
"""simple docstring"""

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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      def __init__(				self ,							lowercase_ ,							lowercase_				)    ->       int:
          super().__init__()

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

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




      @torch.no_grad()
      def __call__(				self ,							lowercase_ = 1 ,							lowercase_ = None ,							lowercase_ = 0.0 ,							lowercase_ = 5_0 ,							lowercase_ = None ,							lowercase_ = "pil" ,							lowercase_ = True ,							)    ->       Union[ImagePipelineOutput, Tuple]:

          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size ,							lowercase_				):
              UpperCAmelCase          =       (
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size,
                  self.unet.config.sample_size,
              )
          else:
              UpperCAmelCase          =       (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          =       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          =       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          =       self.scheduler.step(
                  lowercase_ ,							lowercase_ ,							lowercase_ ,							eta=lowercase_ ,							use_clipped_model_output=lowercase_ ,							generator=lowercase_				).prev_sample

          UpperCAmelCase          =       (image / 2 + 0.5).clamp(0 ,							1				)
          UpperCAmelCase          =       image.cpu().permute(0 ,							2 ,							3 ,							1				).numpy()
          if output_type == "pil":
              UpperCAmelCase          =       self.numpy_to_pil(lowercase_				)

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowercase_				)





"""simple docstring"""

import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE_            =    '''1'''
SCREAMING_SNAKE_CASE_            =    '''0'''
SCREAMING_SNAKE_CASE_            =    '''1'''

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

SCREAMING_SNAKE_CASE_            =    128
SCREAMING_SNAKE_CASE_            =    1
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_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...''')
SCREAMING_SNAKE_CASE_            =    time.time()
SCREAMING_SNAKE_CASE_            =    2000
SCREAMING_SNAKE_CASE_            =    {}
for iter in range(max_iters):
   SCREAMING_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))



