

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					(	lowerCamelCase__      ,  unittest.TestCase						):
   # TODO: is there an appropriate internal test set?
   UpperCamelCase       					=					"""ssube/stable-diffusion-x4-upscaler-onnx"""



   def 	__magic_name__   (							self							:					Dict,		__A							:					int=0						):
     UpperCAmelCase	:							List[str]						=			floats_tensor((1, 3, 1_2_8, 1_2_8),		rng=random.Random(__A						)						)
     UpperCAmelCase	:							str						=			torch.manual_seed(__A						)
     UpperCAmelCase	:							Dict						=			{
         '''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 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							int						=			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,		provider='''CPUExecutionProvider'''						)
     pipe.set_progress_bar_config(disable=__A						)

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

     # started as 128, should now be 512
     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     UpperCAmelCase	:							Union[str, Any]						=			np.array(
         [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]						)
     assert np.abs(image_slice - expected_slice						).max() < 1E-1



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase	:							Dict						=			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,		provider='''CPUExecutionProvider'''						)
     UpperCAmelCase	:							Optional[int]						=			PNDMScheduler.from_config(pipe.scheduler.config,		skip_prk_steps=__A						)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							Optional[int]						=			self.get_dummy_inputs()
     UpperCAmelCase	:							str						=			pipe(**__A						).images
     UpperCAmelCase	:							Dict						=			image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     UpperCAmelCase	:							Union[str, Any]						=			np.array(
         [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4]						)
     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							List[Any]						=			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,		provider='''CPUExecutionProvider'''						)
     UpperCAmelCase	:							Any						=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							List[str]						=			self.get_dummy_inputs()
     UpperCAmelCase	:							Tuple						=			pipe(**__A						).images
     UpperCAmelCase	:							Any						=			image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     UpperCAmelCase	:							int						=			np.array(
         [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5]						)

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



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							Any						=			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,		provider='''CPUExecutionProvider'''						)
     UpperCAmelCase	:							Optional[int]						=			EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							Union[str, Any]						=			self.get_dummy_inputs()
     UpperCAmelCase	:							Tuple						=			pipe(**__A						).images
     UpperCAmelCase	:							Union[str, Any]						=			image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     UpperCAmelCase	:							Optional[int]						=			np.array(
         [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]						)
     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1





   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							List[str]						=			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,		provider='''CPUExecutionProvider'''						)
     UpperCAmelCase	:							List[Any]						=			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							List[Any]						=			self.get_dummy_inputs()
     UpperCAmelCase	:							Tuple						=			pipe(**__A						).images
     UpperCAmelCase	:							Union[str, Any]						=			image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     UpperCAmelCase	:							Optional[int]						=			np.array(
         [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3]						)

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






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



   @property
   def 	__magic_name__   (							self							:					str						):
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )



   @property
   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							Tuple						=			ort.SessionOptions()
     UpperCAmelCase	:							str						=			False
     return options



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							Dict						=			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''						)
     UpperCAmelCase	:							Union[str, Any]						=			init_image.resize((1_2_8, 1_2_8)						)
     # using the PNDM scheduler by default
     UpperCAmelCase	:							Union[str, Any]						=			OnnxStableDiffusionUpscalePipeline.from_pretrained(
         '''ssube/stable-diffusion-x4-upscaler-onnx''',		provider=self.gpu_provider,		sess_options=self.gpu_options,		)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							List[str]						=			'''A fantasy landscape, trending on artstation'''

     UpperCAmelCase	:							Optional[Any]						=			torch.manual_seed(0						)
     UpperCAmelCase	:							Dict						=			pipe(
         prompt=__A,		image=__A,		guidance_scale=7.5,		num_inference_steps=1_0,		generator=__A,		output_type='''np''',		)
     UpperCAmelCase	:							Any						=			output.images
     UpperCAmelCase	:							Optional[Any]						=			images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert images.shape == (1, 5_1_2, 5_1_2, 3)
     UpperCAmelCase	:							int						=			np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2]						)
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Union[str, Any]						=			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''						)
     UpperCAmelCase	:							Tuple						=			init_image.resize((1_2_8, 1_2_8)						)
     UpperCAmelCase	:							Any						=			LMSDiscreteScheduler.from_pretrained(
         '''ssube/stable-diffusion-x4-upscaler-onnx''',		subfolder='''scheduler'''						)
     UpperCAmelCase	:							Any						=			OnnxStableDiffusionUpscalePipeline.from_pretrained(
         '''ssube/stable-diffusion-x4-upscaler-onnx''',		scheduler=__A,		provider=self.gpu_provider,		sess_options=self.gpu_options,		)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							List[Any]						=			'''A fantasy landscape, trending on artstation'''

     UpperCAmelCase	:							int						=			torch.manual_seed(0						)
     UpperCAmelCase	:							Any						=			pipe(
         prompt=__A,		image=__A,		guidance_scale=7.5,		num_inference_steps=2_0,		generator=__A,		output_type='''np''',		)
     UpperCAmelCase	:							Optional[Any]						=			output.images
     UpperCAmelCase	:							Tuple						=			images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert images.shape == (1, 5_1_2, 5_1_2, 3)
     UpperCAmelCase	:							Any						=			np.array(
         [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6]						)
     # 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__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      int      )  ->						str:
  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''      )
  UpperCAmelCase	:							List[Any]						=			str(bin(UpperCAmelCase      )      )[2:]  # remove the leading "0b"
  UpperCAmelCase	:							List[str]						=			str(bin(UpperCAmelCase      )      )[2:]
  UpperCAmelCase	:							Optional[Any]						=			max(len(UpperCAmelCase      )    ,			len(UpperCAmelCase      )      )
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)      )      )
      for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase      )    ,			b_binary.zfill(UpperCAmelCase      )      )      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





