

"""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    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    int=0)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      floats_tensor((1, 3, 1_2_8, 1_2_8)					,  rng=random.Random(a_))
						__lowerCAmelCase:	Optional[int]   				=      np.random.RandomState(a_)
						__lowerCAmelCase:	List[Any]   				=      {
						    '''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 							lowercase_					(     self							:    Union[str, Any])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,  provider="CPUExecutionProvider")
						pipe.set_progress_bar_config(disable=a_)

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

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						__lowerCAmelCase:	str   				=      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 							lowercase_					(     self							:    Optional[Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,  provider="CPUExecutionProvider")
						__lowerCAmelCase:	List[Any]   				=      PNDMScheduler.from_config(pipe.scheduler.config					,  skip_prk_steps=a_)
						pipe.set_progress_bar_config(disable=a_)

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

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						__lowerCAmelCase:	Optional[Any]   				=      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 							lowercase_					(     self							:    Dict)->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,  provider="CPUExecutionProvider")
						__lowerCAmelCase:	Optional[Any]   				=      LMSDiscreteScheduler.from_config(pipe.scheduler.config)
						pipe.set_progress_bar_config(disable=a_)

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

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

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						__lowerCAmelCase:	List[str]   				=      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 							lowercase_					(     self							:    Union[str, Any])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,  provider="CPUExecutionProvider")
						__lowerCAmelCase:	List[Any]   				=      EulerDiscreteScheduler.from_config(pipe.scheduler.config)
						pipe.set_progress_bar_config(disable=a_)

						__lowerCAmelCase:	Optional[int]   				=      self.get_dummy_inputs()
						__lowerCAmelCase:	Any   				=      pipe(**a_).images
						__lowerCAmelCase:	List[Any]   				=      image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						__lowerCAmelCase:	Dict   				=      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 							lowercase_					(     self							:    Any)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,  provider="CPUExecutionProvider")
						__lowerCAmelCase:	Optional[int]   				=      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
						pipe.set_progress_bar_config(disable=a_)

						__lowerCAmelCase:	Union[str, Any]   				=      self.get_dummy_inputs()
						__lowerCAmelCase:	Optional[Any]   				=      pipe(**a_).images
						__lowerCAmelCase:	Dict   				=      image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						__lowerCAmelCase:	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 							lowercase_					(     self							:    List[str])->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,  provider="CPUExecutionProvider")
						__lowerCAmelCase:	List[Any]   				=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
						pipe.set_progress_bar_config(disable=a_)

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

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						__lowerCAmelCase:	Dict   				=      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    snake_case     (    unittest.TestCase  ):

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



						'''simple docstring'''


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

				@property
				def 							lowercase_					(     self							:    Optional[Any])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      ort.SessionOptions()
						__lowerCAmelCase:	Dict   				=      False
						return options

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



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/img2img/sketch-mountains-input.jpg")
						__lowerCAmelCase:	Any   				=      init_image.resize((7_6_8, 5_1_2))
						# using the PNDM scheduler by default
						__lowerCAmelCase:	Tuple   				=      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_)

						__lowerCAmelCase:	Tuple   				=      '''A fantasy landscape, trending on artstation'''

						__lowerCAmelCase:	List[Any]   				=      np.random.RandomState(0)
						__lowerCAmelCase:	int   				=      pipe(
						    prompt=a_					,  image=a_					,  strength=0.75					,  guidance_scale=7.5					,  num_inference_steps=1_0					,  generator=a_					,  output_type="np"					,  )
						__lowerCAmelCase:	List[Any]   				=      output.images
						__lowerCAmelCase:	Dict   				=      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)
						__lowerCAmelCase:	int   				=      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 							lowercase_					(     self							:    Optional[Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/img2img/sketch-mountains-input.jpg")
						__lowerCAmelCase:	List[Any]   				=      init_image.resize((7_6_8, 5_1_2))
						__lowerCAmelCase:	Any   				=      LMSDiscreteScheduler.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"					,  subfolder="scheduler"					,  revision="onnx")
						__lowerCAmelCase:	List[str]   				=      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_)

						__lowerCAmelCase:	str   				=      '''A fantasy landscape, trending on artstation'''

						__lowerCAmelCase:	Union[str, Any]   				=      np.random.RandomState(0)
						__lowerCAmelCase:	List[str]   				=      pipe(
						    prompt=a_					,  image=a_					,  strength=0.75					,  guidance_scale=7.5					,  num_inference_steps=2_0					,  generator=a_					,  output_type="np"					,  )
						__lowerCAmelCase:	List[str]   				=      output.images
						__lowerCAmelCase:	Union[str, Any]   				=      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)
						__lowerCAmelCase:	Optional[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 math import ceil






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Tuple   				=      list(range(0 ,			__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Optional[Any]   				=      [item for sublist in list(device_map.values()			) for item in sublist]

		# Duplicate check
		__lowerCAmelCase:	List[Any]   				=      []
		for i in device_map_blocks:
				if device_map_blocks.count(__SCREAMING_SNAKE_CASE			) > 1 and i not in duplicate_blocks:
						duplicate_blocks.append(__SCREAMING_SNAKE_CASE			)
    # Missing blocks
		__lowerCAmelCase:	Optional[Any]   				=      [i for i in blocks if i not in device_map_blocks]
		__lowerCAmelCase:	List[Any]   				=      [i for i in device_map_blocks if i not in blocks]

		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
				    " These attention blocks were specified more than once: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
				    "blocks to a device on the device_map: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
				    + str(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	List[Any]   				=      list(range(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	Dict   				=      int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE			)			)			)
		__lowerCAmelCase:	Union[str, Any]   				=      [layers[i : i + n_blocks] for i in range(0 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)]

		return dict(zip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)


