'''simple docstring'''


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


snake_case__					:      str				      =  logging.get_logger(__name__)  # pylint: disable=invalid-name






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       super().__init__()
       self.register_modules(
           vae=snake_case_				,		text_encoder=snake_case_				,		tokenizer=snake_case_				,		unet=snake_case_				,		scheduler=snake_case_				,		safety_checker=snake_case_				,		feature_extractor=snake_case_				,		)


  def _UpperCamelCase					(			self				,		snake_case_ = "auto"						):




       '''simple docstring'''

       if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            UpperCAmelCase_							:       Tuple       =				self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.enable_attention_slicing(snake_case_						)






  @torch.no_grad()
  def __call__(			self				,		snake_case_				,		snake_case_ = 5_1_2				,		snake_case_ = 5_1_2				,		snake_case_ = 5_0				,		snake_case_ = 7.5				,		snake_case_ = None				,		snake_case_ = 1				,		snake_case_ = 0.0				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = "pil"				,		snake_case_ = True				,		snake_case_ = None				,		snake_case_ = 1				,		snake_case_ = None				,		**snake_case_				,		):




       '''simple docstring'''

       if isinstance(snake_case_				,		snake_case_						):
            UpperCAmelCase_							:       Any       =				1
       elif isinstance(snake_case_				,		snake_case_						):
            UpperCAmelCase_							:       List[Any]       =				len(snake_case_						)
       else:
            raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case_						)}'''						)

       if height % 8 != 0 or width % 8 != 0:
            raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''						)

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(snake_case_				,		snake_case_						) or callback_steps <= 0)
       ):
            raise ValueError(
                F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                F''' {type(snake_case_						)}.'''						)

       # get prompt text embeddings
       UpperCAmelCase_							:       Union[str, Any]       =				self.tokenizer(
           snake_case_				,		padding='max_length'				,		max_length=self.tokenizer.model_max_length				,		return_tensors='pt'				,		)
       UpperCAmelCase_							:       Union[str, Any]       =				text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            UpperCAmelCase_							:       Dict       =				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]						)
            logger.warning(
                'The following part of your input was truncated because CLIP can only handle sequences up to'
                F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''						)
            UpperCAmelCase_							:       int       =				text_input_ids[:, : self.tokenizer.model_max_length]

       if text_embeddings is None:
            UpperCAmelCase_							:       List[Any]       =				self.text_encoder(text_input_ids.to(self.device						)						)[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				text_embeddings.shape
       UpperCAmelCase_							:       str       =				text_embeddings.repeat(1				,		snake_case_				,		1						)
       UpperCAmelCase_							:       List[Any]       =				text_embeddings.view(bs_embed * num_images_per_prompt				,		snake_case_				,		-1						)

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       UpperCAmelCase_							:       List[str]       =				guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
            UpperCAmelCase_							:       List[str]
            if negative_prompt is None:
                 UpperCAmelCase_							:       Any       =				['']
            elif type(snake_case_						) is not type(snake_case_						):
                 raise TypeError(
                     F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_						)} !='''
                     F''' {type(snake_case_						)}.'''						)
            elif isinstance(snake_case_				,		snake_case_						):
                 UpperCAmelCase_							:       Optional[int]       =				[negative_prompt]
            elif batch_size != len(snake_case_						):
                 raise ValueError(
                     F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case_						)}, but `prompt`:'''
                     F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                     ' the batch size of `prompt`.'						)
            else:
                 UpperCAmelCase_							:       Dict       =				negative_prompt

            UpperCAmelCase_							:       List[Any]       =				text_input_ids.shape[-1]
            UpperCAmelCase_							:       Union[str, Any]       =				self.tokenizer(
                snake_case_				,		padding='max_length'				,		max_length=snake_case_				,		truncation=snake_case_				,		return_tensors='pt'				,		)
            UpperCAmelCase_							:       List[str]       =				self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            UpperCAmelCase_							:       Dict       =				uncond_embeddings.shape[1]
            UpperCAmelCase_							:       Optional[Any]       =				uncond_embeddings.repeat(snake_case_				,		snake_case_				,		1						)
            UpperCAmelCase_							:       Union[str, Any]       =				uncond_embeddings.view(batch_size * num_images_per_prompt				,		snake_case_				,		-1						)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            UpperCAmelCase_							:       Dict       =				torch.cat([uncond_embeddings, text_embeddings]						)

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       UpperCAmelCase_							:       Optional[Any]       =				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCAmelCase_							:       Dict       =				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
       UpperCAmelCase_							:       int       =				text_embeddings.dtype
       if latents is None:
            if self.device.type == "mps":
                 # randn does not exist on mps
                 UpperCAmelCase_							:       Tuple       =				torch.randn(
                     snake_case_				,		generator=snake_case_				,		device='cpu'				,		dtype=snake_case_						).to(self.device						)
                 UpperCAmelCase_							:       Union[str, Any]       =				torch.randn(snake_case_				,		generator=snake_case_				,		device='cpu'				,		dtype=snake_case_						).to(
                     self.device						)
            else:
                 UpperCAmelCase_							:       str       =				torch.randn(
                     snake_case_				,		generator=snake_case_				,		device=self.device				,		dtype=snake_case_						)
                 UpperCAmelCase_							:       str       =				torch.randn(snake_case_				,		generator=snake_case_				,		device=self.device				,		dtype=snake_case_						)
       else:
            if latents_reference.shape != latents_shape:
                 raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''						)
            UpperCAmelCase_							:       Optional[Any]       =				latents_reference.to(self.device						)
            UpperCAmelCase_							:       Any       =				latents.to(self.device						)

       # This is the key part of the pipeline where we
       # try to ensure that the generated images w/ the same seed
       # but different sizes actually result in similar images
       UpperCAmelCase_							:       Union[str, Any]       =				(latents_shape[3] - latents_shape_reference[3]) // 2
       UpperCAmelCase_							:       int       =				(latents_shape[2] - latents_shape_reference[2]) // 2
       UpperCAmelCase_							:       List[str]       =				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       UpperCAmelCase_							:       int       =				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       UpperCAmelCase_							:       int       =				0 if dx < 0 else dx
       UpperCAmelCase_							:       Any       =				0 if dy < 0 else dy
       UpperCAmelCase_							:       str       =				max(-dx				,		0						)
       UpperCAmelCase_							:       Optional[Any]       =				max(-dy				,		0						)
       # import pdb
       # pdb.set_trace()
       UpperCAmelCase_							:       Optional[Any]       =				latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(snake_case_						)

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       UpperCAmelCase_							:       Any       =				self.scheduler.timesteps.to(self.device						)

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCAmelCase_							:       int       =				latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       UpperCAmelCase_							:       List[str]       =				'eta' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
       UpperCAmelCase_							:       Any       =				{}
       if accepts_eta:
            UpperCAmelCase_							:       Any       =				eta

       for i, t in enumerate(self.progress_bar(snake_case_						)						):
            # expand the latents if we are doing classifier free guidance
            UpperCAmelCase_							:       Tuple       =				torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
            UpperCAmelCase_							:       Union[str, Any]       =				self.scheduler.scale_model_input(snake_case_				,		snake_case_						)

            # predict the noise residual
            UpperCAmelCase_							:       Any       =				self.unet(snake_case_				,		snake_case_				,		encoder_hidden_states=snake_case_						).sample

            # perform guidance
            if do_classifier_free_guidance:
                 UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				noise_pred.chunk(2						)
                 UpperCAmelCase_							:       int       =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase_							:       List[str]       =				self.scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                 callback(snake_case_				,		snake_case_				,		snake_case_						)

       UpperCAmelCase_							:       Optional[int]       =				1 / 0.1_82_15 * latents
       UpperCAmelCase_							:       List[Any]       =				self.vae.decode(snake_case_						).sample

       UpperCAmelCase_							:       int       =				(image / 2 + 0.5).clamp(0				,		1						)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       UpperCAmelCase_							:       Optional[Any]       =				image.cpu().permute(0				,		2				,		3				,		1						).float().numpy()

       if self.safety_checker is not None:
            UpperCAmelCase_							:       Union[str, Any]       =				self.feature_extractor(self.numpy_to_pil(snake_case_						)				,		return_tensors='pt'						).to(
                self.device						)
            UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				self.safety_checker(
                images=snake_case_				,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype						)						)
       else:
            UpperCAmelCase_							:       List[str]       =				None

       if output_type == "pil":
            UpperCAmelCase_							:       Optional[Any]       =				self.numpy_to_pil(snake_case_						)

       if not return_dict:
            return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=snake_case_				,		nsfw_content_detected=snake_case_						)







'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


snake_case__					:      Optional[int]				      =  '''1'''
snake_case__					:      str				      =  '''0'''
snake_case__					:      List[str]				      =  '''1'''

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

snake_case__					:      List[Any]				      =  128
snake_case__					:      Union[str, Any]				      =  1
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Union[str, Any]				      =  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__					:      Union[str, Any]				      =  time.time()
snake_case__					:      str				      =  2000
snake_case__					:      Tuple				      =  {}
for iter in range(max_iters):
 snake_case__					:      str				      =  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))






