



'''simple docstring'''



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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__	(		UpperCamelCase__							,       unittest.TestCase ):
					# FIXME: add fast tests
					pass






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



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



					@property
					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  ort.SessionOptions()
						__lowerCAmelCase												=  False
						return options



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo.png"     )
						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"     )
						__lowerCAmelCase												=  OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    "runwayml/stable-diffusion-inpainting"			,				revision="onnx"			,				safety_checker=UpperCamelCase			,				feature_extractor=UpperCamelCase			,				provider=self.gpu_provider			,				sess_options=self.gpu_options			,				)
						pipe.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  "A red cat sitting on a park bench"

						__lowerCAmelCase												=  np.random.RandomState(0     )
						__lowerCAmelCase												=  pipe(
						    prompt=UpperCamelCase			,				image=UpperCamelCase			,				mask_image=UpperCamelCase			,				guidance_scale=7.5			,				num_inference_steps=10			,				generator=UpperCamelCase			,				output_type="np"			,				)
						__lowerCAmelCase												=  output.images
						__lowerCAmelCase												=  images[0, 255:258, 255:258, -1]

						assert images.shape == (1, 512, 512, 3)
						__lowerCAmelCase												=  np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]     )

						assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo.png"     )
						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"     )
						__lowerCAmelCase												=  LMSDiscreteScheduler.from_pretrained(
						    "runwayml/stable-diffusion-inpainting"			,				subfolder="scheduler"			,				revision="onnx"     )
						__lowerCAmelCase												=  OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    "runwayml/stable-diffusion-inpainting"			,				revision="onnx"			,				scheduler=UpperCamelCase			,				safety_checker=UpperCamelCase			,				feature_extractor=UpperCamelCase			,				provider=self.gpu_provider			,				sess_options=self.gpu_options			,				)
						pipe.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  "A red cat sitting on a park bench"

						__lowerCAmelCase												=  np.random.RandomState(0     )
						__lowerCAmelCase												=  pipe(
						    prompt=UpperCamelCase			,				image=UpperCamelCase			,				mask_image=UpperCamelCase			,				guidance_scale=7.5			,				num_inference_steps=20			,				generator=UpperCamelCase			,				output_type="np"			,				)
						__lowerCAmelCase												=  output.images
						__lowerCAmelCase												=  images[0, 255:258, 255:258, -1]

						assert images.shape == (1, 512, 512, 3)
						__lowerCAmelCase												=  np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]     )

						assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3




'''simple docstring'''



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):



					@register_to_config
					def __init__(    self			,				UpperCamelCase = 16			,				UpperCamelCase = 88			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = 1			,				UpperCamelCase = 0.0			,				UpperCamelCase = 32			,				UpperCamelCase = None			,				UpperCamelCase = False			,				UpperCamelCase = None			,				UpperCamelCase = "geglu"			,				UpperCamelCase = True			,				UpperCamelCase = True			,				)     ->			List[str]:
						super().__init__()
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  attention_head_dim
						__lowerCAmelCase												=  num_attention_heads * attention_head_dim

						__lowerCAmelCase												=  in_channels

						__lowerCAmelCase												=  torch.nn.GroupNorm(num_groups=UpperCamelCase			,				num_channels=UpperCamelCase			,				eps=1E-6			,				affine=UpperCamelCase     )
						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )

						# 3. Define transformers blocks
						__lowerCAmelCase												=  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				dropout=UpperCamelCase			,				cross_attention_dim=UpperCamelCase			,				activation_fn=UpperCamelCase			,				attention_bias=UpperCamelCase			,				double_self_attention=UpperCamelCase			,				norm_elementwise_affine=UpperCamelCase			,				)
						        for d in range(UpperCamelCase     )
						    ]     )

						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=1			,				UpperCamelCase=None			,				UpperCamelCase = True			,				)     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  hidden_states.shape
						__lowerCAmelCase												=  batch_frames // num_frames

						__lowerCAmelCase												=  hidden_states

						__lowerCAmelCase												=  hidden_states[None, :].reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				2			,				1			,				3			,				4     )

						__lowerCAmelCase												=  self.norm(UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				3			,				4			,				2			,				1     ).reshape(batch_size * height * width			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  self.proj_in(UpperCamelCase     )

						# 2. Blocks
						for block in self.transformer_blocks:
							__lowerCAmelCase												=  block(
							    UpperCamelCase			,				encoder_hidden_states=UpperCamelCase			,				timestep=UpperCamelCase			,				cross_attention_kwargs=UpperCamelCase			,				class_labels=UpperCamelCase			,				)

						# 3. Output
						__lowerCAmelCase												=  self.proj_out(UpperCamelCase     )
						__lowerCAmelCase												=  (
						    hidden_states[None, None, :]
						    .reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						    .permute(0			,				3			,				4			,				1			,				2     )
						    .contiguous()
						)
						__lowerCAmelCase												=  hidden_states.reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  hidden_states + residual

						if not return_dict:
							return (output,)

						return TransformerTemporalModelOutput(sample=UpperCamelCase     )