





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

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD



class 					__lowerCAmelCase       (			nn.Module     ):
			lowerCamelCase_     :     int
			lowerCamelCase_     :     int
			lowerCamelCase_     :     float       			=      0.0
			lowerCamelCase_     :     int       			=      1
			lowerCamelCase_     :     int       			=      1
			lowerCamelCase_     :     bool       			=      True
			lowerCamelCase_     :     bool       			=      False
			lowerCamelCase_     :     bool       			=      False
			lowerCamelCase_     :     bool       			=      False
			lowerCamelCase_     :     jnp.dtype       			=      jnp.floataa






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    List[str]				=  []
							snake_case_						:    Union[str, Any]				=  []

							for i in range(self.num_layers    ):
											snake_case_						:    Tuple				=  self.in_channels if i == 0 else self.out_channels

											snake_case_						:    List[Any]				=  FlaxResnetBlockaD(
											    in_channels=__magic_name__					,							out_channels=self.out_channels					,							dropout_prob=self.dropout					,							dtype=self.dtype					,							)
											resnets.append(__magic_name__    )

											snake_case_						:    List[str]				=  FlaxTransformeraDModel(
											    in_channels=self.out_channels					,							n_heads=self.num_attention_heads					,							d_head=self.out_channels // self.num_attention_heads					,							depth=1					,							use_linear_projection=self.use_linear_projection					,							only_cross_attention=self.only_cross_attention					,							use_memory_efficient_attention=self.use_memory_efficient_attention					,							dtype=self.dtype					,							)
											attentions.append(__magic_name__    )

							snake_case_						:    List[Any]				=  resnets
							snake_case_						:    int				=  attentions

							if self.add_downsample:
											snake_case_						:    str				=  FlaxDownsampleaD(self.out_channels					,							dtype=self.dtype    )






			def __call__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=True    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    str				=  ()

							for resnet, attn in zip(self.resnets					,							self.attentions    ):
											snake_case_						:    Union[str, Any]				=  resnet(__magic_name__					,							__magic_name__					,							deterministic=__magic_name__    )
											snake_case_						:    Tuple				=  attn(__magic_name__					,							__magic_name__					,							deterministic=__magic_name__    )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:    Dict				=  self.downsamplers_a(__magic_name__    )
											output_states += (hidden_states,)

							return hidden_states, output_states




class 					__lowerCAmelCase       (			nn.Module     ):
			lowerCamelCase_     :     int
			lowerCamelCase_     :     int
			lowerCamelCase_     :     float       			=      0.0
			lowerCamelCase_     :     int       			=      1
			lowerCamelCase_     :     bool       			=      True
			lowerCamelCase_     :     jnp.dtype       			=      jnp.floataa






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    int				=  []

							for i in range(self.num_layers    ):
											snake_case_						:    Optional[int]				=  self.in_channels if i == 0 else self.out_channels

											snake_case_						:    Optional[Any]				=  FlaxResnetBlockaD(
											    in_channels=__magic_name__					,							out_channels=self.out_channels					,							dropout_prob=self.dropout					,							dtype=self.dtype					,							)
											resnets.append(__magic_name__    )
							snake_case_						:    Union[str, Any]				=  resnets

							if self.add_downsample:
											snake_case_						:    Any				=  FlaxDownsampleaD(self.out_channels					,							dtype=self.dtype    )






			def __call__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__=True    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  ()

							for resnet in self.resnets:
											snake_case_						:    List[str]				=  resnet(__magic_name__					,							__magic_name__					,							deterministic=__magic_name__    )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:    Tuple				=  self.downsamplers_a(__magic_name__    )
											output_states += (hidden_states,)

							return hidden_states, output_states




class 					__lowerCAmelCase       (			nn.Module     ):
			lowerCamelCase_     :     int
			lowerCamelCase_     :     int
			lowerCamelCase_     :     int
			lowerCamelCase_     :     float       			=      0.0
			lowerCamelCase_     :     int       			=      1
			lowerCamelCase_     :     int       			=      1
			lowerCamelCase_     :     bool       			=      True
			lowerCamelCase_     :     bool       			=      False
			lowerCamelCase_     :     bool       			=      False
			lowerCamelCase_     :     bool       			=      False
			lowerCamelCase_     :     jnp.dtype       			=      jnp.floataa






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  []
							snake_case_						:    str				=  []

							for i in range(self.num_layers    ):
											snake_case_						:    int				=  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:    Any				=  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:    Optional[int]				=  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels					,							out_channels=self.out_channels					,							dropout_prob=self.dropout					,							dtype=self.dtype					,							)
											resnets.append(__magic_name__    )

											snake_case_						:    Dict				=  FlaxTransformeraDModel(
											    in_channels=self.out_channels					,							n_heads=self.num_attention_heads					,							d_head=self.out_channels // self.num_attention_heads					,							depth=1					,							use_linear_projection=self.use_linear_projection					,							only_cross_attention=self.only_cross_attention					,							use_memory_efficient_attention=self.use_memory_efficient_attention					,							dtype=self.dtype					,							)
											attentions.append(__magic_name__    )

							snake_case_						:    Dict				=  resnets
							snake_case_						:    List[str]				=  attentions

							if self.add_upsample:
											snake_case_						:    List[str]				=  FlaxUpsampleaD(self.out_channels					,							dtype=self.dtype    )






			def __call__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=True    )     ->   List[Any]:


							'''simple docstring'''



							for resnet, attn in zip(self.resnets					,							self.attentions    ):
											# pop res hidden states
											snake_case_						:    Optional[Any]				=  res_hidden_states_tuple[-1]
											snake_case_						:    List[str]				=  res_hidden_states_tuple[:-1]
											snake_case_						:    List[str]				=  jnp.concatenate((hidden_states, res_hidden_states)					,							axis=-1    )

											snake_case_						:    Any				=  resnet(__magic_name__					,							__magic_name__					,							deterministic=__magic_name__    )
											snake_case_						:    Optional[int]				=  attn(__magic_name__					,							__magic_name__					,							deterministic=__magic_name__    )

							if self.add_upsample:
											snake_case_						:    Union[str, Any]				=  self.upsamplers_a(__magic_name__    )

							return hidden_states




class 					__lowerCAmelCase       (			nn.Module     ):
			lowerCamelCase_     :     int
			lowerCamelCase_     :     int
			lowerCamelCase_     :     int
			lowerCamelCase_     :     float       			=      0.0
			lowerCamelCase_     :     int       			=      1
			lowerCamelCase_     :     bool       			=      True
			lowerCamelCase_     :     jnp.dtype       			=      jnp.floataa






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    int				=  []

							for i in range(self.num_layers    ):
											snake_case_						:    Optional[int]				=  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:    Union[str, Any]				=  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:    str				=  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels					,							out_channels=self.out_channels					,							dropout_prob=self.dropout					,							dtype=self.dtype					,							)
											resnets.append(__magic_name__    )

							snake_case_						:    Optional[Any]				=  resnets

							if self.add_upsample:
											snake_case_						:    str				=  FlaxUpsampleaD(self.out_channels					,							dtype=self.dtype    )






			def __call__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=True    )     ->   Any:


							'''simple docstring'''



							for resnet in self.resnets:
											# pop res hidden states
											snake_case_						:    List[str]				=  res_hidden_states_tuple[-1]
											snake_case_						:    List[str]				=  res_hidden_states_tuple[:-1]
											snake_case_						:    Dict				=  jnp.concatenate((hidden_states, res_hidden_states)					,							axis=-1    )

											snake_case_						:    Dict				=  resnet(__magic_name__					,							__magic_name__					,							deterministic=__magic_name__    )

							if self.add_upsample:
											snake_case_						:    Tuple				=  self.upsamplers_a(__magic_name__    )

							return hidden_states




class 					__lowerCAmelCase       (			nn.Module     ):
			lowerCamelCase_     :     int
			lowerCamelCase_     :     float       			=      0.0
			lowerCamelCase_     :     int       			=      1
			lowerCamelCase_     :     int       			=      1
			lowerCamelCase_     :     bool       			=      False
			lowerCamelCase_     :     bool       			=      False
			lowerCamelCase_     :     jnp.dtype       			=      jnp.floataa






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Tuple				=  [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels					,							out_channels=self.in_channels					,							dropout_prob=self.dropout					,							dtype=self.dtype					,							)
							]

							snake_case_						:    List[Any]				=  []

							for _ in range(self.num_layers    ):
											snake_case_						:    Any				=  FlaxTransformeraDModel(
											    in_channels=self.in_channels					,							n_heads=self.num_attention_heads					,							d_head=self.in_channels // self.num_attention_heads					,							depth=1					,							use_linear_projection=self.use_linear_projection					,							use_memory_efficient_attention=self.use_memory_efficient_attention					,							dtype=self.dtype					,							)
											attentions.append(__magic_name__    )

											snake_case_						:    str				=  FlaxResnetBlockaD(
											    in_channels=self.in_channels					,							out_channels=self.in_channels					,							dropout_prob=self.dropout					,							dtype=self.dtype					,							)
											resnets.append(__magic_name__    )

							snake_case_						:    Dict				=  resnets
							snake_case_						:    List[Any]				=  attentions






			def __call__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=True    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.resnets[0](__magic_name__					,							__magic_name__    )
							for attn, resnet in zip(self.attentions					,							self.resnets[1:]    ):
											snake_case_						:    Tuple				=  attn(__magic_name__					,							__magic_name__					,							deterministic=__magic_name__    )
											snake_case_						:    List[str]				=  resnet(__magic_name__					,							__magic_name__					,							deterministic=__magic_name__    )

							return hidden_states







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				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.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

	lowerCAmelCase_    =				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)
