

from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder

@dataclass
class 					a	(						__A					):
						_lowercase         =      4_2







class 					a	(						__A				,			__A					):
						_lowercase         =      True


						@register_to_config
						def __init__(							self    ,       A_ = 3    ,       A_ = 3    ,       A_ = ("DownEncoderBlock2D",)    ,       A_ = ("UpDecoderBlock2D",)    ,       A_ = (64,)    ,       A_ = 1    ,       A_ = "silu"    ,       A_ = 4    ,       A_ = 32    ,       A_ = 32    ,       A_ = 0.1_82_15    ,       ):







										'''simple docstring'''


										super().__init__()

										# pass init params to Encoder
										_UpperCAmelCase							:		List[Any]					=     Encoder(
										    in_channels=A_    ,       out_channels=A_    ,       down_block_types=A_    ,       block_out_channels=A_    ,       layers_per_block=A_    ,       act_fn=A_    ,       norm_num_groups=A_    ,       double_z=A_    ,       )

										# pass init params to Decoder
										_UpperCAmelCase							:		Dict					=     Decoder(
										    in_channels=A_    ,       out_channels=A_    ,       up_block_types=A_    ,       block_out_channels=A_    ,       layers_per_block=A_    ,       norm_num_groups=A_    ,       act_fn=A_    ,       )

										_UpperCAmelCase							:		Any					=     nn.Convad(2 * latent_channels    ,       2 * latent_channels    ,       1						)
										_UpperCAmelCase							:		Dict					=     nn.Convad(A_    ,       A_    ,       1						)

										_UpperCAmelCase							:		Any					=     False
										_UpperCAmelCase							:		Optional[Any]					=     False

										# only relevant if vae tiling is enabled
										_UpperCAmelCase							:		Dict					=     self.config.sample_size
										_UpperCAmelCase							:		int					=     (
										    self.config.sample_size[0]
										    if isinstance(self.config.sample_size    ,       (list, tuple)						)
										    else self.config.sample_size
										)
										_UpperCAmelCase							:		Union[str, Any]					=     int(sample_size / (2 ** (len(self.config.block_out_channels						) - 1))						)
										_UpperCAmelCase							:		Union[str, Any]					=     0.25


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_=False						):







										'''simple docstring'''


										if isinstance(A_    ,       (Encoder, Decoder)						):
														_UpperCAmelCase							:		Any					=     value


						def 							_UpperCAmelCase					(							self    ,       A_ = True						):







										'''simple docstring'''


										_UpperCAmelCase							:		Optional[int]					=     use_tiling


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										self.enable_tiling(A_						)


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Tuple					=     True


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		str					=     False


						@property
						# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Any					=     {}

										def fn_recursive_add_processors(A_    ,       A_    ,       A_						):
														if hasattr(A_    ,       "set_processor"						):
																		_UpperCAmelCase							:		Any					=     module.processor

														for sub_name, child in module.named_children():
																		fn_recursive_add_processors(f'{name}.{sub_name}'    ,       A_    ,       A_						)

														return processors

										for name, module in self.named_children():
														fn_recursive_add_processors(A_    ,       A_    ,       A_						)

										return processors


						def 							_UpperCAmelCase					(							self    ,       A_						):







										'''simple docstring'''


										_UpperCAmelCase							:		Optional[Any]					=     len(self.attn_processors.keys()						)

										if isinstance(A_    ,       A_						) and len(A_						) != count:
														raise ValueError(
														    f'A dict of processors was passed, but the number of processors {len(A_						)} does not match the'
														    f' number of attention layers: {count}. Please make sure to pass {count} processor classes.'						)

										def fn_recursive_attn_processor(A_    ,       A_    ,       A_						):
														if hasattr(A_    ,       "set_processor"						):
																		if not isinstance(A_    ,       A_						):
																						module.set_processor(A_						)
																		else:
																						module.set_processor(processor.pop(f'{name}.processor'						)						)

														for sub_name, child in module.named_children():
																		fn_recursive_attn_processor(f'{name}.{sub_name}'    ,       A_    ,       A_						)

										for name, module in self.named_children():
														fn_recursive_attn_processor(A_    ,       A_    ,       A_						)


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										self.set_attn_processor(AttnProcessor()						)


						@apply_forward_hook
						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_ = True						):







										'''simple docstring'''


										if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
														return self.tiled_encode(A_    ,       return_dict=A_						)

										if self.use_slicing and x.shape[0] > 1:
														_UpperCAmelCase							:		str					=     [self.encoder(A_						) for x_slice in x.split(1						)]
														_UpperCAmelCase							:		Any					=     torch.cat(A_						)
										else:
														_UpperCAmelCase							:		Optional[Any]					=     self.encoder(A_						)

										_UpperCAmelCase							:		Any					=     self.quant_conv(A_						)
										_UpperCAmelCase							:		Optional[Any]					=     DiagonalGaussianDistribution(A_						)

										if not return_dict:
														return (posterior,)

										return AutoencoderKLOutput(latent_dist=A_						)


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_ = True						):







										'''simple docstring'''


										if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
														return self.tiled_decode(A_    ,       return_dict=A_						)

										_UpperCAmelCase							:		int					=     self.post_quant_conv(A_						)
										_UpperCAmelCase							:		List[Any]					=     self.decoder(A_						)

										if not return_dict:
														return (dec,)

										return DecoderOutput(sample=A_						)


						@apply_forward_hook
						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_ = True						):







										'''simple docstring'''


										if self.use_slicing and z.shape[0] > 1:
														_UpperCAmelCase							:		List[str]					=     [self._decode(A_						).sample for z_slice in z.split(1						)]
														_UpperCAmelCase							:		Any					=     torch.cat(A_						)
										else:
														_UpperCAmelCase							:		Optional[int]					=     self._decode(A_						).sample

										if not return_dict:
														return (decoded,)

										return DecoderOutput(sample=A_						)


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_    ,       A_						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[str]					=     min(a.shape[2]    ,       b.shape[2]    ,       A_						)
										for y in range(A_						):
														_UpperCAmelCase							:		List[str]					=     a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
										return b


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_    ,       A_						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[str]					=     min(a.shape[3]    ,       b.shape[3]    ,       A_						)
										for x in range(A_						):
														_UpperCAmelCase							:		Tuple					=     a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
										return b


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_ = True						):







										'''simple docstring'''


										_UpperCAmelCase							:		Dict					=     int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)						)
										_UpperCAmelCase							:		Optional[Any]					=     int(self.tile_latent_min_size * self.tile_overlap_factor						)
										_UpperCAmelCase							:		Dict					=     self.tile_latent_min_size - blend_extent

										# Split the image into 512x512 tiles and encode them separately.
										_UpperCAmelCase							:		Tuple					=     []
										for i in range(0    ,       x.shape[2]    ,       A_						):
														_UpperCAmelCase							:		Dict					=     []
														for j in range(0    ,       x.shape[3]    ,       A_						):
																		_UpperCAmelCase							:		Dict					=     x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																		_UpperCAmelCase							:		Union[str, Any]					=     self.encoder(A_						)
																		_UpperCAmelCase							:		Optional[int]					=     self.quant_conv(A_						)
																		row.append(A_						)
														rows.append(A_						)
										_UpperCAmelCase							:		Tuple					=     []
										for i, row in enumerate(A_						):
														_UpperCAmelCase							:		List[str]					=     []
														for j, tile in enumerate(A_						):
																		# blend the above tile and the left tile
																		# to the current tile and add the current tile to the result row
																		if i > 0:
																						_UpperCAmelCase							:		Optional[Any]					=     self.blend_v(rows[i - 1][j]    ,       A_    ,       A_						)
																		if j > 0:
																						_UpperCAmelCase							:		str					=     self.blend_h(row[j - 1]    ,       A_    ,       A_						)
																		result_row.append(tile[:, :, :row_limit, :row_limit]						)
														result_rows.append(torch.cat(A_    ,       dim=3						)						)

										_UpperCAmelCase							:		List[str]					=     torch.cat(A_    ,       dim=2						)
										_UpperCAmelCase							:		List[str]					=     DiagonalGaussianDistribution(A_						)

										if not return_dict:
														return (posterior,)

										return AutoencoderKLOutput(latent_dist=A_						)


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_ = True						):







										'''simple docstring'''


										_UpperCAmelCase							:		Dict					=     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)						)
										_UpperCAmelCase							:		List[str]					=     int(self.tile_sample_min_size * self.tile_overlap_factor						)
										_UpperCAmelCase							:		Dict					=     self.tile_sample_min_size - blend_extent

										# Split z into overlapping 64x64 tiles and decode them separately.
										# The tiles have an overlap to avoid seams between tiles.
										_UpperCAmelCase							:		str					=     []
										for i in range(0    ,       z.shape[2]    ,       A_						):
														_UpperCAmelCase							:		Optional[Any]					=     []
														for j in range(0    ,       z.shape[3]    ,       A_						):
																		_UpperCAmelCase							:		str					=     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																		_UpperCAmelCase							:		Optional[int]					=     self.post_quant_conv(A_						)
																		_UpperCAmelCase							:		List[Any]					=     self.decoder(A_						)
																		row.append(A_						)
														rows.append(A_						)
										_UpperCAmelCase							:		Any					=     []
										for i, row in enumerate(A_						):
														_UpperCAmelCase							:		Optional[int]					=     []
														for j, tile in enumerate(A_						):
																		# blend the above tile and the left tile
																		# to the current tile and add the current tile to the result row
																		if i > 0:
																						_UpperCAmelCase							:		List[str]					=     self.blend_v(rows[i - 1][j]    ,       A_    ,       A_						)
																		if j > 0:
																						_UpperCAmelCase							:		List[str]					=     self.blend_h(row[j - 1]    ,       A_    ,       A_						)
																		result_row.append(tile[:, :, :row_limit, :row_limit]						)
														result_rows.append(torch.cat(A_    ,       dim=3						)						)

										_UpperCAmelCase							:		Dict					=     torch.cat(A_    ,       dim=2						)
										if not return_dict:
														return (dec,)

										return DecoderOutput(sample=A_						)





						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_ = False    ,       A_ = True    ,       A_ = None    ,       ):







										'''simple docstring'''


										_UpperCAmelCase							:		Dict					=     sample
										_UpperCAmelCase							:		Optional[int]					=     self.encode(A_						).latent_dist
										if sample_posterior:
														_UpperCAmelCase							:		Optional[int]					=     posterior.sample(generator=A_						)
										else:
														_UpperCAmelCase							:		Tuple					=     posterior.mode()
										_UpperCAmelCase							:		List[str]					=     self.decode(A_						).sample

										if not return_dict:
														return (dec,)

										return DecoderOutput(sample=A_						)





import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			a_      =     argparse.ArgumentParser()

			parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

			parser.add_argument(
			    '--txt2img_unclip',
			    default='kakaobrain/karlo-v1-alpha',
			    type=str,
			    required=False,
			    help='The pretrained txt2img unclip.',
			)

			a_      =     parser.parse_args()

			a_      =     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			a_      =     CLIPImageProcessor()
			a_      =     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

			a_      =     UnCLIPImageVariationPipeline(
			    decoder=txtaimg.decoder,
			    text_encoder=txtaimg.text_encoder,
			    tokenizer=txtaimg.tokenizer,
			    text_proj=txtaimg.text_proj,
			    feature_extractor=feature_extractor,
			    image_encoder=image_encoder,
			    super_res_first=txtaimg.super_res_first,
			    super_res_last=txtaimg.super_res_last,
			    decoder_scheduler=txtaimg.decoder_scheduler,
			    super_res_scheduler=txtaimg.super_res_scheduler,
			)

			imgaimg.save_pretrained(args.dump_path)