






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       __lowercase      (	lowercase_  ):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				42


class       __lowercase      (	lowercase_			,      lowercase_  ):







					'''simple docstring'''




					@register_to_config
					def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			Tuple[str] = ("DownEncoderBlock2D",) ,	UpperCamelCase_       :			Tuple[str] = ("UpDecoderBlock2D",) ,	UpperCamelCase_       :			Tuple[int] = (64,) ,	UpperCamelCase_       :			int = 1 ,	UpperCamelCase_       :			str = "silu" ,	UpperCamelCase_       :			int = 3 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			int = 256 ,	UpperCamelCase_       :			int = 32 ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			float = 0.18215 ,	UpperCamelCase_       :			str = "group" ,	):


									"""simple docstring"""



									super().__init__()

									# pass init params to Encoder
									__A         =	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         =	vq_embed_dim if vq_embed_dim is not None else latent_channels

									__A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)
									__A         =	VectorQuantizer(UpperCamelCase_ ,	UpperCamelCase_ ,	beta=0.25 ,	remap=UpperCamelCase_ ,	sane_index_shape=UpperCamelCase_				)
									__A         =	nn.Convad(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)

									# pass init params to Decoder
									__A         =	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 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


									"""simple docstring"""



									__A         =	self.encoder(UpperCamelCase_				)
									__A         =	self.quant_conv(UpperCamelCase_				)

									if not return_dict:
													return (h,)

									return VQEncoderOutput(latents=UpperCamelCase_				)




					@apply_forward_hook
					def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = True				):


									"""simple docstring"""



									if not force_not_quantize:
													__A    ,	__A    ,	__A         =	self.quantize(UpperCamelCase_				)
									else:
													__A         =	h
									__A         =	self.post_quant_conv(UpperCamelCase_				)
									__A         =	self.decoder(UpperCamelCase_ ,	quant if self.config.norm_type == """spatial""" else None				)

									if not return_dict:
													return (dec,)

									return DecoderOutput(sample=UpperCamelCase_				)





					def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			bool = True				):


									"""simple docstring"""



									__A         =	sample
									__A         =	self.encode(UpperCamelCase_				).latents
									__A         =	self.decode(UpperCamelCase_				).sample

									if not return_dict:
													return (dec,)

									return DecoderOutput(sample=UpperCamelCase_				)












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      ImageTextPipelineOutput,
      UniDiffuserPipeline,
  )
else:
  from .modeling_text_decoder import UniDiffuserTextDecoder
  from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
  from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




