




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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class  _lowerCAmelCase     (  __SCREAMING_SNAKE_CASE	):
      A__		     =		42






class  _lowerCAmelCase     (  __SCREAMING_SNAKE_CASE    ,			__SCREAMING_SNAKE_CASE	):

      @register_to_config
      def __init__(   self      ,				__UpperCAmelCase = 3      ,				__UpperCAmelCase = 3      ,				__UpperCAmelCase = ("DownEncoderBlock2D",)      ,				__UpperCAmelCase = ("UpDecoderBlock2D",)      ,				__UpperCAmelCase = (64,)      ,				__UpperCAmelCase = 1      ,				__UpperCAmelCase = "silu"      ,				__UpperCAmelCase = 3      ,				__UpperCAmelCase = 32      ,				__UpperCAmelCase = 256      ,				__UpperCAmelCase = 32      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = 0.18215      ,				__UpperCAmelCase = "group"      ,				):
        super().__init__()

        # pass init params to Encoder
        lowerCAmelCase__				:  Tuple		       =     Encoder(
            in_channels=__snake_case      ,				out_channels=__snake_case      ,				down_block_types=__snake_case      ,				block_out_channels=__snake_case      ,				layers_per_block=__snake_case      ,				act_fn=__snake_case      ,				norm_num_groups=__snake_case      ,				double_z=__snake_case      ,				)

        lowerCAmelCase__				:  str		       =     vq_embed_dim if vq_embed_dim is not None else latent_channels

        lowerCAmelCase__				:  List[Any]		       =     nn.Convad(__snake_case      ,				__snake_case      ,				1		)
        lowerCAmelCase__				:  str		       =     VectorQuantizer(__snake_case      ,				__snake_case      ,				beta=0.25      ,				remap=__snake_case      ,				sane_index_shape=__snake_case		)
        lowerCAmelCase__				:  List[str]		       =     nn.Convad(__snake_case      ,				__snake_case      ,				1		)

        # pass init params to Decoder
        lowerCAmelCase__				:  str		       =     Decoder(
            in_channels=__snake_case      ,				out_channels=__snake_case      ,				up_block_types=__snake_case      ,				block_out_channels=__snake_case      ,				layers_per_block=__snake_case      ,				act_fn=__snake_case      ,				norm_num_groups=__snake_case      ,				norm_type=__snake_case      ,				)

      @apply_forward_hook
      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = True		):
        lowerCAmelCase__				:  str		       =     self.encoder(__snake_case		)
        lowerCAmelCase__				:  int		       =     self.quant_conv(__snake_case		)

        if not return_dict:
          return (h,)

        return VQEncoderOutput(latents=__snake_case		)

      @apply_forward_hook
      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = False      ,				__UpperCAmelCase = True		):
        # also go through quantization layer
        if not force_not_quantize:
          lowerCAmelCase__				:  List[str]		       =     self.quantize(__snake_case		)
        else:
          lowerCAmelCase__				:  Any		       =     h
        lowerCAmelCase__				:  List[str]		       =     self.post_quant_conv(__snake_case		)
        lowerCAmelCase__				:  int		       =     self.decoder(__snake_case      ,				quant if self.config.norm_type == '''spatial''' else None		)

        if not return_dict:
          return (dec,)

        return DecoderOutput(sample=__snake_case		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = True		):
        lowerCAmelCase__				:  Union[str, Any]		       =     sample
        lowerCAmelCase__				:  Optional[int]		       =     self.encode(__snake_case		).latents
        lowerCAmelCase__				:  List[Any]		       =     self.decode(__snake_case		).sample

        if not return_dict:
          return (dec,)

        return DecoderOutput(sample=__snake_case		)







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  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      # FIXME: add fast tests
      pass






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

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

      @property
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     ort.SessionOptions()
        lowerCAmelCase__				:  List[Any]		       =     False
        return options

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  Optional[int]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  List[Any]		       =     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__				:  Optional[int]		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  List[Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  int		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Any		       =     output.images
        lowerCAmelCase__				:  Any		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Any		       =     np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

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







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  str		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				subfolder='''scheduler'''      ,				revision='''onnx'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     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__				:  str		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  Union[str, Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  Optional[Any]		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Tuple		       =     output.images
        lowerCAmelCase__				:  Optional[Any]		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Optional[int]		       =     np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

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

