





'''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   _lowercase						(							a		,					unittest.TestCase				):
   _UpperCamelCase       		=   """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""


   def 					snake_case				( self    ,   _UpperCAmelCase=0					):
       A       :       List[str]							=						floats_tensor((1, 3, 128, 128)    ,   rng=random.Random(_UpperCAmelCase					)					)
       A       :       Optional[int]							=						np.random.RandomState(_UpperCAmelCase					)
       A       :       Optional[int]							=						{
           '''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					):
       A       :       int							=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider'''					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

       assert image.shape == (1, 128, 128, 3)
       A       :       Optional[Any]							=						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 					snake_case				( self					):
       A       :       Optional[int]							=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider'''					)
       A       :       Tuple							=						PNDMScheduler.from_config(pipe.scheduler.config    ,   skip_prk_steps=_UpperCAmelCase					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

       assert image.shape == (1, 128, 128, 3)
       A       :       List[str]							=						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 					snake_case				( self					):
       A       :       Optional[Any]							=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider'''					)
       A       :       Tuple							=						LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

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

       assert image.shape == (1, 128, 128, 3)
       A       :       int							=						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 					snake_case				( self					):
       A       :       Any							=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider'''					)
       A       :       Union[str, Any]							=						EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

       assert image.shape == (1, 128, 128, 3)
       A       :       Optional[Any]							=						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 					snake_case				( self					):
       A       :       Optional[int]							=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider'''					)
       A       :       Optional[Any]							=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       Tuple							=						self.get_dummy_inputs()
       A       :       str							=						pipe(**_UpperCAmelCase					).images
       A       :       List[str]							=						image[0, -3:, -3:, -1]

       assert image.shape == (1, 128, 128, 3)
       A       :       Optional[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 					snake_case				( self					):
       A       :       Optional[int]							=						OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='''CPUExecutionProvider'''					)
       A       :       Tuple							=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

       assert image.shape == (1, 128, 128, 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   _lowercase						(							unittest.TestCase				):


   @property
   def 					snake_case				( self					):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )


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


   def 					snake_case				( self					):
       A       :       str							=						load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''					)
       A       :       Tuple							=						init_image.resize((768, 512)					)
       # using the PNDM scheduler by default
       A       :       List[Any]							=						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					)

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

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

       assert images.shape == (1, 512, 768, 3)
       A       :       Optional[Any]							=						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 					snake_case				( self					):
       A       :       Tuple							=						load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''					)
       A       :       Any							=						init_image.resize((768, 512)					)
       A       :       Optional[Any]							=						LMSDiscreteScheduler.from_pretrained(
           '''runwayml/stable-diffusion-v1-5'''    ,   subfolder='''scheduler'''    ,   revision='''onnx'''					)
       A       :       Optional[int]							=						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					)

       A       :       Dict							=						'''A fantasy landscape, trending on artstation'''

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

       assert images.shape == (1, 512, 768, 3)
       A       :       int							=						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







'''simple docstring'''






from typing import TYPE_CHECKING

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


snake_case_     =	{
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	["""SpeechT5Tokenizer"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SpeechT5ForSpeechToText""",
         """SpeechT5ForSpeechToSpeech""",
         """SpeechT5ForTextToSpeech""",
         """SpeechT5Model""",
         """SpeechT5PreTrainedModel""",
         """SpeechT5HifiGan""",
     ]

if TYPE_CHECKING:
     from .configuration_speechta import (
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechTaConfig,
         SpeechTaHifiGanConfig,
     )
     from .feature_extraction_speechta import SpeechTaFeatureExtractor
     from .processing_speechta import SpeechTaProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_speechta import SpeechTaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speechta import (
              SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
              SpeechTaForSpeechToSpeech,
              SpeechTaForSpeechToText,
              SpeechTaForTextToSpeech,
              SpeechTaHifiGan,
              SpeechTaModel,
              SpeechTaPreTrainedModel,
          )

else:
     import sys

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