


'''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					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		int    							=						'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''

 def  snake_case__   (      self      ,      __lowercase=0						):

  """simple docstring"""







  __A  :		Tuple       =			floats_tensor((1, 3, 128, 128)      ,      rng=random.Random(__lowercase						)						)
  __A  :		Optional[int]       =			np.random.RandomState(__lowercase						)
  __A  :		str       =			{
      'prompt': 'A painting of a squirrel eating a burger',
      'image': image,
      'generator': generator,
      'num_inference_steps': 3,
      'strength': 0.7_5,
      'guidance_scale': 7.5,
      'output_type': 'numpy',
  }
  return inputs

 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  pipe.set_progress_bar_config(disable=__lowercase						)

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

  assert image.shape == (1, 128, 128, 3)
  __A  :		str       =			np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]						)
  assert np.abs(image_slice - expected_slice						).max() < 1E-1

 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		Dict       =			PNDMScheduler.from_config(pipe.scheduler.config      ,      skip_prk_steps=__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

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

  assert image.shape == (1, 128, 128, 3)
  __A  :		Union[str, Any]       =			np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]						)

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

 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		Tuple       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
  pipe.set_progress_bar_config(disable=__lowercase						)

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

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

  assert image.shape == (1, 128, 128, 3)
  __A  :		Any       =			np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]						)

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

 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		List[Any]       =			EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
  pipe.set_progress_bar_config(disable=__lowercase						)

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

  assert image.shape == (1, 128, 128, 3)
  __A  :		List[Any]       =			np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]						)

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

 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		Optional[int]       =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
  pipe.set_progress_bar_config(disable=__lowercase						)

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

  assert image.shape == (1, 128, 128, 3)
  __A  :		Optional[int]       =			np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]						)

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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,      provider='CPUExecutionProvider'						)
  __A  :		List[str]       =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
  pipe.set_progress_bar_config(disable=__lowercase						)

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

  assert image.shape == (1, 128, 128, 3)
  __A  :		Any       =			np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]						)

  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"""







  __A  :		str       =			ort.SessionOptions()
  __A  :		Tuple       =			False
  return options

 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			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)						)
  # using the PNDM scheduler by default
  __A  :		Union[str, 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						)

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

  __A  :		str       =			np.random.RandomState(0						)
  __A  :		Union[str, Any]       =			pipe(
      prompt=__lowercase      ,      image=__lowercase      ,      strength=0.7_5      ,      guidance_scale=7.5      ,      num_inference_steps=10      ,      generator=__lowercase      ,      output_type='np'      ,      )
  __A  :		List[str]       =			output.images
  __A  :		Union[str, Any]       =			images[0, 255:258, 383:386, -1]

  assert images.shape == (1, 512, 768, 3)
  __A  :		int       =			np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]						)
  # 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"""







  __A  :		Optional[Any]       =			load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/img2img/sketch-mountains-input.jpg'						)
  __A  :		str       =			init_image.resize((768, 512)						)
  __A  :		Tuple       =			LMSDiscreteScheduler.from_pretrained(
      'runwayml/stable-diffusion-v1-5'      ,      subfolder='scheduler'      ,      revision='onnx'						)
  __A  :		List[str]       =			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  :		Tuple       =			'A fantasy landscape, trending on artstation'

  __A  :		Union[str, Any]       =			np.random.RandomState(0						)
  __A  :		List[str]       =			pipe(
      prompt=__lowercase      ,      image=__lowercase      ,      strength=0.7_5      ,      guidance_scale=7.5      ,      num_inference_steps=20      ,      generator=__lowercase      ,      output_type='np'      ,      )
  __A  :		Optional[int]       =			output.images
  __A  :		Dict       =			images[0, 255:258, 383:386, -1]

  assert images.shape == (1, 512, 768, 3)
  __A  :		Tuple       =			np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]						)
  # 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'''
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow

@is_pipeline_test
@require_torch
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			pipeline(
      task='zero-shot-audio-classification'      ,      model='hf-internal-testing/tiny-clap-htsat-unfused'						)
  __A  :		str       =			load_dataset('ashraq/esc50'						)
  __A  :		Union[str, Any]       =			dataset['train']['audio'][-1]['array']
  __A  :		List[str]       =			audio_classifier(__lowercase      ,      candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']						)
  self.assertEqual(
      nested_simplify(__lowercase						)      ,      [{'score': 0.5_0_1, 'label': 'Sound of a dog'}, {'score': 0.4_9_9, 'label': 'Sound of vaccum cleaner'}]      ,      )


 @unittest.skip('No models are available in TF'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 @slow
 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			pipeline(
      task='zero-shot-audio-classification'      ,      model='laion/clap-htsat-unfused'      ,      )
  # This is an audio of a dog
  __A  :		Optional[Any]       =			load_dataset('ashraq/esc50'						)
  __A  :		List[str]       =			dataset['train']['audio'][-1]['array']
  __A  :		Union[str, Any]       =			audio_classifier(__lowercase      ,      candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']						)

  self.assertEqual(
      nested_simplify(__lowercase						)      ,      [
          {'score': 0.9_9_9, 'label': 'Sound of a dog'},
          {'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
      ]      ,      )

  __A  :		Any       =			audio_classifier([audio] * 5      ,      candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']						)
  self.assertEqual(
      nested_simplify(__lowercase						)      ,      [
          [
              {'score': 0.9_9_9, 'label': 'Sound of a dog'},
              {'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
          ],
      ]
      * 5      ,      )
  __A  :		str       =			audio_classifier(
      [audio] * 5      ,      candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']      ,      batch_size=5						)
  self.assertEqual(
      nested_simplify(__lowercase						)      ,      [
          [
              {'score': 0.9_9_9, 'label': 'Sound of a dog'},
              {'score': 0.0_0_1, 'label': 'Sound of vaccum cleaner'},
          ],
      ]
      * 5      ,      )







 @unittest.skip('No models are available in TF'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass






