






import os
import sys
import unittest


a		:       Dict			     = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a		:       Tuple			     = os.path.join(git_repo_path, "src", "transformers")

a		:       Optional[Any]			     = "\n{0} = None\n"

a		:       Dict			     = "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


a		:       int			     = "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"
class     a    ( unittest.TestCase   ):



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Optional[Any]:
        __UpperCAmelCase							:	Dict           =  find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""					)
        self.assertIsNone(__lowercase					)

        __UpperCAmelCase							:	Any           =  find_backend("""    if not is_tokenizers_available():"""					)
        self.assertEqual(__lowercase		,      """tokenizers"""					)

        __UpperCAmelCase							:	List[Any]           =  find_backend("""    if not is_tensorflow_text_available():"""					)
        self.assertEqual(__lowercase		,      """tensorflow_text"""					)

        __UpperCAmelCase							:	Optional[Any]           =  find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""					)
        self.assertEqual(__lowercase		,      """sentencepiece_and_tokenizers"""					)

        __UpperCAmelCase							:	str           =  find_backend(
            """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""					)
        self.assertEqual(__lowercase		,      """sentencepiece_and_tensorflow_text"""					)

        __UpperCAmelCase							:	Dict           =  find_backend(
            """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""					)
        self.assertEqual(__lowercase		,      """sentencepiece_and_tokenizers_and_vision"""					)
      def 	UpperCAmelCase      (      self					:			int					)							->      List[Any]:
        __UpperCAmelCase							:	Tuple           =  read_init()
        # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
        self.assertIn("""torch"""		,      __lowercase					)
        self.assertIn("""tensorflow_text"""		,      __lowercase					)
        self.assertIn("""sentencepiece_and_tokenizers"""		,      __lowercase					)

        # Likewise, we can't assert on the exact content of a key
        self.assertIn("""BertModel"""		,      objects["""torch"""]					)
        self.assertIn("""TFBertModel"""		,      objects["""tf"""]					)
        self.assertIn("""FlaxBertModel"""		,      objects["""flax"""]					)
        self.assertIn("""BertModel"""		,      objects["""torch"""]					)
        self.assertIn("""TFBertTokenizer"""		,      objects["""tensorflow_text"""]					)
        self.assertIn("""convert_slow_tokenizer"""		,      objects["""sentencepiece_and_tokenizers"""]					)
      def 	UpperCAmelCase      (      self					:			str					)							->      str:
        __UpperCAmelCase							:	Dict           =  create_dummy_object("""CONSTANT"""		,      """'torch'"""					)
        self.assertEqual(__lowercase		,      """\nCONSTANT = None\n"""					)

        __UpperCAmelCase							:	Optional[int]           =  create_dummy_object("""function"""		,      """'torch'"""					)
        self.assertEqual(
            __lowercase		,      """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""					)

        __UpperCAmelCase							:	Tuple           =  """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
        __UpperCAmelCase							:	List[Any]           =  create_dummy_object("""FakeClass"""		,      """'torch'"""					)
        self.assertEqual(__lowercase		,      __lowercase					)




      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Dict:
        __UpperCAmelCase							:	Optional[int]           =  """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
        __UpperCAmelCase							:	Optional[int]           =  create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}					)
        self.assertEqual(dummy_files["""torch"""]		,      __lowercase					)










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    ( lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : int							       =	'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			Optional[Any]=0					)							->      Any:
        __UpperCAmelCase							:	Any           =  floats_tensor((1, 3, 128, 128)		,      rng=random.Random(__lowercase					)					)
        __UpperCAmelCase							:	int           =  np.random.RandomState(__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  {
            """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 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[Any]:
        __UpperCAmelCase							:	List[Any]           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	List[str]           =  np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]					)
        assert np.abs(image_slice - expected_slice					).max() < 1e-1
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      List[str]:
        __UpperCAmelCase							:	Dict           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	int           =  PNDMScheduler.from_config(pipe.scheduler.config		,      skip_prk_steps=__lowercase					)
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	str           =  np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1
      def 	UpperCAmelCase      (      self					:			str					)							->      Tuple:
        __UpperCAmelCase							:	Tuple           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	int           =  LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        # warmup pass to apply optimizations
        __UpperCAmelCase							:	Optional[int]           =  pipe(**self.get_dummy_inputs()					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	Optional[int]           =  np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	int           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	Union[str, Any]           =  EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	Tuple           =  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-1
      def 	UpperCAmelCase      (      self					:			int					)							->      Any:
        __UpperCAmelCase							:	Any           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	List[Any]           =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	List[str]           =  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]					)

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




      def 	UpperCAmelCase      (      self					:			Tuple					)							->      str:
        __UpperCAmelCase							:	Dict           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,      provider="""CPUExecutionProvider"""					)
        __UpperCAmelCase							:	str           =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
        pipe.set_progress_bar_config(disable=__lowercase					)

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

        assert image.shape == (1, 128, 128, 3)
        __UpperCAmelCase							:	Union[str, Any]           =  np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]					)

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


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



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[Any]:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )
      @property
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Tuple:
        __UpperCAmelCase							:	Optional[int]           =  ort.SessionOptions()
        __UpperCAmelCase							:	List[Any]           =  False
        return options
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Tuple:
        __UpperCAmelCase							:	List[Any]           =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""					)
        __UpperCAmelCase							:	Dict           =  init_image.resize((768, 512)					)
        # using the PNDM scheduler by default
        __UpperCAmelCase							:	Any           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            """CompVis/stable-diffusion-v1-4"""		,      revision="""onnx"""		,      safety_checker=__lowercase		,      feature_extractor=__lowercase		,      provider=self.gpu_provider		,      sess_options=self.gpu_options		,      )
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Dict           =  """A fantasy landscape, trending on artstation"""

        __UpperCAmelCase							:	str           =  np.random.RandomState(0					)
        __UpperCAmelCase							:	Optional[Any]           =  pipe(
            prompt=__lowercase		,      image=__lowercase		,      strength=0.75		,      guidance_scale=7.5		,      num_inference_steps=10		,      generator=__lowercase		,      output_type="""np"""		,      )
        __UpperCAmelCase							:	str           =  output.images
        __UpperCAmelCase							:	int           =  images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 768, 3)
        __UpperCAmelCase							:	Union[str, Any]           =  np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]					)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	Tuple           =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/img2img/sketch-mountains-input.jpg"""					)
        __UpperCAmelCase							:	int           =  init_image.resize((768, 512)					)
        __UpperCAmelCase							:	Tuple           =  LMSDiscreteScheduler.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""		,      subfolder="""scheduler"""		,      revision="""onnx"""					)
        __UpperCAmelCase							:	Tuple           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""		,      revision="""onnx"""		,      scheduler=__lowercase		,      safety_checker=__lowercase		,      feature_extractor=__lowercase		,      provider=self.gpu_provider		,      sess_options=self.gpu_options		,      )
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Dict           =  """A fantasy landscape, trending on artstation"""

        __UpperCAmelCase							:	int           =  np.random.RandomState(0					)
        __UpperCAmelCase							:	Optional[int]           =  pipe(
            prompt=__lowercase		,      image=__lowercase		,      strength=0.75		,      guidance_scale=7.5		,      num_inference_steps=20		,      generator=__lowercase		,      output_type="""np"""		,      )
        __UpperCAmelCase							:	Union[str, Any]           =  output.images
        __UpperCAmelCase							:	Union[str, Any]           =  images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 768, 3)
        __UpperCAmelCase							:	str           =  np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]					)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


