  1							)
    _UpperCamelCase:     Union[str, Any]							= image.cpu().permute(0		,  2		,  3		,  1							).numpy()
    if output_type == "pil":
     _UpperCamelCase:     Tuple							= self.numpy_to_pil(_lowercase							)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=_lowercase							)
import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase_			    =							'''1'''
UpperCAmelCase_			    =							'''0'''
UpperCAmelCase_			    =							'''1'''

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

UpperCAmelCase_			    =							1_2_8
UpperCAmelCase_			    =							1
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							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_			    =							time.time()
UpperCAmelCase_			    =							2_0_0_0
UpperCAmelCase_			    =							{}
for iter in range(max_iters):
  UpperCAmelCase_			    =							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) * 1_0_0_0 / max_iters))