






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





@dataclass
class 	SCREAMING_SNAKE_CASE					(					lowercase__       ):
			snake_case__  =				42





class 	SCREAMING_SNAKE_CASE					(					lowercase__	,   lowercase__       ):






			@register_to_config
			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int = 3  ,						__SCREAMING_SNAKE_CASE      :			int = 3  ,						__SCREAMING_SNAKE_CASE      :			Tuple[str] = ("DownEncoderBlock2D",)  ,						__SCREAMING_SNAKE_CASE      :			Tuple[str] = ("UpDecoderBlock2D",)  ,						__SCREAMING_SNAKE_CASE      :			Tuple[int] = (64,)  ,						__SCREAMING_SNAKE_CASE      :			int = 1  ,						__SCREAMING_SNAKE_CASE      :			str = "silu"  ,						__SCREAMING_SNAKE_CASE      :			int = 3  ,						__SCREAMING_SNAKE_CASE      :			int = 32  ,						__SCREAMING_SNAKE_CASE      :			int = 256  ,						__SCREAMING_SNAKE_CASE      :			int = 32  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						__SCREAMING_SNAKE_CASE      :			float = 0.1_8215  ,						__SCREAMING_SNAKE_CASE      :			str = "group"  ,						)			->			List[str]:
							super().__init__()

							# pass init params to Encoder
							a_		:  str											=      Encoder(
							    in_channels=UpperCAmelCase__  ,						out_channels=UpperCAmelCase__  ,						down_block_types=UpperCAmelCase__  ,						block_out_channels=UpperCAmelCase__  ,						layers_per_block=UpperCAmelCase__  ,						act_fn=UpperCAmelCase__  ,						norm_num_groups=UpperCAmelCase__  ,						double_z=UpperCAmelCase__  ,						)

							a_		:  List[str]											=      vq_embed_dim if vq_embed_dim is not None else latent_channels

							a_		:  Any											=      nn.Convad(UpperCAmelCase__  ,						UpperCAmelCase__  ,						1					)
							a_		:  List[str]											=      VectorQuantizer(UpperCAmelCase__  ,						UpperCAmelCase__  ,						beta=0.25  ,						remap=UpperCAmelCase__  ,						sane_index_shape=UpperCAmelCase__					)
							a_		:  Optional[int]											=      nn.Convad(UpperCAmelCase__  ,						UpperCAmelCase__  ,						1					)

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






			@apply_forward_hook
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			torch.FloatTensor  ,						__SCREAMING_SNAKE_CASE      :			bool = True					)			->			Union[str, Any]:
							a_		:  Any											=      self.encoder(UpperCAmelCase__					)
							a_		:  Tuple											=      self.quant_conv(UpperCAmelCase__					)

							if not return_dict:
											return (h,)

							return VQEncoderOutput(latents=UpperCAmelCase__					)






			@apply_forward_hook
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			torch.FloatTensor  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			bool = True					)			->			List[Any]:
							# also go through quantization layer
							if not force_not_quantize:
											a_		:  Union[str, Any]											=      self.quantize(UpperCAmelCase__					)
							else:
											a_		:  List[str]											=      h
							a_		:  Union[str, Any]											=      self.post_quant_conv(UpperCAmelCase__					)
							a_		:  int											=      self.decoder(UpperCAmelCase__  ,						quant if self.config.norm_type == '''spatial''' else None					)

							if not return_dict:
											return (dec,)

							return DecoderOutput(sample=UpperCAmelCase__					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			torch.FloatTensor  ,						__SCREAMING_SNAKE_CASE      :			bool = True					)			->			Any:
							a_		:  List[str]											=      sample
							a_		:  Optional[Any]											=      self.encode(UpperCAmelCase__					).latents
							a_		:  Tuple											=      self.decode(UpperCAmelCase__					).sample

							if not return_dict:
											return (dec,)

							return DecoderOutput(sample=UpperCAmelCase__					)












'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				IFInpaintingSuperResolutionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							return self._get_superresolution_dummy_components()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict=0					)			->			List[Any]:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Optional[int]											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      floats_tensor((1, 3, 16, 16)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''original_image''': original_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}

							return inputs






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()  ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''  ,						)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != '''cuda'''  ,						reason='''float16 requires CUDA'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[int]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Optional[Any]:
							self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Optional[Any]:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2  ,						)




