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     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     List[Any]     ,  snake_case__     :     Optional[int]		)      ->	int:
				super().__init__()

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

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



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

				# Sample gaussian noise to begin loop
				if isinstance(self.unet.config.sample_size     ,  lowercase_		):
						_lowerCamelCase				   =  (
						    batch_size,
						    self.unet.config.in_channels,
						    self.unet.config.sample_size,
						    self.unet.config.sample_size,
						)
				else:
						_lowerCamelCase				   =  (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."""		)

				_lowerCamelCase				   =  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
						_lowerCamelCase				   =  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
						_lowerCamelCase				   =  self.scheduler.step(
						    lowercase_     ,  lowercase_     ,  lowercase_     ,  eta=lowercase_     ,  use_clipped_model_output=lowercase_     ,  generator=lowercase_		).prev_sample

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



