


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 		a__    (							a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       List[str]     						=				"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=0       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     floats_tensor((1, 3, 1_28, 1_28) ,   rng=random.Random(lowerCamelCase_       )       )
														lowerCAmelCase__	       =     np.random.RandomState(lowerCamelCase_       )
														lowerCAmelCase__	       =     {
														    '''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   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
														pipe.set_progress_bar_config(disable=lowerCamelCase_       )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__	       =     np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]       )
														assert np.abs(image_slice - expected_slice       ).max() < 1e-1







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
														lowerCAmelCase__	       =     PNDMScheduler.from_config(pipe.scheduler.config ,   skip_prk_steps=lowerCamelCase_       )
														pipe.set_progress_bar_config(disable=lowerCamelCase_       )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__	       =     np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__	       =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
														lowerCAmelCase__	       =     LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
														pipe.set_progress_bar_config(disable=lowerCamelCase_       )

														# warmup pass to apply optimizations
														lowerCAmelCase__	       =     pipe(**self.get_dummy_inputs()       )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__	       =     np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__	       =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
														lowerCAmelCase__	       =     EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
														pipe.set_progress_bar_config(disable=lowerCamelCase_       )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__	       =     np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__	       =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
														lowerCAmelCase__	       =     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
														pipe.set_progress_bar_config(disable=lowerCamelCase_       )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__	       =     np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]       )

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,   provider='''CPUExecutionProvider'''       )
														lowerCAmelCase__	       =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
														pipe.set_progress_bar_config(disable=lowerCamelCase_       )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__	       =     np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]       )

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




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





							'''simple docstring'''






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







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     ort.SessionOptions()
														lowerCAmelCase__	       =     False
														return options







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/img2img/sketch-mountains-input.jpg'''       )
														lowerCAmelCase__	       =     init_image.resize((7_68, 5_12)       )
														# using the PNDM scheduler by default
														lowerCAmelCase__	       =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(
														    '''CompVis/stable-diffusion-v1-4''' ,   revision='''onnx''' ,   safety_checker=lowerCamelCase_ ,   feature_extractor=lowerCamelCase_ ,   provider=self.gpu_provider ,   sess_options=self.gpu_options ,   )
														pipe.set_progress_bar_config(disable=lowerCamelCase_       )

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

														lowerCAmelCase__	       =     np.random.RandomState(0       )
														lowerCAmelCase__	       =     pipe(
														    prompt=lowerCamelCase_ ,   image=lowerCamelCase_ ,   strength=0.75 ,   guidance_scale=7.5 ,   num_inference_steps=10 ,   generator=lowerCamelCase_ ,   output_type='''np''' ,   )
														lowerCAmelCase__	       =     output.images
														lowerCAmelCase__	       =     images[0, 2_55:2_58, 3_83:3_86, -1]

														assert images.shape == (1, 5_12, 7_68, 3)
														lowerCAmelCase__	       =     np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]       )
														# 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       )	->							Dict:
														lowerCAmelCase__	       =     load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/img2img/sketch-mountains-input.jpg'''       )
														lowerCAmelCase__	       =     init_image.resize((7_68, 5_12)       )
														lowerCAmelCase__	       =     LMSDiscreteScheduler.from_pretrained(
														    '''runwayml/stable-diffusion-v1-5''' ,   subfolder='''scheduler''' ,   revision='''onnx'''       )
														lowerCAmelCase__	       =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(
														    '''runwayml/stable-diffusion-v1-5''' ,   revision='''onnx''' ,   scheduler=lowerCamelCase_ ,   safety_checker=lowerCamelCase_ ,   feature_extractor=lowerCamelCase_ ,   provider=self.gpu_provider ,   sess_options=self.gpu_options ,   )
														pipe.set_progress_bar_config(disable=lowerCamelCase_       )

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

														lowerCAmelCase__	       =     np.random.RandomState(0       )
														lowerCAmelCase__	       =     pipe(
														    prompt=lowerCamelCase_ ,   image=lowerCamelCase_ ,   strength=0.75 ,   guidance_scale=7.5 ,   num_inference_steps=20 ,   generator=lowerCamelCase_ ,   output_type='''np''' ,   )
														lowerCAmelCase__	       =     output.images
														lowerCAmelCase__	       =     images[0, 2_55:2_58, 3_83:3_86, -1]

														assert images.shape == (1, 5_12, 7_68, 3)
														lowerCAmelCase__	       =     np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]       )
														# 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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       torch.FloatTensor




class 		a__    (							a__    ,    a__  ):





							'''simple docstring'''






							@register_to_config
							def __init__(		self ,   lowerCamelCase_ = 6_55_36 ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 0 ,   lowerCamelCase_ = "fourier" ,   lowerCamelCase_ = True ,   lowerCamelCase_ = False ,   lowerCamelCase_ = 0.0 ,   lowerCamelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,   lowerCamelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,   lowerCamelCase_ = "UNetMidBlock1D" ,   lowerCamelCase_ = None ,   lowerCamelCase_ = (32, 32, 64) ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 8 ,   lowerCamelCase_ = 1 ,   lowerCamelCase_ = False ,   )	->							Optional[int]:
														super().__init__()
														lowerCAmelCase__	       =     sample_size

														# time
														if time_embedding_type == "fourier":
																					lowerCAmelCase__	       =     GaussianFourierProjection(
																					    embedding_size=8 ,   set_W_to_weight=lowerCamelCase_ ,   log=lowerCamelCase_ ,   flip_sin_to_cos=lowerCamelCase_       )
																					lowerCAmelCase__	       =     2 * block_out_channels[0]
														elif time_embedding_type == "positional":
																					lowerCAmelCase__	       =     Timesteps(
																					    block_out_channels[0] ,   flip_sin_to_cos=lowerCamelCase_ ,   downscale_freq_shift=lowerCamelCase_       )
																					lowerCAmelCase__	       =     block_out_channels[0]

														if use_timestep_embedding:
																					lowerCAmelCase__	       =     block_out_channels[0] * 4
																					lowerCAmelCase__	       =     TimestepEmbedding(
																					    in_channels=lowerCamelCase_ ,   time_embed_dim=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   out_dim=block_out_channels[0] ,   )

														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None

														# down
														lowerCAmelCase__	       =     in_channels
														for i, down_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     block_out_channels[i]

																					if i == 0:
																												input_channel += extra_in_channels

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_down_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_downsample=not is_final_block or downsample_each_block ,   )
																					self.down_blocks.append(lowerCamelCase_       )

														# mid
														lowerCAmelCase__	       =     get_mid_block(
														    lowerCamelCase_ ,   in_channels=block_out_channels[-1] ,   mid_channels=block_out_channels[-1] ,   out_channels=block_out_channels[-1] ,   embed_dim=block_out_channels[0] ,   num_layers=lowerCamelCase_ ,   add_downsample=lowerCamelCase_ ,   )

														# up
														lowerCAmelCase__	       =     list(reversed(lowerCamelCase_       )       )
														lowerCAmelCase__	       =     reversed_block_out_channels[0]
														if out_block_type is None:
																					lowerCAmelCase__	       =     out_channels
														else:
																					lowerCAmelCase__	       =     block_out_channels[0]

														for i, up_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     (
																					    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase_       ) - 1 else final_upsample_channels
																					)

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_up_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_upsample=not is_final_block ,   )
																					self.up_blocks.append(lowerCamelCase_       )
																					lowerCAmelCase__	       =     output_channel

														# out
														lowerCAmelCase__	       =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,   32       )
														lowerCAmelCase__	       =     get_out_block(
														    out_block_type=lowerCamelCase_ ,   num_groups_out=lowerCamelCase_ ,   embed_dim=block_out_channels[0] ,   out_channels=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   fc_dim=block_out_channels[-1] // 4 ,   )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = True ,   )	->							Union[UNetaDOutput, Tuple]:
														lowerCAmelCase__	       =     timestep
														if not torch.is_tensor(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     torch.tensor([timesteps] ,   dtype=torch.long ,   device=sample.device       )
														elif torch.is_tensor(lowerCamelCase_       ) and len(timesteps.shape       ) == 0:
																					lowerCAmelCase__	       =     timesteps[None].to(sample.device       )

														lowerCAmelCase__	       =     self.time_proj(lowerCamelCase_       )
														if self.config.use_timestep_embedding:
																					lowerCAmelCase__	       =     self.time_mlp(lowerCamelCase_       )
														else:
																					lowerCAmelCase__	       =     timestep_embed[..., None]
																					lowerCAmelCase__	       =     timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
																					lowerCAmelCase__	       =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

														# 2. down
														lowerCAmelCase__	       =     ()
														for downsample_block in self.down_blocks:
																					lowerCAmelCase__       , lowerCAmelCase__	       =     downsample_block(hidden_states=lowerCamelCase_ ,   temb=lowerCamelCase_       )
																					down_block_res_samples += res_samples

														# 3. mid
														if self.mid_block:
																					lowerCAmelCase__	       =     self.mid_block(lowerCamelCase_ ,   lowerCamelCase_       )

														# 4. up
														for i, upsample_block in enumerate(self.up_blocks       ):
																					lowerCAmelCase__	       =     down_block_res_samples[-1:]
																					lowerCAmelCase__	       =     down_block_res_samples[:-1]
																					lowerCAmelCase__	       =     upsample_block(lowerCamelCase_ ,   res_hidden_states_tuple=lowerCamelCase_ ,   temb=lowerCamelCase_       )

														# 5. post-process
														if self.out_block:
																					lowerCAmelCase__	       =     self.out_block(lowerCamelCase_ ,   lowerCamelCase_       )

														if not return_dict:
																					return (sample,)

														return UNetaDOutput(sample=lowerCamelCase_       )