'''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_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str=0     )  ->			int:
        _A    		=  floats_tensor((1, 3, 1_28, 1_28), rng=random.Random(UpperCamelCase__     )     )
        _A    		=  np.random.RandomState(UpperCamelCase__     )
        _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      :			Union[str, Any]     )  ->			Any:
        _A    		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider='CPUExecutionProvider'     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        _A    		=  np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]     )
        assert np.abs(image_slice - expected_slice     ).max() < 1e-1
   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[Any]:
        _A    		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider='CPUExecutionProvider'     )
        _A    		=  PNDMScheduler.from_config(pipe.scheduler.config, skip_prk_steps=UpperCamelCase__     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        _A    		=  np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-1
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			int:
        _A    		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider='CPUExecutionProvider'     )
        _A    		=  LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

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

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

        assert image.shape == (1, 1_28, 1_28, 3)
        _A    		=  np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-1
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Tuple:
        _A    		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider='CPUExecutionProvider'     )
        _A    		=  EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        _A    		=  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-1
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			List[str]:
        _A    		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider='CPUExecutionProvider'     )
        _A    		=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        _A    		=  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]     )

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

   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint, provider='CPUExecutionProvider'     )
        _A    		=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        _A    		=  np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]     )

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







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







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Dict:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )
   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Tuple:
        _A    		=  ort.SessionOptions()
        _A    		=  False
        return options
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			List[Any]:
        _A    		=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg'     )
        _A    		=  init_image.resize((7_68, 5_12)     )
        # using the PNDM scheduler by default
        _A    		=  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    		=  'A fantasy landscape, trending on artstation'

        _A    		=  np.random.RandomState(0     )
        _A    		=  pipe(
            prompt=UpperCamelCase__, image=UpperCamelCase__, strength=0.75, guidance_scale=7.5, num_inference_steps=10, generator=UpperCamelCase__, output_type='np', )
        _A    		=  output.images
        _A    		=  images[0, 2_55:2_58, 3_83:3_86, -1]

        assert images.shape == (1, 5_12, 7_68, 3)
        _A    		=  np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]     )
        # 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      :			Optional[int]     )  ->			Dict:
        _A    		=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg'     )
        _A    		=  init_image.resize((7_68, 5_12)     )
        _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=UpperCamelCase__, safety_checker=UpperCamelCase__, feature_extractor=UpperCamelCase__, provider=self.gpu_provider, sess_options=self.gpu_options, )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  'A fantasy landscape, trending on artstation'

        _A    		=  np.random.RandomState(0     )
        _A    		=  pipe(
            prompt=UpperCamelCase__, image=UpperCamelCase__, strength=0.75, guidance_scale=7.5, num_inference_steps=20, generator=UpperCamelCase__, output_type='np', )
        _A    		=  output.images
        _A    		=  images[0, 2_55:2_58, 3_83:3_86, -1]

        assert images.shape == (1, 5_12, 7_68, 3)
        _A    		=  np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]     )
        # 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 platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     return EnvironmentCommand()







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @staticmethod
   def 	__UpperCAmelCase   (      UpperCamelCase__      :			ArgumentParser     )  ->			List[str]:
        _A    		=  parser.add_parser('env'     )
        download_parser.set_defaults(func=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  huggingface_hub.__version__

        _A    		=  'not installed'
        _A    		=  'NA'
        if is_torch_available():
             import torch

             _A    		=  torch.__version__
             _A    		=  torch.cuda.is_available()

        _A    		=  'not installed'
        if is_transformers_available():
             import transformers

             _A    		=  transformers.__version__

        _A    		=  'not installed'
        if is_accelerate_available():
             import accelerate

             _A    		=  accelerate.__version__

        _A    		=  'not installed'
        if is_xformers_available():
             import xformers

             _A    		=  xformers.__version__

        _A    		=  {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': f'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'     )
        print(self.format_dict(UpperCamelCase__     )     )

        return info

   @staticmethod
   def 	__UpperCAmelCase   (      UpperCamelCase__      :			List[Any]     )  ->			Dict:
        return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]     ) + "\n"



