



"""simple docstring"""






from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin






@dataclass
class 	__snake_case	(     _lowercase):
  snake_case__    :       torch.FloatTensor






class 	__snake_case	(     _lowercase  ,					_lowercase):




  @register_to_config
  def __init__(			self					:						int						, __lowerCAmelCase					:						int = 3_2						, __lowerCAmelCase					:						int = 6_4						, __lowerCAmelCase					:						int = 2_0						, __lowerCAmelCase					:						int = 7_6_8						, __lowerCAmelCase					:						Tuple=7_7						, __lowerCAmelCase					:						Tuple=4						, __lowerCAmelCase					:						float = 0.0						, __lowerCAmelCase					:						str = "silu"						, __lowerCAmelCase					:						Optional[str] = None						, __lowerCAmelCase					:						Optional[str] = None						, __lowerCAmelCase					:						Optional[str] = "linear"						, __lowerCAmelCase					:						Optional[str] = "prd"						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						Optional[int] = None						, ):






         """simple docstring"""






         super().__init__()
         _lowerCamelCase						:    Any         =			num_attention_heads
         _lowerCamelCase						:    Optional[int]         =			attention_head_dim
         _lowerCamelCase						:    Dict         =			num_attention_heads * attention_head_dim
         _lowerCamelCase						:    Optional[Any]         =			additional_embeddings

         _lowerCamelCase						:    List[Any]         =			time_embed_dim or inner_dim
         _lowerCamelCase						:    Optional[Any]         =			embedding_proj_dim or embedding_dim
         _lowerCamelCase						:    Tuple         =			clip_embed_dim or embedding_dim

         _lowerCamelCase						:    int         =			Timesteps(__lowerCAmelCase						, __lowerCAmelCase						, 0		)
         _lowerCamelCase						:    str         =			TimestepEmbedding(__lowerCAmelCase						, __lowerCAmelCase						, out_dim=__lowerCAmelCase						, act_fn=__lowerCAmelCase		)

         _lowerCamelCase						:    Union[str, Any]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)

         if embedding_proj_norm_type is None:
                _lowerCamelCase						:    Dict         =			None
         elif embedding_proj_norm_type == "layer":
                _lowerCamelCase						:    Any         =			nn.LayerNorm(__lowerCAmelCase		)
         else:
                raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''		)

         _lowerCamelCase						:    List[str]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)

         if encoder_hid_proj_type is None:
                _lowerCamelCase						:    Any         =			None
         elif encoder_hid_proj_type == "linear":
                _lowerCamelCase						:    List[str]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)
         else:
                raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''		)

         _lowerCamelCase						:    Optional[int]         =			nn.Parameter(torch.zeros(1						, num_embeddings + additional_embeddings						, __lowerCAmelCase		)		)

         if added_emb_type == "prd":
                _lowerCamelCase						:    Optional[Any]         =			nn.Parameter(torch.zeros(1						, 1						, __lowerCAmelCase		)		)
         elif added_emb_type is None:
                _lowerCamelCase						:    Union[str, Any]         =			None
         else:
                raise ValueError(
                    f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''		)

         _lowerCamelCase						:    Tuple         =			nn.ModuleList(
             [
                 BasicTransformerBlock(
                     __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, dropout=__lowerCAmelCase						, activation_fn='''gelu'''						, attention_bias=__lowerCAmelCase						, )
                 for d in range(__lowerCAmelCase		)
             ]		)

         if norm_in_type == "layer":
                _lowerCamelCase						:    Any         =			nn.LayerNorm(__lowerCAmelCase		)
         elif norm_in_type is None:
                _lowerCamelCase						:    Any         =			None
         else:
                raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.'''		)

         _lowerCamelCase						:    str         =			nn.LayerNorm(__lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    Optional[int]         =			torch.full(
             [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						, -1_00_00.0		)
         causal_attention_mask.triu_(1		)
         _lowerCamelCase						:    Dict         =			causal_attention_mask[None, ...]
         self.register_buffer('''causal_attention_mask'''						, __lowerCAmelCase						, persistent=__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			nn.Parameter(torch.zeros(1						, __lowerCAmelCase		)		)
         _lowerCamelCase						:    Tuple         =			nn.Parameter(torch.zeros(1						, __lowerCAmelCase		)		)




  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			{}

         def fn_recursive_add_processors(__lowerCAmelCase					:						str						, __lowerCAmelCase					:						torch.nn.Module						, __lowerCAmelCase					:						Dict[str, AttentionProcessor]		):
                if hasattr(__lowerCAmelCase						, '''set_processor'''		):
                       _lowerCamelCase						:    str         =			module.processor

                for sub_name, child in module.named_children():
                       fn_recursive_add_processors(f'''{name}.{sub_name}'''						, __lowerCAmelCase						, __lowerCAmelCase		)

                return processors

         for name, module in self.named_children():
                fn_recursive_add_processors(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)

         return processors




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						Union[AttentionProcessor, Dict[str, AttentionProcessor]]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			len(self.attn_processors.keys()		)

         if isinstance(__lowerCAmelCase						, __lowerCAmelCase		) and len(__lowerCAmelCase		) != count:
                raise ValueError(
                    f'''A dict of processors was passed, but the number of processors {len(__lowerCAmelCase		)} does not match the'''
                    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''		)

         def fn_recursive_attn_processor(__lowerCAmelCase					:						str						, __lowerCAmelCase					:						torch.nn.Module						, __lowerCAmelCase					:						Tuple		):
                if hasattr(__lowerCAmelCase						, '''set_processor'''		):
                       if not isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                              module.set_processor(__lowerCAmelCase		)
                       else:
                              module.set_processor(processor.pop(f'''{name}.processor'''		)		)

                for sub_name, child in module.named_children():
                       fn_recursive_attn_processor(f'''{name}.{sub_name}'''						, __lowerCAmelCase						, __lowerCAmelCase		)

         for name, module in self.named_children():
                fn_recursive_attn_processor(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         self.set_attn_processor(AttnProcessor()		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Union[torch.Tensor, float, int]						, __lowerCAmelCase					:						torch.FloatTensor						, __lowerCAmelCase					:						Optional[torch.FloatTensor] = None						, __lowerCAmelCase					:						Optional[torch.BoolTensor] = None						, __lowerCAmelCase					:						bool = True						, ):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			hidden_states.shape[0]

         _lowerCamelCase						:    Union[str, Any]         =			timestep
         if not torch.is_tensor(__lowerCAmelCase		):
                _lowerCamelCase						:    Dict         =			torch.tensor([timesteps]						, dtype=torch.long						, device=hidden_states.device		)
         elif torch.is_tensor(__lowerCAmelCase		) and len(timesteps.shape		) == 0:
                _lowerCamelCase						:    str         =			timesteps[None].to(hidden_states.device		)

         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         _lowerCamelCase						:    Tuple         =			timesteps * torch.ones(__lowerCAmelCase						, dtype=timesteps.dtype						, device=timesteps.device		)

         _lowerCamelCase						:    Tuple         =			self.time_proj(__lowerCAmelCase		)

         # timesteps does not contain any weights and will always return f32 tensors
         # but time_embedding might be fp16, so we need to cast here.
         _lowerCamelCase						:    Union[str, Any]         =			timesteps_projected.to(dtype=self.dtype		)
         _lowerCamelCase						:    Any         =			self.time_embedding(__lowerCAmelCase		)

         if self.embedding_proj_norm is not None:
                _lowerCamelCase						:    str         =			self.embedding_proj_norm(__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			self.embedding_proj(__lowerCAmelCase		)
         if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
                _lowerCamelCase						:    Optional[Any]         =			self.encoder_hidden_states_proj(__lowerCAmelCase		)
         elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
                raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''		)

         _lowerCamelCase						:    str         =			self.proj_in(__lowerCAmelCase		)

         _lowerCamelCase						:    Tuple         =			self.positional_embedding.to(hidden_states.dtype		)

         _lowerCamelCase						:    Tuple         =			[]
         _lowerCamelCase						:    Dict         =			0

         if encoder_hidden_states is not None:
                additional_embeds.append(__lowerCAmelCase		)
                additional_embeddings_len += encoder_hidden_states.shape[1]

         if len(proj_embeddings.shape		) == 2:
                _lowerCamelCase						:    str         =			proj_embeddings[:, None, :]

         if len(hidden_states.shape		) == 2:
                _lowerCamelCase						:    Dict         =			hidden_states[:, None, :]

         _lowerCamelCase						:    int         =			additional_embeds + [
             proj_embeddings,
             time_embeddings[:, None, :],
             hidden_states,
         ]

         if self.prd_embedding is not None:
                _lowerCamelCase						:    int         =			self.prd_embedding.to(hidden_states.dtype		).expand(__lowerCAmelCase						, -1						, -1		)
                additional_embeds.append(__lowerCAmelCase		)

         _lowerCamelCase						:    str         =			torch.cat(
             __lowerCAmelCase						, dim=1						, )

         # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
         _lowerCamelCase						:    Dict         =			additional_embeddings_len + proj_embeddings.shape[1] + 1
         if positional_embeddings.shape[1] < hidden_states.shape[1]:
                _lowerCamelCase						:    List[Any]         =			F.pad(
                    __lowerCAmelCase						, (
                        0,
                        0,
                        additional_embeddings_len,
                        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                    )						, value=0.0						, )

         _lowerCamelCase						:    Any         =			hidden_states + positional_embeddings

         if attention_mask is not None:
                _lowerCamelCase						:    Union[str, Any]         =			(1 - attention_mask.to(hidden_states.dtype		)) * -1_00_00.0
                _lowerCamelCase						:    Tuple         =			F.pad(__lowerCAmelCase						, (0, self.additional_embeddings)						, value=0.0		)
                _lowerCamelCase						:    Any         =			(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype		)
                _lowerCamelCase						:    int         =			attention_mask.repeat_interleave(self.config.num_attention_heads						, dim=0		)

         if self.norm_in is not None:
                _lowerCamelCase						:    List[str]         =			self.norm_in(__lowerCAmelCase		)

         for block in self.transformer_blocks:
                _lowerCamelCase						:    List[str]         =			block(__lowerCAmelCase						, attention_mask=__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			self.norm_out(__lowerCAmelCase		)

         if self.prd_embedding is not None:
                _lowerCamelCase						:    Any         =			hidden_states[:, -1]
         else:
                _lowerCamelCase						:    Dict         =			hidden_states[:, additional_embeddings_len:]

         _lowerCamelCase						:    str         =			self.proj_to_clip_embeddings(__lowerCAmelCase		)

         if not return_dict:
                return (predicted_image_embedding,)

         return PriorTransformerOutput(predicted_image_embedding=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			(prior_latents * self.clip_std) + self.clip_mean
         return prior_latents







"""simple docstring"""






import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort






class 	__snake_case	(     _lowercase  ,					unittest.TestCase):
  # TODO: is there an appropriate internal test set?
  snake_case__    :       List[str]        =						"ssube/stable-diffusion-x4-upscaler-onnx"




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						int=0		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			floats_tensor((1, 3, 1_2_8, 1_2_8)						, rng=random.Random(__lowerCAmelCase		)		)
         _lowerCamelCase						:    Union[str, Any]         =			torch.manual_seed(__lowerCAmelCase		)
         _lowerCamelCase						:    Tuple         =			{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						, provider='''CPUExecutionProvider'''		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    Union[str, Any]         =			self.get_dummy_inputs()
         _lowerCamelCase						:    Any         =			pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    Dict         =			image[0, -3:, -3:, -1].flatten()

         # started as 128, should now be 512
         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    str         =			np.array(
             [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]		)
         assert np.abs(image_slice - expected_slice		).max() < 1E-1




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						, provider='''CPUExecutionProvider'''		)
         _lowerCamelCase						:    List[str]         =			PNDMScheduler.from_config(pipe.scheduler.config						, skip_prk_steps=__lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			self.get_dummy_inputs()
         _lowerCamelCase						:    Optional[Any]         =			pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    str         =			image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    Optional[int]         =			np.array(
             [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64]		)
         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						, provider='''CPUExecutionProvider'''		)
         _lowerCamelCase						:    Optional[int]         =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    Tuple         =			self.get_dummy_inputs()
         _lowerCamelCase						:    str         =			pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    Tuple         =			image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    str         =			np.array(
             [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						, provider='''CPUExecutionProvider'''		)
         _lowerCamelCase						:    Union[str, Any]         =			EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			self.get_dummy_inputs()
         _lowerCamelCase						:    Tuple         =			pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    Union[str, Any]         =			image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    Union[str, Any]         =			np.array(
             [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]		)
         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						, provider='''CPUExecutionProvider'''		)
         _lowerCamelCase						:    int         =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    Union[str, Any]         =			self.get_dummy_inputs()
         _lowerCamelCase						:    List[Any]         =			pipe(**__lowerCAmelCase		).images
         _lowerCamelCase						:    Optional[Any]         =			image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    Optional[int]         =			np.array(
             [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1






@nightly
@require_onnxruntime
@require_torch_gpu
class 	__snake_case	(     unittest.TestCase):




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			ort.SessionOptions()
         _lowerCamelCase						:    List[str]         =			False
         return options




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''		)
         _lowerCamelCase						:    Any         =			init_image.resize((1_2_8, 1_2_8)		)
         # using the PNDM scheduler by default
         _lowerCamelCase						:    List[str]         =			OnnxStableDiffusionUpscalePipeline.from_pretrained(
             '''ssube/stable-diffusion-x4-upscaler-onnx'''						, provider=self.gpu_provider						, sess_options=self.gpu_options						, )
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			'''A fantasy landscape, trending on artstation'''

         _lowerCamelCase						:    List[Any]         =			torch.manual_seed(0		)
         _lowerCamelCase						:    List[str]         =			pipe(
             prompt=__lowerCAmelCase						, image=__lowerCAmelCase						, guidance_scale=7.5						, num_inference_steps=1_0						, generator=__lowerCAmelCase						, output_type='''np'''						, )
         _lowerCamelCase						:    List[Any]         =			output.images
         _lowerCamelCase						:    List[Any]         =			images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    str         =			np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72]		)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 2E-2




  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''		)
         _lowerCamelCase						:    int         =			init_image.resize((1_2_8, 1_2_8)		)
         _lowerCamelCase						:    str         =			LMSDiscreteScheduler.from_pretrained(
             '''ssube/stable-diffusion-x4-upscaler-onnx'''						, subfolder='''scheduler'''		)
         _lowerCamelCase						:    Dict         =			OnnxStableDiffusionUpscalePipeline.from_pretrained(
             '''ssube/stable-diffusion-x4-upscaler-onnx'''						, scheduler=__lowerCAmelCase						, provider=self.gpu_provider						, sess_options=self.gpu_options						, )
         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[int]         =			'''A fantasy landscape, trending on artstation'''

         _lowerCamelCase						:    int         =			torch.manual_seed(0		)
         _lowerCamelCase						:    List[str]         =			pipe(
             prompt=__lowerCAmelCase						, image=__lowerCAmelCase						, guidance_scale=7.5						, num_inference_steps=2_0						, generator=__lowerCAmelCase						, output_type='''np'''						, )
         _lowerCamelCase						:    Union[str, Any]         =			output.images
         _lowerCamelCase						:    Optional[Any]         =			images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCamelCase						:    str         =			np.array(
             [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66]		)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 2E-2


