






'''simple docstring'''

from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin






@dataclass
class        a_		(    lowerCamelCase       ):
  lowercase  	=   42






class        a_		(    lowerCamelCase			, lowerCamelCase       ):

  @register_to_config
  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = 32    ,	_SCREAMING_SNAKE_CASE = 64    ,	_SCREAMING_SNAKE_CASE = 20    ,	_SCREAMING_SNAKE_CASE = 768    ,	_SCREAMING_SNAKE_CASE=77    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	_SCREAMING_SNAKE_CASE = "silu"    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = "linear"    ,	_SCREAMING_SNAKE_CASE = "prd"    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Union[str, Any]:



        """simple docstring"""






        super().__init__()
        UpperCamelCase       						=      num_attention_heads
        UpperCamelCase       						=      attention_head_dim
        UpperCamelCase       						=      num_attention_heads * attention_head_dim
        UpperCamelCase       						=      additional_embeddings

        UpperCamelCase       						=      time_embed_dim or inner_dim
        UpperCamelCase       						=      embedding_proj_dim or embedding_dim
        UpperCamelCase       						=      clip_embed_dim or embedding_dim

        UpperCamelCase       						=      Timesteps(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	0   )
        UpperCamelCase       						=      TimestepEmbedding(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	out_dim=_SCREAMING_SNAKE_CASE    ,	act_fn=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      nn.Linear(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        if embedding_proj_norm_type is None:
              UpperCamelCase       						=      None
        elif embedding_proj_norm_type == "layer":
              UpperCamelCase       						=      nn.LayerNorm(_SCREAMING_SNAKE_CASE   )
        else:
              raise ValueError(F"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"   )

        UpperCamelCase       						=      nn.Linear(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        if encoder_hid_proj_type is None:
              UpperCamelCase       						=      None
        elif encoder_hid_proj_type == "linear":
              UpperCamelCase       						=      nn.Linear(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        else:
              raise ValueError(F"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"   )

        UpperCamelCase       						=      nn.Parameter(torch.zeros(1    ,	num_embeddings + additional_embeddings    ,	_SCREAMING_SNAKE_CASE   )   )

        if added_emb_type == "prd":
              UpperCamelCase       						=      nn.Parameter(torch.zeros(1    ,	1    ,	_SCREAMING_SNAKE_CASE   )   )
        elif added_emb_type is None:
              UpperCamelCase       						=      None
        else:
              raise ValueError(
                  F"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."   )

        UpperCamelCase       						=      nn.ModuleList(
            [
                BasicTransformerBlock(
                    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	dropout=_SCREAMING_SNAKE_CASE    ,	activation_fn="""gelu"""    ,	attention_bias=_SCREAMING_SNAKE_CASE    ,	)
                for d in range(_SCREAMING_SNAKE_CASE   )
            ]   )

        if norm_in_type == "layer":
              UpperCamelCase       						=      nn.LayerNorm(_SCREAMING_SNAKE_CASE   )
        elif norm_in_type is None:
              UpperCamelCase       						=      None
        else:
              raise ValueError(F"Unsupported norm_in_type: {norm_in_type}."   )

        UpperCamelCase       						=      nn.LayerNorm(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      nn.Linear(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]    ,	-1_0_0_0_0.0   )
        causal_attention_mask.triu_(1   )
        UpperCamelCase       						=      causal_attention_mask[None, ...]
        self.register_buffer("""causal_attention_mask"""    ,	_SCREAMING_SNAKE_CASE    ,	persistent=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      nn.Parameter(torch.zeros(1    ,	_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      nn.Parameter(torch.zeros(1    ,	_SCREAMING_SNAKE_CASE   )   )

  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def 						A__		(   self   )						->   Dict[str, AttentionProcessor]:



        """simple docstring"""






        UpperCamelCase       						=      {}

        def fn_recursive_add_processors(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              if hasattr(_SCREAMING_SNAKE_CASE    ,	"""set_processor"""   ):
                    UpperCamelCase       						=      module.processor

              for sub_name, child in module.named_children():
                    fn_recursive_add_processors(F"{name}.{sub_name}"    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              return processors

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

        return processors

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      len(self.attn_processors.keys()   )

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

        def fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              if hasattr(_SCREAMING_SNAKE_CASE    ,	"""set_processor"""   ):
                    if not isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                          module.set_processor(_SCREAMING_SNAKE_CASE   )
                    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}"    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

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

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        self.set_attn_processor(AttnProcessor()   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = True    ,	)						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      hidden_states.shape[0]

        UpperCamelCase       						=      timestep
        if not torch.is_tensor(_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      torch.tensor([timesteps]    ,	dtype=torch.long    ,	device=hidden_states.device   )
        elif torch.is_tensor(_SCREAMING_SNAKE_CASE   ) and len(timesteps.shape   ) == 0:
              UpperCamelCase       						=      timesteps[None].to(hidden_states.device   )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        UpperCamelCase       						=      timesteps * torch.ones(_SCREAMING_SNAKE_CASE    ,	dtype=timesteps.dtype    ,	device=timesteps.device   )

        UpperCamelCase       						=      self.time_proj(_SCREAMING_SNAKE_CASE   )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        UpperCamelCase       						=      timesteps_projected.to(dtype=self.dtype   )
        UpperCamelCase       						=      self.time_embedding(_SCREAMING_SNAKE_CASE   )

        if self.embedding_proj_norm is not None:
              UpperCamelCase       						=      self.embedding_proj_norm(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.embedding_proj(_SCREAMING_SNAKE_CASE   )
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
              UpperCamelCase       						=      self.encoder_hidden_states_proj(_SCREAMING_SNAKE_CASE   )
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
              raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"""   )

        UpperCamelCase       						=      self.proj_in(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.positional_embedding.to(hidden_states.dtype   )

        UpperCamelCase       						=      []
        UpperCamelCase       						=      0

        if encoder_hidden_states is not None:
              additional_embeds.append(_SCREAMING_SNAKE_CASE   )
              additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape   ) == 2:
              UpperCamelCase       						=      proj_embeddings[:, None, :]

        if len(hidden_states.shape   ) == 2:
              UpperCamelCase       						=      hidden_states[:, None, :]

        UpperCamelCase       						=      additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
              UpperCamelCase       						=      self.prd_embedding.to(hidden_states.dtype   ).expand(_SCREAMING_SNAKE_CASE    ,	-1    ,	-1   )
              additional_embeds.append(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.cat(
            _SCREAMING_SNAKE_CASE    ,	dim=1    ,	)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        UpperCamelCase       						=      additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
              UpperCamelCase       						=      F.pad(
                  _SCREAMING_SNAKE_CASE    ,	(
                      0,
                      0,
                      additional_embeddings_len,
                      self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                  )    ,	value=0.0    ,	)

        UpperCamelCase       						=      hidden_states + positional_embeddings

        if attention_mask is not None:
              UpperCamelCase       						=      (1 - attention_mask.to(hidden_states.dtype   )) * -1_0_0_0_0.0
              UpperCamelCase       						=      F.pad(_SCREAMING_SNAKE_CASE    ,	(0, self.additional_embeddings)    ,	value=0.0   )
              UpperCamelCase       						=      (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype   )
              UpperCamelCase       						=      attention_mask.repeat_interleave(self.config.num_attention_heads    ,	dim=0   )

        if self.norm_in is not None:
              UpperCamelCase       						=      self.norm_in(_SCREAMING_SNAKE_CASE   )

        for block in self.transformer_blocks:
              UpperCamelCase       						=      block(_SCREAMING_SNAKE_CASE    ,	attention_mask=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.norm_out(_SCREAMING_SNAKE_CASE   )

        if self.prd_embedding is not None:
              UpperCamelCase       						=      hidden_states[:, -1]
        else:
              UpperCamelCase       						=      hidden_states[:, additional_embeddings_len:]

        UpperCamelCase       						=      self.proj_to_clip_embeddings(_SCREAMING_SNAKE_CASE   )

        if not return_dict:
              return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=_SCREAMING_SNAKE_CASE   )


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      (prior_latents * self.clip_std) + self.clip_mean
        return prior_latents








'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

     SCREAMING_SNAKE_CASE__											=     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)
