



"""simple docstring"""

import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Union[str, Any]	      =	"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

 def 		_snake_case      (  self	: Any						,  lowercase_	: Any=0       ):
     snake_case_				:  Optional[int]       =			floats_tensor((1, 3, 128, 128)						,  rng=random.Random(lowercase_       )       )
     snake_case_				:  Tuple       =			np.random.RandomState(lowercase_       )
     snake_case_				:  Optional[Any]       =			{
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''generator''': generator,
         '''num_inference_steps''': 3,
         '''strength''': 0.75,
         '''guidance_scale''': 7.5,
         '''output_type''': '''numpy''',
     }
     return inputs

 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Dict       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  int       =			self.get_dummy_inputs()
     snake_case_				:  int       =			pipe(**lowercase_       ).images
     snake_case_				:  Dict       =			image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  List[Any]       =			np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]       )
     assert np.abs(image_slice - expected_slice       ).max() < 1E-1

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  Any       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  List[str]       =			PNDMScheduler.from_config(pipe.scheduler.config						,  skip_prk_steps=lowercase_       )
     pipe.set_progress_bar_config(disable=lowercase_       )

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

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  int       =			np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]       )

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

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  int       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  List[Any]       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     # warmup pass to apply optimizations
     snake_case_				:  Tuple       =			pipe(**self.get_dummy_inputs()       )

     snake_case_				:  int       =			self.get_dummy_inputs()
     snake_case_				:  Tuple       =			pipe(**lowercase_       ).images
     snake_case_				:  Union[str, Any]       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Optional[Any]       =			np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]       )

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

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Dict       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  List[str]       =			EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=lowercase_       )

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

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  str       =			np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]       )

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

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Any       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  Optional[Any]       =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=lowercase_       )

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

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Dict       =			np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]       )

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






 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Dict       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  str       =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=lowercase_       )

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

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  List[str]       =			np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]       )

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





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

 @property
 def 		_snake_case      (  self	: Dict       ):
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )

 @property
 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Tuple       =			ort.SessionOptions()
     snake_case_				:  str       =			False
     return options

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Union[str, Any]       =			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''       )
     snake_case_				:  Tuple       =			init_image.resize((768, 512)       )
     # using the PNDM scheduler by default
     snake_case_				:  Optional[Any]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4'''						,  revision='''onnx'''						,  safety_checker=lowercase_						,  feature_extractor=lowercase_						,  provider=self.gpu_provider						,  sess_options=self.gpu_options						,  )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  List[str]       =			'''A fantasy landscape, trending on artstation'''

     snake_case_				:  Tuple       =			np.random.RandomState(0       )
     snake_case_				:  Optional[int]       =			pipe(
         prompt=lowercase_						,  image=lowercase_						,  strength=0.75						,  guidance_scale=7.5						,  num_inference_steps=10						,  generator=lowercase_						,  output_type='''np'''						,  )
     snake_case_				:  str       =			output.images
     snake_case_				:  str       =			images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 768, 3)
     snake_case_				:  Dict       =			np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]       )
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  Any       =			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''       )
     snake_case_				:  Dict       =			init_image.resize((768, 512)       )
     snake_case_				:  Dict       =			LMSDiscreteScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''						,  subfolder='''scheduler'''						,  revision='''onnx'''       )
     snake_case_				:  Dict       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''						,  revision='''onnx'''						,  scheduler=lowercase_						,  safety_checker=lowercase_						,  feature_extractor=lowercase_						,  provider=self.gpu_provider						,  sess_options=self.gpu_options						,  )
     pipe.set_progress_bar_config(disable=lowercase_       )

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

     snake_case_				:  int       =			np.random.RandomState(0       )
     snake_case_				:  Optional[int]       =			pipe(
         prompt=lowercase_						,  image=lowercase_						,  strength=0.75						,  guidance_scale=7.5						,  num_inference_steps=20						,  generator=lowercase_						,  output_type='''np'''						,  )
     snake_case_				:  Union[str, Any]       =			output.images
     snake_case_				:  Any       =			images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 768, 3)
     snake_case_				:  Tuple       =			np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]       )
     # 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"""

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[Any]       =			AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     snake_case_				:  List[Any]       =			AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     model.to(lowercase_       )

     from datasets import load_dataset

     snake_case_				:  str       =			load_dataset('''nielsr/rvlcdip-demo'''       )

     snake_case_				:  Tuple       =			dataset['''train'''][0]['''image'''].convert('''RGB'''       )

     snake_case_				:  Union[str, Any]       =			image_processor(lowercase_						,  return_tensors='''pt'''       ).to(lowercase_       )

     # forward pass
     with torch.no_grad():
         snake_case_				:  List[Any]       =			model(**lowercase_       )
         snake_case_				:  int       =			outputs.logits

     snake_case_				:  Any       =			torch.Size((1, 16)       )
     self.assertEqual(logits.shape						,  lowercase_       )

     snake_case_				:  Any       =			torch.tensor(
         [-0.41_58, -0.40_92, -0.43_47]						,  device=lowercase_						,  dtype=torch.float						,  )
     self.assertTrue(torch.allclose(logits[0, :3]						,  lowercase_						,  atol=1E-4       )       )





