

import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      "ssube/stable-diffusion-x4-upscaler-onnx"






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Tuple=0      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, 3, 128, 128)			,     rng=random.Random(__lowerCamelCase      )      )
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Tuple      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider='''CPUExecutionProvider'''      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       # started as 128, should now be 512
       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]      )
       assert np.abs(image_slice - expected_slice      ).max() < 1e-1






  def    lowercase_	(							self		:	Dict      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider='''CPUExecutionProvider'''      )
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler.from_config(pipe.scheduler.config			,     skip_prk_steps=__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964]      )
       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider='''CPUExecutionProvider'''      )
       SCREAMING_SNAKE_CASE__					=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515]      )

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






  def    lowercase_	(							self		:	List[Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider='''CPUExecutionProvider'''      )
       SCREAMING_SNAKE_CASE__					=					EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223]      )
       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1







  def    lowercase_	(							self		:	Dict      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider='''CPUExecutionProvider'''      )
       SCREAMING_SNAKE_CASE__					=					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043]      )

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






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






  """simple docstring"""


  @property
  def    lowercase_	(							self		:	int      )	->							Optional[int]:
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					ort.SessionOptions()
       SCREAMING_SNAKE_CASE__					=					False
       return options






  def    lowercase_	(							self		:	Any      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''      )
       SCREAMING_SNAKE_CASE__					=					init_image.resize((128, 128)      )
       # using the PNDM scheduler by default
       SCREAMING_SNAKE_CASE__					=					OnnxStableDiffusionUpscalePipeline.from_pretrained(
           '''ssube/stable-diffusion-x4-upscaler-onnx'''			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					pipe(
           prompt=__lowerCamelCase			,     image=__lowerCamelCase			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=__lowerCamelCase			,     output_type='''np'''			,     )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					images[0, 255:258, 383:386, -1]

       assert images.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]      )
       # 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]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''      )
       SCREAMING_SNAKE_CASE__					=					init_image.resize((128, 128)      )
       SCREAMING_SNAKE_CASE__					=					LMSDiscreteScheduler.from_pretrained(
           '''ssube/stable-diffusion-x4-upscaler-onnx'''			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					OnnxStableDiffusionUpscalePipeline.from_pretrained(
           '''ssube/stable-diffusion-x4-upscaler-onnx'''			,     scheduler=__lowerCamelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

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

       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					pipe(
           prompt=__lowerCamelCase			,     image=__lowerCamelCase			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=__lowerCamelCase			,     output_type='''np'''			,     )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					images[0, 255:258, 383:386, -1]

       assert images.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566]      )
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





import math






class  UpperCAmelCase__	:






  """simple docstring"""


  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	list[list[float]]			,     __lowerCamelCase		:	list[int]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					0.0
       SCREAMING_SNAKE_CASE__					=					0.0
       for i in range(len(__lowerCamelCase      )      ):
            da += math.pow((sample[i] - weights[0][i])			,     2      )
            da += math.pow((sample[i] - weights[1][i])			,     2      )
            return 0 if da > da else 1
       return 0







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	list[list[int | float]]			,     __lowerCamelCase		:	list[int]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	float      )	->							list[list[int | float]]:
       for i in range(len(__lowerCamelCase      )      ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

     # weight initialization ( n, C )
     SCREAMING_SNAKE_CASE__					=					[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

     # training
     SCREAMING_SNAKE_CASE__					=					SelfOrganizingMap()
     SCREAMING_SNAKE_CASE__					=					3
     SCREAMING_SNAKE_CASE__					=					0.5

     for _ in range(_A       ):
          for j in range(len(_A       )       ):
               # training sample
               SCREAMING_SNAKE_CASE__					=					training_samples[j]

               # Compute the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

               # Update the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.update(_A   ,	_A   ,	_A   ,	_A       )

    # classify test sample
     SCREAMING_SNAKE_CASE__					=					[0, 0, 0, 1]
     SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

     # results
     print(F'''Clusters that the test sample belongs to : {winner}'''       )
     print(F'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
       main()


