




'''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      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    Optional[int]=0   )     ->				Dict:


														"""simple docstring"""


														_a      	=							floats_tensor((1, 3, 1_28, 1_28)				,     rng=random.Random(lowerCAmelCase_   )   )
														_a      	=							np.random.RandomState(lowerCAmelCase_   )
														_a      	=							{
														    '''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			:    Any   )     ->				str:


														"""simple docstring"""


														_a      	=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider'''   )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							self.get_dummy_inputs()
														_a      	=							pipe(**lowerCAmelCase_   ).images
														_a      	=							image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 1_28, 1_28, 3)
														_a      	=							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			:    Tuple   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider'''   )
														_a      	=							PNDMScheduler.from_config(pipe.scheduler.config				,     skip_prk_steps=lowerCAmelCase_   )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							self.get_dummy_inputs()
														_a      	=							pipe(**lowerCAmelCase_   ).images
														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 1_28, 1_28, 3)
														_a      	=							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			:    str   )     ->				Dict:


														"""simple docstring"""


														_a      	=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider'''   )
														_a      	=							LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														# warmup pass to apply optimizations
														_a      	=							pipe(**self.get_dummy_inputs()   )

														_a      	=							self.get_dummy_inputs()
														_a      	=							pipe(**lowerCAmelCase_   ).images
														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 1_28, 1_28, 3)
														_a      	=							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			:    str   )     ->				Any:


														"""simple docstring"""


														_a      	=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider'''   )
														_a      	=							EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							self.get_dummy_inputs()
														_a      	=							pipe(**lowerCAmelCase_   ).images
														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 1_28, 1_28, 3)
														_a      	=							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			:    Optional[Any]   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider'''   )
														_a      	=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							self.get_dummy_inputs()
														_a      	=							pipe(**lowerCAmelCase_   ).images
														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 1_28, 1_28, 3)
														_a      	=							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			:    Tuple   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider'''   )
														_a      	=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							self.get_dummy_inputs()
														_a      	=							pipe(**lowerCAmelCase_   ).images
														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 1_28, 1_28, 3)
														_a      	=							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      A       (						unittest.TestCase       ):







							@property
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Any:


														"""simple docstring"""


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







							@property
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							ort.SessionOptions()
														_a      	=							False
														return options







							def   __lowerCAmelCase     (    self			:    str   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/img2img/sketch-mountains-input.jpg'''   )
														_a      	=							init_image.resize((7_68, 5_12)   )
														# using the PNDM scheduler by default
														_a      	=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
														    '''CompVis/stable-diffusion-v1-4'''				,     revision='''onnx'''				,     safety_checker=lowerCAmelCase_				,     feature_extractor=lowerCAmelCase_				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							'''A fantasy landscape, trending on artstation'''

														_a      	=							np.random.RandomState(0   )
														_a      	=							pipe(
														    prompt=lowerCAmelCase_				,     image=lowerCAmelCase_				,     strength=0.7_5				,     guidance_scale=7.5				,     num_inference_steps=10				,     generator=lowerCAmelCase_				,     output_type='''np'''				,     )
														_a      	=							output.images
														_a      	=							images[0, 2_55:2_58, 3_83:3_86, -1]

														assert images.shape == (1, 5_12, 7_68, 3)
														_a      	=							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			:    Optional[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/img2img/sketch-mountains-input.jpg'''   )
														_a      	=							init_image.resize((7_68, 5_12)   )
														_a      	=							LMSDiscreteScheduler.from_pretrained(
														    '''runwayml/stable-diffusion-v1-5'''				,     subfolder='''scheduler'''				,     revision='''onnx'''   )
														_a      	=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
														    '''runwayml/stable-diffusion-v1-5'''				,     revision='''onnx'''				,     scheduler=lowerCAmelCase_				,     safety_checker=lowerCAmelCase_				,     feature_extractor=lowerCAmelCase_				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
														pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							'''A fantasy landscape, trending on artstation'''

														_a      	=							np.random.RandomState(0   )
														_a      	=							pipe(
														    prompt=lowerCAmelCase_				,     image=lowerCAmelCase_				,     strength=0.7_5				,     guidance_scale=7.5				,     num_inference_steps=20				,     generator=lowerCAmelCase_				,     output_type='''np'''				,     )
														_a      	=							output.images
														_a      	=							images[0, 2_55:2_58, 3_83:3_86, -1]

														assert images.shape == (1, 5_12, 7_68, 3)
														_a      	=							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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case : str  				=						{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[str]  				=						['LayoutLMv3TokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Optional[int]  				=						[
				    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LayoutLMv3ForQuestionAnswering',
				    'LayoutLMv3ForSequenceClassification',
				    'LayoutLMv3ForTokenClassification',
				    'LayoutLMv3Model',
				    'LayoutLMv3PreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Tuple  				=						[
				    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFLayoutLMv3ForQuestionAnswering',
				    'TFLayoutLMv3ForSequenceClassification',
				    'TFLayoutLMv3ForTokenClassification',
				    'TFLayoutLMv3Model',
				    'TFLayoutLMv3PreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[Any]  				=						['LayoutLMv3FeatureExtractor']
				_snake_case : Tuple  				=						['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
				from .configuration_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    LayoutLMvaConfig,
				    LayoutLMvaOnnxConfig,
				)
				from .processing_layoutlmva import LayoutLMvaProcessor
				from .tokenization_layoutlmva import LayoutLMvaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlmva import (
								    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMvaForQuestionAnswering,
								    LayoutLMvaForSequenceClassification,
								    LayoutLMvaForTokenClassification,
								    LayoutLMvaModel,
								    LayoutLMvaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlmva import (
								    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMvaForQuestionAnswering,
								    TFLayoutLMvaForSequenceClassification,
								    TFLayoutLMvaForTokenClassification,
								    TFLayoutLMvaModel,
								    TFLayoutLMvaPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
								from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
				import sys

				_snake_case : Any  				=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

