





"""simple docstring"""






import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()




class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					PriorTransformer
					__lowerCAmelCase					=					'''hidden_states'''







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					4
									__a	:  List[str]         =					8
									__a	:  List[str]         =					7

									__a	:  List[str]         =					floats_tensor((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )

									__a	:  str         =					floats_tensor((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					floats_tensor((batch_size, num_embeddings, embedding_dim)       ).to(_UpperCAmelCase       )

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=0       ):
									torch.manual_seed(_UpperCAmelCase       )
									__a	:  Any         =					4
									__a	:  Dict         =					8
									__a	:  Tuple         =					7

									__a	:  List[str]         =					torch.randn((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )

									__a	:  Any         =					torch.randn((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )
									__a	:  List[Any]         =					torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(_UpperCAmelCase       )

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}







					@property
					def 	_lowerCamelCase						(  self       ):
									return (4, 8)







					@property
					def 	_lowerCamelCase						(  self       ):
									return (4, 8)







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					{
									    '''num_attention_heads''': 2,
									    '''attention_head_dim''': 4,
									    '''num_layers''': 2,
									    '''embedding_dim''': 8,
									    '''num_embeddings''': 7,
									    '''additional_embeddings''': 4,
									}
									__a	:  Any         =					self.dummy_input
									return init_dict, inputs_dict







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  str         =					PriorTransformer.from_pretrained(
									    '''hf-internal-testing/prior-dummy'''		,     output_loading_info=_UpperCAmelCase       )
									self.assertIsNotNone(_UpperCAmelCase       )
									self.assertEqual(len(loading_info['''missing_keys''']       )		,     0       )

									model.to(_UpperCAmelCase       )
									__a	:  Dict         =					model(**self.dummy_input       )[0]

									assert hidden_states is not None, "Make sure output is not None"







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  Dict         =					self.prepare_init_args_and_inputs_for_common()

									__a	:  int         =					self.model_class(**_UpperCAmelCase       )
									__a	:  Dict         =					inspect.signature(model.forward       )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__a	:  List[Any]         =					[*signature.parameters.keys()]

									__a	:  str         =					['''hidden_states''', '''timestep''']
									self.assertListEqual(arg_names[:2]		,     _UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''       )
									__a	:  Union[str, Any]         =					model.to(_UpperCAmelCase       )

									if hasattr(_UpperCAmelCase		,     '''set_default_attn_processor'''       ):
													model.set_default_attn_processor()

									__a	:  List[str]         =					self.get_dummy_seed_input()

									with torch.no_grad():
													__a	:  Optional[Any]         =					model(**_UpperCAmelCase       )[0]

									__a	:  str         =					output[0, :5].flatten().cpu()
									print(_UpperCAmelCase       )

									# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
									# the expected output slices are not the same for CPU and GPU.
									__a	:  int         =					torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]       )
									self.assertTrue(torch_all_close(_UpperCAmelCase		,     _UpperCAmelCase		,     rtol=1e-2       )       )






@slow
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=1		,     _UpperCAmelCase=768		,     _UpperCAmelCase=77		,     _UpperCAmelCase=0       ):
									torch.manual_seed(_UpperCAmelCase       )
									__a	:  Tuple         =					batch_size
									__a	:  Optional[int]         =					embedding_dim
									__a	:  int         =					num_embeddings

									__a	:  str         =					torch.randn((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )

									__a	:  Tuple         =					torch.randn((batch_size, embedding_dim)       ).to(_UpperCAmelCase       )
									__a	:  Optional[Any]         =					torch.randn((batch_size, num_embeddings, embedding_dim)       ).to(_UpperCAmelCase       )

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}







					def 	_lowerCamelCase						(  self       ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					@parameterized.expand(
					    [
					        # fmt: off
					        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
					        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
					        # fmt: on
					    ]       )
					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  str         =					PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''		,     subfolder='''prior'''       )
									model.to(_UpperCAmelCase       )
									__a	:  str         =					self.get_dummy_seed_input(seed=_UpperCAmelCase       )

									with torch.no_grad():
													__a	:  Any         =					model(**_UpperCAmelCase       )[0]

									assert list(sample.shape       ) == [1, 768]

									__a	:  Optional[int]         =					sample[0, :8].flatten().cpu()
									print(_UpperCAmelCase       )
									__a	:  Any         =					torch.tensor(_UpperCAmelCase       )

									assert torch_all_close(_UpperCAmelCase		,     _UpperCAmelCase		,     atol=1e-3       )






"""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 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=0       ):
									__a	:  Tuple         =					floats_tensor((1, 3, 128, 128)		,     rng=random.Random(_UpperCAmelCase       )       )
									__a	:  Any         =					np.random.RandomState(_UpperCAmelCase       )
									__a	:  Any         =					{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''strength''': 0.7_5,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Dict         =					self.get_dummy_inputs()
									__a	:  Any         =					pipe(**_UpperCAmelCase       ).images
									__a	:  Optional[int]         =					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 128, 128, 3)
									__a	:  List[Any]         =					np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]       )
									assert np.abs(image_slice - expected_slice       ).max() < 1e-1







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  Tuple         =					PNDMScheduler.from_config(pipe.scheduler.config		,     skip_prk_steps=_UpperCAmelCase       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

									assert image.shape == (1, 128, 128, 3)
									__a	:  Optional[int]         =					np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  Any         =					LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									# warmup pass to apply optimizations
									__a	:  Any         =					pipe(**self.get_dummy_inputs()       )

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

									assert image.shape == (1, 128, 128, 3)
									__a	:  int         =					np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  Any         =					EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

									assert image.shape == (1, 128, 128, 3)
									__a	:  Optional[Any]         =					np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  Any         =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

									__a	:  Union[str, Any]         =					self.get_dummy_inputs()
									__a	:  str         =					pipe(**_UpperCAmelCase       ).images
									__a	:  Union[str, Any]         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									__a	:  Optional[int]         =					np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]       )

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider='''CPUExecutionProvider'''       )
									__a	:  str         =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

									assert image.shape == (1, 128, 128, 3)
									__a	:  Optional[Any]         =					np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]       )

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






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







					'''simple docstring'''




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







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					ort.SessionOptions()
									__a	:  Any         =					False
									return options







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

									__a	:  Tuple         =					'''A fantasy landscape, trending on artstation'''

									__a	:  Tuple         =					np.random.RandomState(0       )
									__a	:  int         =					pipe(
									    prompt=_UpperCAmelCase		,     image=_UpperCAmelCase		,     strength=0.7_5		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=_UpperCAmelCase		,     output_type='''np'''		,     )
									__a	:  List[Any]         =					output.images
									__a	:  int         =					images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									__a	:  Any         =					np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]       )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/img2img/sketch-mountains-input.jpg'''       )
									__a	:  Tuple         =					init_image.resize((768, 512)       )
									__a	:  str         =					LMSDiscreteScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''		,     subfolder='''scheduler'''		,     revision='''onnx'''       )
									__a	:  Optional[int]         =					OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''		,     revision='''onnx'''		,     scheduler=_UpperCAmelCase		,     safety_checker=_UpperCAmelCase		,     feature_extractor=_UpperCAmelCase		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

									__a	:  str         =					np.random.RandomState(0       )
									__a	:  str         =					pipe(
									    prompt=_UpperCAmelCase		,     image=_UpperCAmelCase		,     strength=0.7_5		,     guidance_scale=7.5		,     num_inference_steps=20		,     generator=_UpperCAmelCase		,     output_type='''np'''		,     )
									__a	:  Dict         =					output.images
									__a	:  List[Any]         =					images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									__a	:  Dict         =					np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]       )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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