

"""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      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  # TODO: is there an appropriate internal test set?
  snake_case_				       =       """ssube/stable-diffusion-x4-upscaler-onnx"""

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[Any]=0    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					floats_tensor((1, 3, 128, 128)					,rng=random.Random(A    )    )
   UpperCAmelCase__					:  int					   =					torch.manual_seed(A    )
   UpperCAmelCase__					:  List[Any]					   =					{
       """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    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   pipe.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  List[Any]					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  List[str]					   =					pipe(**A    ).images
   UpperCAmelCase__					:  Any					   =					image[0, -3:, -3:, -1].flatten()

   # started as 128, should now be 512
   assert image.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  Optional[int]					   =					np.array(
       [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]    )
   assert np.abs(image_slice - expected_slice    ).max() < 1e-1

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  Dict					   =					PNDMScheduler.from_config(pipe.scheduler.config					,skip_prk_steps=A    )
   pipe.set_progress_bar_config(disable=A    )

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

   assert image.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  str					   =					np.array(
       [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4]    )
   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-1

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  Any					   =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=A    )

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

   assert image.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  Optional[Any]					   =					np.array(
       [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5]    )

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

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  Dict					   =					EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=A    )

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

   assert image.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  Optional[Any]					   =					np.array(
       [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]    )
   assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-1




  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,provider="""CPUExecutionProvider"""    )
   UpperCAmelCase__					:  str					   =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
   pipe.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  str					   =					self.get_dummy_inputs()
   UpperCAmelCase__					:  List[str]					   =					pipe(**A    ).images
   UpperCAmelCase__					:  int					   =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  Optional[int]					   =					np.array(
       [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3]    )

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







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

  @property
  def    __lowercase       (  self		:			Tuple    ):






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

  @property
  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					ort.SessionOptions()
   UpperCAmelCase__					:  List[Any]					   =					False
   return options

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/img2img/sketch-mountains-input.jpg"""    )
   UpperCAmelCase__					:  Optional[int]					   =					init_image.resize((128, 128)    )
   # using the PNDM scheduler by default
   UpperCAmelCase__					:  Optional[Any]					   =					OnnxStableDiffusionUpscalePipeline.from_pretrained(
       """ssube/stable-diffusion-x4-upscaler-onnx"""					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   pipe.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  Optional[Any]					   =					"""A fantasy landscape, trending on artstation"""

   UpperCAmelCase__					:  str					   =					torch.manual_seed(0    )
   UpperCAmelCase__					:  Dict					   =					pipe(
       prompt=A					,image=A					,guidance_scale=7.5					,num_inference_steps=10					,generator=A					,output_type="""np"""					,)
   UpperCAmelCase__					:  List[Any]					   =					output.images
   UpperCAmelCase__					:  Optional[int]					   =					images[0, 255:258, 383:386, -1]

   assert images.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  Tuple					   =					np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2]    )
   # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/img2img/sketch-mountains-input.jpg"""    )
   UpperCAmelCase__					:  Optional[int]					   =					init_image.resize((128, 128)    )
   UpperCAmelCase__					:  List[str]					   =					LMSDiscreteScheduler.from_pretrained(
       """ssube/stable-diffusion-x4-upscaler-onnx"""					,subfolder="""scheduler"""    )
   UpperCAmelCase__					:  int					   =					OnnxStableDiffusionUpscalePipeline.from_pretrained(
       """ssube/stable-diffusion-x4-upscaler-onnx"""					,scheduler=A					,provider=self.gpu_provider					,sess_options=self.gpu_options					,)
   pipe.set_progress_bar_config(disable=A    )

   UpperCAmelCase__					:  Any					   =					"""A fantasy landscape, trending on artstation"""

   UpperCAmelCase__					:  int					   =					torch.manual_seed(0    )
   UpperCAmelCase__					:  Tuple					   =					pipe(
       prompt=A					,image=A					,guidance_scale=7.5					,num_inference_steps=20					,generator=A					,output_type="""np"""					,)
   UpperCAmelCase__					:  List[str]					   =					output.images
   UpperCAmelCase__					:  int					   =					images[0, 255:258, 383:386, -1]

   assert images.shape == (1, 512, 512, 3)
   UpperCAmelCase__					:  int					   =					np.array(
       [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6]    )
   # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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








"""simple docstring"""




from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__UpperCAmelCase =				logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase					)
class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			List[str]					,*A		:			Optional[int]					,**A		:			List[str]    ):






   '''simple docstring'''
   super().__init__(*A					,**A    )
   self.check_model_type(A    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			Dict=None					,A		:			Optional[int]=None					,A		:			Tuple=None					,**A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					{}, {}
   if padding is not None:
    UpperCAmelCase__					:  Union[str, Any]					   =					padding
   if truncation is not None:
    UpperCAmelCase__					:  List[str]					   =					truncation
   if top_k is not None:
    UpperCAmelCase__					:  List[Any]					   =					top_k
   return preprocess_params, {}, postprocess_params

  def __call__(  self		:			List[Any]					,A		:			Union["Image.Image", str]					,A		:			str = None					,**A		:			Union[str, Any]    ):






   '''simple docstring'''
   if isinstance(A					,(Image.Image, str)    ) and isinstance(A					,A    ):
    UpperCAmelCase__					:  Any					   =					{"""image""": image, """question""": question}
   else:
    UpperCAmelCase__					:  Dict					   =					image
   UpperCAmelCase__					:  Dict					   =					super().__call__(A					,**A    )
   return results

  def    __lowercase       (  self		:			Optional[int]					,A		:			Optional[Any]					,A		:			Optional[Any]=False					,A		:			List[Any]=False    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					load_image(inputs["""image"""]    )
   UpperCAmelCase__					:  Optional[Any]					   =					self.tokenizer(
       inputs["""question"""]					,return_tensors=self.framework					,padding=A					,truncation=A    )
   UpperCAmelCase__					:  int					   =					self.image_processor(images=A					,return_tensors=self.framework    )
   model_inputs.update(A    )
   return model_inputs

  def    __lowercase       (  self		:			Dict					,A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model(**A    )
   return model_outputs




  def    __lowercase       (  self		:			int					,A		:			List[Any]					,A		:			List[str]=5    ):






   '''simple docstring'''
   if top_k > self.model.config.num_labels:
    UpperCAmelCase__					:  Any					   =					self.model.config.num_labels

   if self.framework == "pt":
    UpperCAmelCase__					:  Optional[int]					   =					model_outputs.logits.sigmoid()[0]
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					probs.topk(A    )
   else:
    raise ValueError(f"Unsupported framework: {self.framework}"    )

   UpperCAmelCase__					:  List[str]					   =					scores.tolist()
   UpperCAmelCase__					:  Optional[int]					   =					ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(A					,A    )]





