





"""simple docstring"""






import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
		import tensorflow as tf
		from tensorflow.python.eager import context
		from tensorflow.python.framework import ops

		from transformers import GradientAccumulator, create_optimizer




@require_tf
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									self.assertEqual(len(_UpperCAmelCase       )		,     len(_UpperCAmelCase       )       )
									for a, b in zip(_UpperCAmelCase		,     _UpperCAmelCase       ):
													self.assertAlmostEqual(_UpperCAmelCase		,     _UpperCAmelCase		,     delta=_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					GradientAccumulator()
									accumulator([tf.constant([1.0, 2.0]       )]       )
									accumulator([tf.constant([-2.0, 1.0]       )]       )
									accumulator([tf.constant([-1.0, 2.0]       )]       )
									with self.assertRaises(_UpperCAmelCase       ):
													accumulator([tf.constant([1.0, 1.0]       ), tf.constant([2.0, 2.0]       )]       )
									self.assertEqual(accumulator.step		,     3       )
									self.assertEqual(len(accumulator.gradients       )		,     1       )
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,     [-2.0, 5.0]		,     tol=1e-2       )
									accumulator.reset()
									self.assertEqual(accumulator.step		,     0       )
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,     [0.0, 0.0]		,     tol=1e-2       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					None
									ops.enable_eager_execution_internal()
									__a	:  Optional[int]         =					tf.config.list_physical_devices('''CPU'''       )
									if len(_UpperCAmelCase       ) == 1:
													tf.config.set_logical_device_configuration(
													    physical_devices[0]		,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]       )
									__a	:  Any         =					tf.config.list_logical_devices(device_type='''CPU'''       )
									__a	:  List[str]         =					tf.distribute.MirroredStrategy(devices=devices[:2]       )

									with strategy.scope():
													__a	:  int         =					GradientAccumulator()
													__a	:  Tuple         =					tf.Variable([4.0, 3.0]       )
													__a    ,  __a	:  str         =					create_optimizer(5e-5		,     10		,     5       )
													__a	:  Union[str, Any]         =					tf.Variable([0.0, 0.0]		,     trainable=_UpperCAmelCase       )

									def accumulate_on_replica(_UpperCAmelCase       ):
													accumulator([gradient]       )

									def apply_on_replica():
													optimizer.apply_gradients(list(zip(accumulator.gradients		,     [variable]       )       )       )

									@tf.function
									def accumulate(_UpperCAmelCase		,     _UpperCAmelCase       ):
													with strategy.scope():
																	__a	:  List[Any]         =					strategy.experimental_local_results(_UpperCAmelCase       )
																	local_variables[0].assign(_UpperCAmelCase       )
																	local_variables[1].assign(_UpperCAmelCase       )
																	strategy.run(_UpperCAmelCase		,     args=(gradient_placeholder,)       )

									@tf.function
									def apply_grad():
													with strategy.scope():
																	strategy.run(_UpperCAmelCase       )

									def _check_local_values(_UpperCAmelCase		,     _UpperCAmelCase       ):
													__a	:  List[str]         =					strategy.experimental_local_results(accumulator._gradients[0]       )
													self.assertListAlmostEqual(values[0].value()		,     _UpperCAmelCase		,     tol=1e-2       )
													self.assertListAlmostEqual(values[1].value()		,     _UpperCAmelCase		,     tol=1e-2       )

									accumulate([1.0, 2.0]		,     [-1.0, 1.0]       )
									accumulate([3.0, -1.0]		,     [-1.0, -1.0]       )
									accumulate([-2.0, 2.0]		,     [3.0, -2.0]       )
									self.assertEqual(accumulator.step		,     3       )
									_check_local_values([2.0, 3.0]		,     [1.0, -2.0]       )
									apply_grad()
									self.assertListAlmostEqual(variable.value()		,     [4.0, 3.0]		,     tol=1e-2       )
									accumulator.reset()
									self.assertEqual(accumulator.step		,     0       )
									_check_local_values([0.0, 0.0]		,     [0.0, 0.0]       )






"""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