



'''simple docstring'''






from typing import TYPE_CHECKING

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


lowerCAmelCase:      int		 =		{
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:      Any		 =		["""CLIPTokenizerFast"""]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:      List[Any]		 =		["""CLIPFeatureExtractor"""]
     lowerCAmelCase:      Optional[int]		 =		["""CLIPImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:      List[Any]		 =		[
         """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """CLIPModel""",
         """CLIPPreTrainedModel""",
         """CLIPTextModel""",
         """CLIPTextModelWithProjection""",
         """CLIPVisionModel""",
         """CLIPVisionModelWithProjection""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:      List[Any]		 =		[
         """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFCLIPModel""",
         """TFCLIPPreTrainedModel""",
         """TFCLIPTextModel""",
         """TFCLIPVisionModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:      Dict		 =		[
         """FlaxCLIPModel""",
         """FlaxCLIPPreTrainedModel""",
         """FlaxCLIPTextModel""",
         """FlaxCLIPTextPreTrainedModel""",
         """FlaxCLIPVisionModel""",
         """FlaxCLIPVisionPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_clip import (
         CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPConfig,
         CLIPOnnxConfig,
         CLIPTextConfig,
         CLIPVisionConfig,
     )
     from .processing_clip import CLIPProcessor
     from .tokenization_clip import CLIPTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_clip_fast import CLIPTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_clip import CLIPFeatureExtractor
          from .image_processing_clip import CLIPImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clip import (
              CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPModel,
              CLIPPreTrainedModel,
              CLIPTextModel,
              CLIPTextModelWithProjection,
              CLIPVisionModel,
              CLIPVisionModelWithProjection,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_clip import (
              TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCLIPModel,
              TFCLIPPreTrainedModel,
              TFCLIPTextModel,
              TFCLIPVisionModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_clip import (
              FlaxCLIPModel,
              FlaxCLIPPreTrainedModel,
              FlaxCLIPTextModel,
              FlaxCLIPTextPreTrainedModel,
              FlaxCLIPVisionModel,
              FlaxCLIPVisionPreTrainedModel,
          )


else:
     import sys

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




'''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__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Union[str, Any]=0			):
      a		:      List[str]       =  floats_tensor((1, 3, 1_28, 1_28)		,    rng=random.Random(__snake_case			)			)
      a		:      Optional[Any]       =  np.random.RandomState(__snake_case			)
      a		:      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     lowercase_ (   self				:						Union[str, Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]			)
      assert np.abs(image_slice - expected_slice			).max() < 1e-1







    def     lowercase_ (   self				:						str			):
      a		:      List[Any]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Dict       =  PNDMScheduler.from_config(pipe.scheduler.config		,    skip_prk_steps=__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]			)

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







    def     lowercase_ (   self				:						List[str]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      List[Any]       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Any       =  np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]			)

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







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Any       =  EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[str]       =  self.get_dummy_inputs()
      a		:      Optional[int]       =  pipe(**__snake_case			).images
      a		:      Any       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Tuple			):
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[Any]       =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Dict       =  self.get_dummy_inputs()
      a		:      Union[str, Any]       =  pipe(**__snake_case			).images
      a		:      int       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[int]       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Optional[int]       =  np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]			)

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

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







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







    @property
    def     lowercase_ (   self				:						Dict			):
      a		:      Tuple       =  ort.SessionOptions()
      a		:      Optional[Any]       =  False
      return options







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

      a		:      Union[str, Any]       =  'A fantasy landscape, trending on artstation'

      a		:      Dict       =  np.random.RandomState(0			)
      a		:      Optional[int]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=10		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      str       =  output.images
      a		:      Optional[int]       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      List[str]       =  np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]			)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







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

      a		:      Optional[int]       =  'A fantasy landscape, trending on artstation'

      a		:      str       =  np.random.RandomState(0			)
      a		:      List[str]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=20		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      str       =  output.images
      a		:      Tuple       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      Union[str, Any]       =  np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]			)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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