


import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       UNetaDModel     ,							SCREAMING_SNAKE_CASE__				:       UNetaDModel     ,							SCREAMING_SNAKE_CASE__				:       DDPMScheduler     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							)		->			List[Any]:
             super().__init__()
             a_					:		Tuple							=  value_function
             a_					:		List[str]							=  unet
             a_					:		Optional[int]							=  scheduler
             a_					:		str							=  env
             a_					:		str							=  env.get_dataset()
             a_					:		Dict							=  {}
             for key in self.data.keys():
                    try:
                           a_					:		Any							=  self.data[key].mean()
                    except:  # noqa: E722
                           pass
             a_					:		List[Any]							=  {}
             for key in self.data.keys():
                    try:
                           a_					:		int							=  self.data[key].std()
                    except:  # noqa: E722
                           pass
             a_					:		int							=  env.observation_space.shape[0]
             a_					:		Tuple							=  env.action_space.shape[0]
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			Tuple:
             return (x_in - self.means[key]) / self.stds[key]
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Any:
             return x_in * self.stds[key] + self.means[key]
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Optional[int]:
             if type(SCREAMING_SNAKE_CASE__     ) is dict:
                    return {k: self.to_torch(SCREAMING_SNAKE_CASE__     ) for k, v in x_in.items()}
             elif torch.is_tensor(SCREAMING_SNAKE_CASE__     ):
                    return x_in.to(self.unet.device     )
             return torch.tensor(SCREAMING_SNAKE_CASE__     ,							device=self.unet.device     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Any:
             for key, val in cond.items():
                    a_					:		int							=  val.clone()
             return x_in
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			Any:
             a_					:		Optional[int]							=  x.shape[0]
             a_					:		Optional[Any]							=  None
             for i in tqdm.tqdm(self.scheduler.timesteps     ):
                    # create batch of timesteps to pass into model
                    a_					:		Optional[int]							=  torch.full((batch_size,)     ,							SCREAMING_SNAKE_CASE__     ,							device=self.unet.device     ,							dtype=torch.long     )
                    for _ in range(SCREAMING_SNAKE_CASE__     ):
                           with torch.enable_grad():
                                  x.requires_grad_()

                                  # permute to match dimension for pre-trained models
                                  a_					:		Tuple							=  self.value_function(x.permute(0     ,							2     ,							1     )     ,							SCREAMING_SNAKE_CASE__     ).sample
                                  a_					:		List[Any]							=  torch.autograd.grad([y.sum()]     ,							[x]     )[0]

                                  a_					:		Union[str, Any]							=  self.scheduler._get_variance(SCREAMING_SNAKE_CASE__     )
                                  a_					:		Optional[int]							=  torch.exp(0.5 * posterior_variance     )
                                  a_					:		List[Any]							=  model_std * grad

                           a_					:		List[str]							=  0
                           a_					:		Dict							=  x.detach()
                           a_					:		Any							=  x + scale * grad
                           a_					:		int							=  self.reset_xa(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							self.action_dim     )

                    a_					:		Tuple							=  self.unet(x.permute(0     ,							2     ,							1     )     ,							SCREAMING_SNAKE_CASE__     ).sample.permute(0     ,							2     ,							1     )

                    # TODO: verify deprecation of this kwarg
                    a_					:		List[str]							=  self.scheduler.step(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							predict_epsilon=SCREAMING_SNAKE_CASE__     )['prev_sample']

                    # apply conditions to the trajectory (set the initial state)
                    a_					:		Optional[Any]							=  self.reset_xa(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							self.action_dim     )
                    a_					:		Union[str, Any]							=  self.to_torch(SCREAMING_SNAKE_CASE__     )
             return x, y

      def __call__(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple=6_4     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=3_2     ,							SCREAMING_SNAKE_CASE__				:       Tuple=2     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     )		->			List[Any]:
             # normalize the observations and create  batch dimension
             a_					:		List[str]							=  self.normalize(SCREAMING_SNAKE_CASE__     ,							'observations'     )
             a_					:		Dict							=  obs[None].repeat(SCREAMING_SNAKE_CASE__     ,							axis=0     )

             a_					:		Any							=  {0: self.to_torch(SCREAMING_SNAKE_CASE__     )}
             a_					:		Optional[int]							=  (batch_size, planning_horizon, self.state_dim + self.action_dim)

             # generate initial noise and apply our conditions (to make the trajectories start at current state)
             a_					:		List[str]							=  randn_tensor(SCREAMING_SNAKE_CASE__     ,							device=self.unet.device     )
             a_					:		Dict							=  self.reset_xa(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							self.action_dim     )
             a_					:		Union[str, Any]							=  self.to_torch(SCREAMING_SNAKE_CASE__     )

             # run the diffusion process
             a_							,		a_					:		List[str]							=  self.run_diffusion(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             # sort output trajectories by value
             a_					:		List[str]							=  y.argsort(0     ,							descending=SCREAMING_SNAKE_CASE__     ).squeeze()
             a_					:		int							=  x[sorted_idx]
             a_					:		Optional[int]							=  sorted_values[:, :, : self.action_dim]
             a_					:		Optional[Any]							=  actions.detach().cpu().numpy()
             a_					:		Dict							=  self.de_normalize(SCREAMING_SNAKE_CASE__     ,							key='actions'     )

             # select the action with the highest value
             if y is not None:
                    a_					:		str							=  0
             else:
                    # if we didn't run value guiding, select a random action
                    a_					:		List[Any]							=  np.random.randint(0     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  denorm_actions[selected_index, 0]
             return denorm_actions






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  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      # TODO: is there an appropriate internal test set?
      snake_case__				:     Any        =						'''ssube/stable-diffusion-x4-upscaler-onnx'''
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       int=0     )		->			Tuple:
             a_					:		Union[str, Any]							=  floats_tensor((1, 3, 1_2_8, 1_2_8)     ,							rng=random.Random(SCREAMING_SNAKE_CASE__     )     )
             a_					:		Dict							=  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  {
                 '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        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Dict:
             a_					:		Dict							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             # started as 128, should now be 512
             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		List[Any]							=  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        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Dict:
             a_					:		Tuple							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		int							=  PNDMScheduler.from_config(pipe.scheduler.config     ,							skip_prk_steps=SCREAMING_SNAKE_CASE__     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		str							=  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        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Dict:
             a_					:		Optional[Any]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		Optional[Any]							=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Optional[Any]							=  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        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			int:
             a_					:		List[str]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		int							=  EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		int							=  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        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		Tuple							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,							provider='CPUExecutionProvider'     )
             a_					:		Optional[int]							=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             a_					:		Union[str, Any]							=  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  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Union[str, Any]:
             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[str]:
             a_					:		List[str]							=  ort.SessionOptions()
             a_					:		int							=  False
             return options
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Tuple:
             a_					:		str							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'     )
             a_					:		int							=  init_image.resize((1_2_8, 1_2_8)     )
             # using the PNDM scheduler by default
             a_					:		Dict							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

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

             a_					:		str							=  torch.manual_seed(0     )
             a_					:		List[str]							=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							guidance_scale=7.5     ,							num_inference_steps=1_0     ,							generator=SCREAMING_SNAKE_CASE__     ,							output_type='np'     ,							)
             a_					:		Dict							=  output.images
             a_					:		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)
             a_					:		str							=  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        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[str]:
             a_					:		Dict							=  load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'     )
             a_					:		List[str]							=  init_image.resize((1_2_8, 1_2_8)     )
             a_					:		Dict							=  LMSDiscreteScheduler.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							subfolder='scheduler'     )
             a_					:		Union[str, Any]							=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
                 'ssube/stable-diffusion-x4-upscaler-onnx'     ,							scheduler=SCREAMING_SNAKE_CASE__     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
             pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  'A fantasy landscape, trending on artstation'

             a_					:		Tuple							=  torch.manual_seed(0     )
             a_					:		Optional[Any]							=  pipe(
                 prompt=SCREAMING_SNAKE_CASE__     ,							image=SCREAMING_SNAKE_CASE__     ,							guidance_scale=7.5     ,							num_inference_steps=2_0     ,							generator=SCREAMING_SNAKE_CASE__     ,							output_type='np'     ,							)
             a_					:		str							=  output.images
             a_					:		List[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)
             a_					:		Tuple							=  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


