


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE       :      Tuple												= logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE       :      Optional[Any]												= "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=8 )    ->						Dict:
    _lowercase       :							Optional[Any]      		=		height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _lowercase       :							Any      		=		width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   Dict:




           """simple docstring"""

           super().__init__()

           self.register_modules(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      movq=lowerCamelCase,      )
           _lowercase       :							List[str]      		=		2 ** (len(self.movq.config.block_out_channels) - 1)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[Any]:




           """simple docstring"""

           if latents is None:
               _lowercase       :							Optional[Any]      		=		randn_tensor(lowerCamelCase,      generator=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase)
           else:
               if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''')
               _lowercase       :							int      		=		latents.to(lowerCamelCase)

           _lowercase       :							int      		=		latents * scheduler.init_noise_sigma
           return latents
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   Optional[int]:




           """simple docstring"""

           if is_accelerate_available():
               from accelerate import cpu_offload
           else:
               raise ImportError('Please install accelerate via `pip install accelerate`')

           _lowercase       :							Tuple      		=		torch.device(F'''cuda:{gpu_id}''')

           _lowercase       :							Optional[Any]      		=		[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   int:




           """simple docstring"""

           if is_accelerate_available() and is_accelerate_version('>=',      '0.17.0.dev0'):
               from accelerate import cpu_offload_with_hook
           else:
               raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.')

           _lowercase       :							Union[str, Any]      		=		torch.device(F'''cuda:{gpu_id}''')

           if self.device.type != "cpu":
               self.to('cpu',      silence_dtype_warnings=lowerCamelCase)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           _lowercase       :							Optional[Any]      		=		None
           for cpu_offloaded_model in [self.unet, self.movq]:
               _lowercase		,				_lowercase       :							List[str]      		=		cpu_offload_with_hook(lowerCamelCase,      lowerCamelCase,      prev_module_hook=lowerCamelCase)

           # We'll offload the last model manually.
           _lowercase       :							Dict      		=		hook
       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           if not hasattr(self.unet,      '_hf_hook'):
               return self.device
           for module in self.unet.modules():
               if (
                   hasattr(lowerCamelCase,      '_hf_hook')
                   and hasattr(module._hf_hook,      'execution_device')
                   and module._hf_hook.execution_device is not None
               ):
                   return torch.device(module._hf_hook.execution_device)
           return self.device




       @torch.no_grad()
       @replace_example_docstring(lowerCamelCase)
       def __call__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 5_12,      lowerCamelCase = 5_12,      lowerCamelCase = 1_00,      lowerCamelCase = 4.0,      lowerCamelCase = 1,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      )       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							int      		=		self._execution_device

           _lowercase       :							Any      		=		guidance_scale > 1.0

           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							int      		=		torch.cat(lowerCamelCase,      dim=0)
           _lowercase       :							Optional[int]      		=		image_embeds.shape[0] * num_images_per_prompt
           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							Optional[int]      		=		torch.cat(lowerCamelCase,      dim=0)

           if do_classifier_free_guidance:
               _lowercase       :							str      		=		image_embeds.repeat_interleave(lowerCamelCase,      dim=0)
               _lowercase       :							int      		=		negative_image_embeds.repeat_interleave(lowerCamelCase,      dim=0)

           _lowercase       :							List[str]      		=		torch.cat([negative_image_embeds, image_embeds],      dim=0).to(dtype=self.unet.dtype,      device=lowerCamelCase)

           self.scheduler.set_timesteps(lowerCamelCase,      device=lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.scheduler.timesteps

           _lowercase       :							List[str]      		=		self.unet.config.in_channels

           _lowercase		,				_lowercase       :							Union[str, Any]      		=		downscale_height_and_width(lowerCamelCase,      lowerCamelCase,      self.movq_scale_factor)

           # create initial latent
           _lowercase       :							str      		=		self.prepare_latents(
               (batch_size, num_channels_latents, height, width),      image_embeds.dtype,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      self.scheduler,      )

           for i, t in enumerate(self.progress_bar(lowerCamelCase)):
               # expand the latents if we are doing classifier free guidance
               _lowercase       :							List[Any]      		=		torch.cat([latents] * 2) if do_classifier_free_guidance else latents

               _lowercase       :							Tuple      		=		{'image_embeds': image_embeds}
               _lowercase       :							Optional[Any]      		=		self.unet(
                   sample=lowerCamelCase,      timestep=lowerCamelCase,      encoder_hidden_states=lowerCamelCase,      added_cond_kwargs=lowerCamelCase,      return_dict=lowerCamelCase,      )[0]

               if do_classifier_free_guidance:
                   _lowercase		,				_lowercase       :							List[str]      		=		noise_pred.split(latents.shape[1],      dim=1)
                   _lowercase		,				_lowercase       :							Tuple      		=		noise_pred.chunk(2)
                   _lowercase		,				_lowercase       :							Any      		=		variance_pred.chunk(2)
                   _lowercase       :							Dict      		=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   _lowercase       :							Tuple      		=		torch.cat([noise_pred, variance_pred_text],      dim=1)

               if not (
                   hasattr(self.scheduler.config,      'variance_type')
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                   _lowercase		,				_lowercase       :							int      		=		noise_pred.split(latents.shape[1],      dim=1)

               # compute the previous noisy sample x_t -> x_t-1
               _lowercase       :							str      		=		self.scheduler.step(
                   lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      generator=lowerCamelCase,      )[0]
           # post-processing
           _lowercase       :							str      		=		self.movq.decode(lowerCamelCase,      force_not_quantize=lowerCamelCase)['sample']

           if output_type not in ["pt", "np", "pil"]:
               raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''')

           if output_type in ["np", "pil"]:
               _lowercase       :							Dict      		=		image * 0.5 + 0.5
               _lowercase       :							Any      		=		image.clamp(0,      1)
               _lowercase       :							List[str]      		=		image.cpu().permute(0,      2,      3,      1).float().numpy()

           if output_type == "pil":
               _lowercase       :							Optional[Any]      		=		self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowerCamelCase)






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 _lowerCamelCase(						_a, unittest.TestCase   ):
       # TODO: is there an appropriate internal test set?
       lowercase_ :	int     =			"""ssube/stable-diffusion-x4-upscaler-onnx"""
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		floats_tensor((1, 3, 1_28, 1_28),      rng=random.Random(lowerCamelCase))
           _lowercase       :							Union[str, Any]      		=		torch.manual_seed(lowerCamelCase)
           _lowercase       :							Optional[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 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Tuple      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Dict      		=		self.get_dummy_inputs()
           _lowercase       :							Optional[int]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1].flatten()

           # started as 128, should now be 512
           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							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 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							str      		=		PNDMScheduler.from_config(pipe.scheduler.config,      skip_prk_steps=lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

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

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							int      		=		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 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							Optional[int]      		=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

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

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Optional[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 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							List[str]      		=		EulerDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

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

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							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.flatten() - expected_slice).max() < 1E-1




       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Tuple      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							Any      		=		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

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

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		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 _lowerCamelCase(						unittest.TestCase   ):
       @property
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
       @property
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		ort.SessionOptions()
           _lowercase       :							str      		=		False
           return options
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							int      		=		init_image.resize((1_28, 1_28))
           # using the PNDM scheduler by default
           _lowercase       :							Tuple      		=		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)

           _lowercase       :							Optional[int]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							str      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      guidance_scale=7.5,      num_inference_steps=10,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							List[Any]      		=		output.images
           _lowercase       :							List[Any]      		=		images[0, 2_55:2_58, 3_83:3_86, -1]

           assert images.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							List[Any]      		=		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 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							int      		=		init_image.resize((1_28, 1_28))
           _lowercase       :							str      		=		LMSDiscreteScheduler.from_pretrained(
               'ssube/stable-diffusion-x4-upscaler-onnx',      subfolder='scheduler')
           _lowercase       :							Dict      		=		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)

           _lowercase       :							Optional[int]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							str      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      guidance_scale=7.5,      num_inference_steps=20,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							str      		=		output.images
           _lowercase       :							str      		=		images[0, 2_55:2_58, 3_83:3_86, -1]

           assert images.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, 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


