





"""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    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""






       def 	snake_case		(       self       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				floats_tensor((1, 3, 1_2_8, 1_2_8)       ,					rng=random.Random(__UpperCAmelCase ) )
         lowerCAmelCase__     :List[str]											=				np.random.RandomState(__UpperCAmelCase )
         lowerCAmelCase__     :List[str]											=				{
             '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 ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Union[str, 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 ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :str											=				PNDMScheduler.from_config(pipe.scheduler.config       ,					skip_prk_steps=__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs()
         lowerCAmelCase__     :Optional[Any]											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Any											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :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 ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :List[str]											=				LMSDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         # warmup pass to apply optimizations
         lowerCAmelCase__     :List[Any]											=				pipe(**self.get_dummy_inputs() )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs()
         lowerCAmelCase__     :int											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Any											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Union[str, 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 ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :Dict											=				EulerDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Optional[int]											=				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 ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :str											=				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs()
         lowerCAmelCase__     :Any											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Optional[int]											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :int											=				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 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :List[Any]											=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Optional[Any]											=				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    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

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






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				ort.SessionOptions()
         lowerCAmelCase__     :Optional[int]											=				False
         return options






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg' )
         lowerCAmelCase__     :Any											=				init_image.resize((7_6_8, 5_1_2) )
         # using the PNDM scheduler by default
         lowerCAmelCase__     :Optional[int]											=				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 )

         lowerCAmelCase__     :List[Any]											=				'A fantasy landscape, trending on artstation'

         lowerCAmelCase__     :Optional[Any]											=				np.random.RandomState(0 )
         lowerCAmelCase__     :List[str]											=				pipe(
             prompt=__UpperCAmelCase       ,					image=__UpperCAmelCase       ,					strength=0.75       ,					guidance_scale=7.5       ,					num_inference_steps=1_0       ,					generator=__UpperCAmelCase       ,					output_type='np'       ,					)
         lowerCAmelCase__     :Any											=				output.images
         lowerCAmelCase__     :List[str]											=				images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         lowerCAmelCase__     :List[Any]											=				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 ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg' )
         lowerCAmelCase__     :Optional[Any]											=				init_image.resize((7_6_8, 5_1_2) )
         lowerCAmelCase__     :List[Any]											=				LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-v1-5'       ,					subfolder='scheduler'       ,					revision='onnx' )
         lowerCAmelCase__     :Optional[Any]											=				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 )

         lowerCAmelCase__     :List[Any]											=				'A fantasy landscape, trending on artstation'

         lowerCAmelCase__     :List[Any]											=				np.random.RandomState(0 )
         lowerCAmelCase__     :List[Any]											=				pipe(
             prompt=__UpperCAmelCase       ,					image=__UpperCAmelCase       ,					strength=0.75       ,					guidance_scale=7.5       ,					num_inference_steps=2_0       ,					generator=__UpperCAmelCase       ,					output_type='np'       ,					)
         lowerCAmelCase__     :Optional[Any]											=				output.images
         lowerCAmelCase__     :int											=				images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         lowerCAmelCase__     :List[Any]											=				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





from typing import TYPE_CHECKING

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


lowerCamelCase		       =      {
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      ["""BlipImageProcessor"""]


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """BlipModel""",
						    """BlipPreTrainedModel""",
						    """BlipForConditionalGeneration""",
						    """BlipForQuestionAnswering""",
						    """BlipVisionModel""",
						    """BlipTextModel""",
						    """BlipForImageTextRetrieval""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFBlipModel""",
						    """TFBlipPreTrainedModel""",
						    """TFBlipForConditionalGeneration""",
						    """TFBlipForQuestionAnswering""",
						    """TFBlipVisionModel""",
						    """TFBlipTextModel""",
						    """TFBlipForImageTextRetrieval""",
						]

if TYPE_CHECKING:
						from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
						from .processing_blip import BlipProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_blip import BlipImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blip import (
												    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlipForConditionalGeneration,
												    BlipForImageTextRetrieval,
												    BlipForQuestionAnswering,
												    BlipModel,
												    BlipPreTrainedModel,
												    BlipTextModel,
												    BlipVisionModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blip import (
												    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFBlipForConditionalGeneration,
												    TFBlipForImageTextRetrieval,
												    TFBlipForQuestionAnswering,
												    TFBlipModel,
												    TFBlipPreTrainedModel,
												    TFBlipTextModel,
												    TFBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


