
import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort





class    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  Dict=0 ):
												lowerCamelCase_					=       np.random.RandomState(__UpperCamelCase )
												lowerCamelCase_					=       {
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """generator""": generator,
												    """num_inference_steps""": 2,
												    """guidance_scale""": 7.5,
												    """output_type""": """numpy""",
												}
												return inputs

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 1_2_8, 1_2_8, 3)
												lowerCamelCase_					=       np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       PNDMScheduler.from_config(pipe.scheduler.config ,       skip_prk_steps=__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 1_2_8, 1_2_8, 3)
												lowerCamelCase_					=       np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       LMSDiscreteScheduler.from_config(pipe.scheduler.config )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 1_2_8, 1_2_8, 3)
												lowerCamelCase_					=       np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       EulerDiscreteScheduler.from_config(pipe.scheduler.config )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 1_2_8, 1_2_8, 3)
												lowerCamelCase_					=       np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 1_2_8, 1_2_8, 3)
												lowerCamelCase_					=       np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 1_2_8, 1_2_8, 3)
												lowerCamelCase_					=       np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs()
												lowerCamelCase_					=       3 * [inputs["""prompt"""]]

												# forward
												lowerCamelCase_					=       pipe(**__UpperCamelCase )
												lowerCamelCase_					=       output.images[0, -3:, -3:, -1]

												lowerCamelCase_					=       self.get_dummy_inputs()
												lowerCamelCase_					=       3 * [inputs.pop("""prompt""" )]

												lowerCamelCase_					=       pipe.tokenizer(
												    __UpperCamelCase ,       padding="""max_length""" ,       max_length=pipe.tokenizer.model_max_length ,       truncation=__UpperCamelCase ,       return_tensors="""np""" ,       )
												lowerCamelCase_					=       text_inputs["""input_ids"""]

												lowerCamelCase_					=       pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]

												lowerCamelCase_					=       prompt_embeds

												# forward
												lowerCamelCase_					=       pipe(**__UpperCamelCase )
												lowerCamelCase_					=       output.images[0, -3:, -3:, -1]

												assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4



					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs()
												lowerCamelCase_					=       3 * ["""this is a negative prompt"""]
												lowerCamelCase_					=       negative_prompt
												lowerCamelCase_					=       3 * [inputs["""prompt"""]]

												# forward
												lowerCamelCase_					=       pipe(**__UpperCamelCase )
												lowerCamelCase_					=       output.images[0, -3:, -3:, -1]

												lowerCamelCase_					=       self.get_dummy_inputs()
												lowerCamelCase_					=       3 * [inputs.pop("""prompt""" )]

												lowerCamelCase_					=       []
												for p in [prompt, negative_prompt]:
																			lowerCamelCase_					=       pipe.tokenizer(
																			    __UpperCamelCase ,       padding="""max_length""" ,       max_length=pipe.tokenizer.model_max_length ,       truncation=__UpperCamelCase ,       return_tensors="""np""" ,       )
																			lowerCamelCase_					=       text_inputs["""input_ids"""]

																			embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )

												lowerCamelCase_	, lowerCamelCase_					=       embeds

												# forward
												lowerCamelCase_					=       pipe(**__UpperCamelCase )
												lowerCamelCase_					=       output.images[0, -3:, -3:, -1]

												assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4





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

					@property
					def  lowercase__   (						self       :  Union[str, Any] ):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)

					@property
					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       ort.SessionOptions()
												lowerCamelCase_					=       False
												return options

					def  lowercase__   (						self       :  List[Any] ):
												# using the PNDM scheduler by default
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(
												    """CompVis/stable-diffusion-v1-4""" ,       revision="""onnx""" ,       safety_checker=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       provider=self.gpu_provider ,       sess_options=self.gpu_options ,       )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       """A painting of a squirrel eating a burger"""
												np.random.seed(0 )
												lowerCamelCase_					=       sd_pipe([prompt] ,       guidance_scale=6.0 ,       num_inference_steps=1_0 ,       output_type="""np""" )
												lowerCamelCase_					=       output.images

												lowerCamelCase_					=       image[0, -3:, -3:, -1]

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       DDIMScheduler.from_pretrained(
												    """runwayml/stable-diffusion-v1-5""" ,       subfolder="""scheduler""" ,       revision="""onnx""" )
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.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 ,       )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       """open neural network exchange"""
												lowerCamelCase_					=       np.random.RandomState(0 )
												lowerCamelCase_					=       sd_pipe([prompt] ,       guidance_scale=7.5 ,       num_inference_steps=1_0 ,       generator=__UpperCamelCase ,       output_type="""np""" )
												lowerCamelCase_					=       output.images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       LMSDiscreteScheduler.from_pretrained(
												    """runwayml/stable-diffusion-v1-5""" ,       subfolder="""scheduler""" ,       revision="""onnx""" )
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.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 ,       )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       """open neural network exchange"""
												lowerCamelCase_					=       np.random.RandomState(0 )
												lowerCamelCase_					=       sd_pipe([prompt] ,       guidance_scale=7.5 ,       num_inference_steps=1_0 ,       generator=__UpperCamelCase ,       output_type="""np""" )
												lowerCamelCase_					=       output.images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       0

												def test_callback_fn(__UpperCamelCase       :  int ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  np.ndarray ) -> None:
																			lowerCamelCase_					=       True
																			nonlocal number_of_steps
																			number_of_steps += 1
																			if step == 0:
																										assert latents.shape == (1, 4, 6_4, 6_4)
																										lowerCamelCase_					=       latents[0, -3:, -3:, -1]
																										lowerCamelCase_					=       np.array(
																										    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167] )

																										assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
																			elif step == 5:
																										assert latents.shape == (1, 4, 6_4, 6_4)
																										lowerCamelCase_					=       latents[0, -3:, -3:, -1]
																										lowerCamelCase_					=       np.array(
																										    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875] )

																										assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3

												lowerCamelCase_					=       False

												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(
												    """runwayml/stable-diffusion-v1-5""" ,       revision="""onnx""" ,       safety_checker=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       provider=self.gpu_provider ,       sess_options=self.gpu_options ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       """Andromeda galaxy in a bottle"""

												lowerCamelCase_					=       np.random.RandomState(0 )
												pipe(
												    prompt=__UpperCamelCase ,       num_inference_steps=5 ,       guidance_scale=7.5 ,       generator=__UpperCamelCase ,       callback=__UpperCamelCase ,       callback_steps=1 ,       )
												assert test_callback_fn.has_been_called
												assert number_of_steps == 6



					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(
												    """runwayml/stable-diffusion-v1-5""" ,       revision="""onnx""" ,       safety_checker=__UpperCamelCase ,       feature_extractor=__UpperCamelCase ,       provider=self.gpu_provider ,       sess_options=self.gpu_options ,       )
												assert isinstance(__UpperCamelCase ,       __UpperCamelCase )
												assert pipe.safety_checker is None

												lowerCamelCase_					=       pipe("""example prompt""" ,       num_inference_steps=2 ).images[0]
												assert image is not None

												# check that there's no error when saving a pipeline with one of the models being None
												with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(__UpperCamelCase )
																			lowerCamelCase_					=       OnnxStableDiffusionPipeline.from_pretrained(__UpperCamelCase )

												# sanity check that the pipeline still works
												assert pipe.safety_checker is None
												lowerCamelCase_					=       pipe("""example prompt""" ,       num_inference_steps=2 ).images[0]
												assert image is not None







from typing import TYPE_CHECKING

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


lowercase   =	{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2TokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''LayoutLMv2FeatureExtractor''']
		lowercase   =	['''LayoutLMv2ImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv2ForQuestionAnswering''',
		    '''LayoutLMv2ForSequenceClassification''',
		    '''LayoutLMv2ForTokenClassification''',
		    '''LayoutLMv2Layer''',
		    '''LayoutLMv2Model''',
		    '''LayoutLMv2PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
		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_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaLayer,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)
else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





