



"""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     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Optional[int]           =  '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	Dict=0				):



     """simple docstring"""
     _snake_case   	=      floats_tensor((1, 3, 1_2_8, 1_2_8)							,       rng=random.Random(__lowerCamelCase				)				)
     _snake_case   	=      np.random.RandomState(__lowerCamelCase				)
     _snake_case   	=      {
         '''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    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      PNDMScheduler.from_config(pipe.scheduler.config							,       skip_prk_steps=__lowerCamelCase				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

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

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      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     UpperCAmelCase    (	unittest.TestCase  ):



   @property
   def    __UpperCAmelCase    (       self    :	Optional[int]				):



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



   @property
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      ort.SessionOptions()
     _snake_case   	=      False
     return options



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''				)
     _snake_case   	=      init_image.resize((7_6_8, 5_1_2)				)
     # using the PNDM scheduler by default
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4'''							,       revision='''onnx'''							,       safety_checker=__lowerCamelCase							,       feature_extractor=__lowerCamelCase							,       provider=self.gpu_provider							,       sess_options=self.gpu_options							,       )
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

     _snake_case   	=      np.random.RandomState(0				)
     _snake_case   	=      pipe(
         prompt=__lowerCamelCase							,       image=__lowerCamelCase							,       strength=0.7_5							,       guidance_scale=7.5							,       num_inference_steps=1_0							,       generator=__lowerCamelCase							,       output_type='''np'''							,       )
     _snake_case   	=      output.images
     _snake_case   	=      images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert images.shape == (1, 5_1_2, 7_6_8, 3)
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''				)
     _snake_case   	=      init_image.resize((7_6_8, 5_1_2)				)
     _snake_case   	=      LMSDiscreteScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''							,       subfolder='''scheduler'''							,       revision='''onnx'''				)
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''							,       revision='''onnx'''							,       scheduler=__lowerCamelCase							,       safety_checker=__lowerCamelCase							,       feature_extractor=__lowerCamelCase							,       provider=self.gpu_provider							,       sess_options=self.gpu_options							,       )
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

     _snake_case   	=      np.random.RandomState(0				)
     _snake_case   	=      pipe(
         prompt=__lowerCamelCase							,       image=__lowerCamelCase							,       strength=0.7_5							,       guidance_scale=7.5							,       num_inference_steps=2_0							,       generator=__lowerCamelCase							,       output_type='''np'''							,       )
     _snake_case   	=      output.images
     _snake_case   	=      images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert images.shape == (1, 5_1_2, 7_6_8, 3)
     _snake_case   	=      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"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


snake_case   					=				{'''tokenization_herbert''': ['''HerbertTokenizer''']}

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


if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_herbert_fast import HerbertTokenizerFast

else:
     import sys

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





