
'''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       A				(							_a		,    unittest.TestCase					):
			lowerCamelCase				:				Tuple									=	"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



			def A__			(       self							,  lowerCamelCase__=0							)      ->		Any:




									'''simple docstring'''



									lowercase__							=							floats_tensor((1, 3, 128, 128)							,  rng=random.Random(A_							)							)
									lowercase__							=							np.random.RandomState(A_							)
									lowercase__							=							{
									    """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 A__			(       self							)      ->		List[str]:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									pipe.set_progress_bar_config(disable=A_							)

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

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]							)
									assert np.abs(image_slice - expected_slice							).max() < 1e-1



			def A__			(       self							)      ->		Any:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							PNDMScheduler.from_config(pipe.scheduler.config							,  skip_prk_steps=A_							)
									pipe.set_progress_bar_config(disable=A_							)

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

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]							)

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



			def A__			(       self							)      ->		List[Any]:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
									pipe.set_progress_bar_config(disable=A_							)

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

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

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]							)

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



			def A__			(       self							)      ->		Tuple:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
									pipe.set_progress_bar_config(disable=A_							)

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

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]							)

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



			def A__			(       self							)      ->		List[Any]:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
									pipe.set_progress_bar_config(disable=A_							)

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

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]							)

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



			def A__			(       self							)      ->		Tuple:




									'''simple docstring'''



									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,  provider="""CPUExecutionProvider"""							)
									lowercase__							=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
									pipe.set_progress_bar_config(disable=A_							)

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

									assert image.shape == (1, 128, 128, 3)
									lowercase__							=							np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]							)

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





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



			@property
			def A__			(       self							)      ->		Union[str, Any]:




									'''simple docstring'''



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



			@property
			def A__			(       self							)      ->		Tuple:




									'''simple docstring'''



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



			def A__			(       self							)      ->		Tuple:




									'''simple docstring'''



									lowercase__							=							load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/img2img/sketch-mountains-input.jpg"""							)
									lowercase__							=							init_image.resize((768, 512)							)
									# using the PNDM scheduler by default
									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""							,  revision="""onnx"""							,  safety_checker=A_							,  feature_extractor=A_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									pipe.set_progress_bar_config(disable=A_							)

									lowercase__							=							"""A fantasy landscape, trending on artstation"""

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

									assert images.shape == (1, 512, 768, 3)
									lowercase__							=							np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]							)
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



			def A__			(       self							)      ->		Tuple:




									'''simple docstring'''



									lowercase__							=							load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/img2img/sketch-mountains-input.jpg"""							)
									lowercase__							=							init_image.resize((768, 512)							)
									lowercase__							=							LMSDiscreteScheduler.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""							,  subfolder="""scheduler"""							,  revision="""onnx"""							)
									lowercase__							=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""							,  revision="""onnx"""							,  scheduler=A_							,  safety_checker=A_							,  feature_extractor=A_							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
									pipe.set_progress_bar_config(disable=A_							)

									lowercase__							=							"""A fantasy landscape, trending on artstation"""

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

									assert images.shape == (1, 512, 768, 3)
									lowercase__							=							np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]							)
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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










import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__snake_case   							=						'''src/diffusers'''

# Matches is_xxx_available()
__snake_case   							=						re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
__snake_case   							=						re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')


__snake_case   							=						'''
{0} = None
'''

__snake_case   							=						'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
'''


__snake_case   							=						'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			_re_backend.findall(_lowercase )
     if len(_lowercase ) == 0:
          return None

     return "_and_".join(_lowercase )






def 		_A		(		)					-> Tuple:


     """simple docstring"""


     with open(os.path.join(_lowercase					,  '__init__.py' )					,  'r'					,  encoding='utf-8'					,  newline='\n' ) as f:
          __UpperCamelCase =			f.readlines()

     # Get to the point we do the actual imports for type checking
     __UpperCamelCase =			0
     __UpperCamelCase =			{}
     # Go through the end of the file
     while line_index < len(_lowercase ):
          # If the line contains is_backend_available, we grab all objects associated with the `else` block
          __UpperCamelCase =			find_backend(lines[line_index] )
          if backend is not None:
               while not lines[line_index].startswith('else:' ):
                    line_index += 1
               line_index += 1
               __UpperCamelCase =			[]
               # Until we unindent, add backend objects to the list
               while line_index < len(_lowercase ) and len(lines[line_index] ) > 1:
                    __UpperCamelCase =			lines[line_index]
                    __UpperCamelCase =			_re_single_line_import.search(_lowercase )
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(', ' ) )
                    elif line.startswith(' ' * 8 ):
                         objects.append(line[8:-2] )
                    line_index += 1

               if len(_lowercase ) > 0:
                    __UpperCamelCase =			objects
          else:
               line_index += 1

     return backend_specific_objects






def 		_A		(		_lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


     if name.isupper():
          return DUMMY_CONSTANT.format(_lowercase )
     elif name.islower():
          return DUMMY_FUNCTION.format(_lowercase					,  _lowercase )
     else:
          return DUMMY_CLASS.format(_lowercase					,  _lowercase )






def 		_A		(		_lowercase=None )					-> Optional[Any]:


     """simple docstring"""


     if backend_specific_objects is None:
          __UpperCamelCase =			read_init()
     # For special correspondence backend to module name as used in the function requires_modulename
     __UpperCamelCase =			{}

     for backend, objects in backend_specific_objects.items():
          __UpperCamelCase =			'[' + ', '.join(f'''"{b}"''' for b in backend.split('_and_' ) ) + ']'
          __UpperCamelCase =			'# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
          dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
          dummy_file += "\n".join([create_dummy_object(_lowercase					,  _lowercase ) for o in objects] )
          __UpperCamelCase =			dummy_file

     return dummy_files






def 		_A		(		_lowercase=False )					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			create_dummy_files()
     # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
     __UpperCamelCase =			{'torch': 'pt'}

     # Locate actual dummy modules and read their content.
     __UpperCamelCase =			os.path.join(_lowercase					,  'utils' )
     __UpperCamelCase =			{
         backend: os.path.join(_lowercase					,  f'''dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py''' )
         for backend in dummy_files.keys()
     }

     __UpperCamelCase =			{}
     for backend, file_path in dummy_file_paths.items():
          if os.path.isfile(_lowercase ):
               with open(_lowercase					,  'r'					,  encoding='utf-8'					,  newline='\n' ) as f:
                    __UpperCamelCase =			f.read()
          else:
               __UpperCamelCase =			''

     for backend in dummy_files.keys():
          if dummy_files[backend] != actual_dummies[backend]:
               if overwrite:
                    print(
                        f'''Updating diffusers.utils.dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py as the main '''
                        '__init__ has new objects.' )
                    with open(dummy_file_paths[backend]					,  'w'					,  encoding='utf-8'					,  newline='\n' ) as f:
                         f.write(dummy_files[backend] )
               else:
                    raise ValueError(
                        'The main __init__ has objects that are not present in '
                        f'''diffusers.utils.dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py. Run `make fix-copies` '''
                        'to fix this.' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    __snake_case   							=						parser.parse_args()

    check_dummies(args.fix_and_overwrite)


