

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 						__magic_name__			(							lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	str    =   'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'



   def  _UpperCAmelCase (				self,    lowercase_=0       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =floats_tensor((1, 3, 128, 128),    rng=random.Random(lowercase_       )       )
     a__  =np.random.RandomState(lowercase_       )
     a__  ={
         '''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]:






     """simple docstring"""






     a__  =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='''CPUExecutionProvider'''       )
     pipe.set_progress_bar_config(disable=lowercase_       )

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

     assert image.shape == (1, 128, 128, 3)
     a__  =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  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






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

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

     assert image.shape == (1, 128, 128, 3)
     a__  =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  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






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

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

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

     assert image.shape == (1, 128, 128, 3)
     a__  =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  _UpperCAmelCase (				self       )							->    Union[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=lowercase_       )

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

     assert image.shape == (1, 128, 128, 3)
     a__  =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  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






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

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

     assert image.shape == (1, 128, 128, 3)
     a__  =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  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






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

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

     assert image.shape == (1, 128, 128, 3)
     a__  =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 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   @property
   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






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



   @property
   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    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((768, 512)       )
     # using the PNDM scheduler by default
     a__  =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_       )

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

     a__  =np.random.RandomState(0       )
     a__  =pipe(
         prompt=lowercase_,    image=lowercase_,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=10,    generator=lowercase_,    output_type='''np''',    )
     a__  =output.images
     a__  =images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 768, 3)
     a__  =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  _UpperCAmelCase (				self       )							->    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((768, 512)       )
     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=lowercase_,    safety_checker=lowercase_,    feature_extractor=lowercase_,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
     pipe.set_progress_bar_config(disable=lowercase_       )

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

     a__  =np.random.RandomState(0       )
     a__  =pipe(
         prompt=lowercase_,    image=lowercase_,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=20,    generator=lowercase_,    output_type='''np''',    )
     a__  =output.images
     a__  =images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 768, 3)
     a__  =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



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''XLNetTokenizer''']

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''XLNetForMultipleChoice''',
           '''XLNetForQuestionAnswering''',
           '''XLNetForQuestionAnsweringSimple''',
           '''XLNetForSequenceClassification''',
           '''XLNetForTokenClassification''',
           '''XLNetLMHeadModel''',
           '''XLNetModel''',
           '''XLNetPreTrainedModel''',
           '''load_tf_weights_in_xlnet''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFXLNetForMultipleChoice''',
           '''TFXLNetForQuestionAnsweringSimple''',
           '''TFXLNetForSequenceClassification''',
           '''TFXLNetForTokenClassification''',
           '''TFXLNetLMHeadModel''',
           '''TFXLNetMainLayer''',
           '''TFXLNetModel''',
           '''TFXLNetPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet import XLNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xlnet_fast import XLNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xlnet import (
                  XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLNetForMultipleChoice,
                  XLNetForQuestionAnswering,
                  XLNetForQuestionAnsweringSimple,
                  XLNetForSequenceClassification,
                  XLNetForTokenClassification,
                  XLNetLMHeadModel,
                  XLNetModel,
                  XLNetPreTrainedModel,
                  load_tf_weights_in_xlnet,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xlnet import (
                  TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXLNetForMultipleChoice,
                  TFXLNetForQuestionAnsweringSimple,
                  TFXLNetForSequenceClassification,
                  TFXLNetForTokenClassification,
                  TFXLNetLMHeadModel,
                  TFXLNetMainLayer,
                  TFXLNetModel,
                  TFXLNetPreTrainedModel,
              )

else:
       import sys

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