

"""simple docstring"""




import flax.linen as nn
import jax
import jax.numpy as jnp





class    lowerCamelCase						(nn.Module			):
			lowerCamelCase__   :		int
			lowerCamelCase__   :		jnp.dtype							=							jnp.floataa

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    self.out_channels    ,				kernel_size=(3, 3)    ,				strides=(1, 1)    ,				padding=((1, 1), (1, 1))    ,				dtype=self.dtype    ,				)





			def __call__(					self				:						int    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      hidden_states.shape
						SCREAMING_SNAKE_CASE__								=      jax.image.resize(
						    __UpperCAmelCase    ,				shape=(batch, height * 2, width * 2, channels)    ,				method="""nearest"""    ,				)
						SCREAMING_SNAKE_CASE__								=      self.conv(__UpperCAmelCase       )
						return hidden_states





class    lowerCamelCase						(nn.Module			):
			lowerCamelCase__   :		int
			lowerCamelCase__   :		jnp.dtype							=							jnp.floataa

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    self.out_channels    ,				kernel_size=(3, 3)    ,				strides=(2, 2)    ,				padding=((1, 1), (1, 1))    ,				dtype=self.dtype    ,				)





			def __call__(					self				:						List[str]    ,				__UpperCAmelCase				:						Tuple       )   ->    Tuple:
						# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
						# hidden_states = jnp.pad(hidden_states, pad_width=pad)
						SCREAMING_SNAKE_CASE__								=      self.conv(__UpperCAmelCase       )
						return hidden_states





class    lowerCamelCase						(nn.Module			):
			lowerCamelCase__   :		int
			lowerCamelCase__   :		int							=							None
			lowerCamelCase__   :		float							=							0.0
			lowerCamelCase__   :		bool							=							None
			lowerCamelCase__   :		jnp.dtype							=							jnp.floataa

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.in_channels if self.out_channels is None else self.out_channels

						SCREAMING_SNAKE_CASE__								=      nn.GroupNorm(num_groups=3_2    ,				epsilon=1e-5       )
						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    __UpperCAmelCase    ,				kernel_size=(3, 3)    ,				strides=(1, 1)    ,				padding=((1, 1), (1, 1))    ,				dtype=self.dtype    ,				)

						SCREAMING_SNAKE_CASE__								=      nn.Dense(__UpperCAmelCase    ,				dtype=self.dtype       )

						SCREAMING_SNAKE_CASE__								=      nn.GroupNorm(num_groups=3_2    ,				epsilon=1e-5       )
						SCREAMING_SNAKE_CASE__								=      nn.Dropout(self.dropout_prob       )
						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    __UpperCAmelCase    ,				kernel_size=(3, 3)    ,				strides=(1, 1)    ,				padding=((1, 1), (1, 1))    ,				dtype=self.dtype    ,				)

						SCREAMING_SNAKE_CASE__								=      self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

						SCREAMING_SNAKE_CASE__								=      None
						if use_nin_shortcut:
									SCREAMING_SNAKE_CASE__								=      nn.Conv(
									    __UpperCAmelCase    ,				kernel_size=(1, 1)    ,				strides=(1, 1)    ,				padding="""VALID"""    ,				dtype=self.dtype    ,				)





			def __call__(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any=True       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      hidden_states
						SCREAMING_SNAKE_CASE__								=      self.norma(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      nn.swish(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.conva(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.time_emb_proj(nn.swish(__UpperCAmelCase       )       )
						SCREAMING_SNAKE_CASE__								=      jnp.expand_dims(jnp.expand_dims(__UpperCAmelCase    ,				1       )    ,				1       )
						SCREAMING_SNAKE_CASE__								=      hidden_states + temb

						SCREAMING_SNAKE_CASE__								=      self.norma(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      nn.swish(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.dropout(__UpperCAmelCase    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.conva(__UpperCAmelCase       )

						if self.conv_shortcut is not None:
									SCREAMING_SNAKE_CASE__								=      self.conv_shortcut(__UpperCAmelCase       )

						return hidden_states + residual





"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
						import onnxruntime as ort





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

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

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      ort.SessionOptions()
						SCREAMING_SNAKE_CASE__								=      False
						return options





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""       )

						# using the PNDM scheduler by default
						SCREAMING_SNAKE_CASE__								=      OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""    ,				revision="""onnx"""    ,				safety_checker=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				provider=self.gpu_provider    ,				sess_options=self.gpu_options    ,				)
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

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

						SCREAMING_SNAKE_CASE__								=      np.random.RandomState(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(
						    prompt=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				strength=0.75    ,				guidance_scale=7.5    ,				num_inference_steps=1_5    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 1e-2


