

'''simple docstring'''






import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

lowerCAmelCase__											=	logging.get_logger(__name__)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[int]							=			'vision-encoder-decoder'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			True






  def __init__(      self   :     str					,**lowercase__   :     Optional[Any]			):
   super().__init__(**lowercase__			)
   if "encoder" not in kwargs or "decoder" not in kwargs:
    raise ValueError(
        F"A configuraton of type {self.model_type} cannot be instantiated because "
        F"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"			)

   __lowercase							  = kwargs.pop('''encoder'''			)
   __lowercase							  = encoder_config.pop('''model_type'''			)
   __lowercase							  = kwargs.pop('''decoder'''			)
   __lowercase							  = decoder_config.pop('''model_type'''			)

   __lowercase							  = AutoConfig.for_model(lowercase__					,**lowercase__			)
   __lowercase							  = AutoConfig.for_model(lowercase__					,**lowercase__			)
   __lowercase							  = True






  @classmethod
  def 					SCREAMING_SNAKE_CASE      (      cls   :     Optional[Any]					,lowercase__   :     PretrainedConfig					,lowercase__   :     PretrainedConfig					,**lowercase__   :     str			):
   logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
   __lowercase							  = True
   __lowercase							  = True

   return cls(encoder=encoder_config.to_dict()					,decoder=decoder_config.to_dict()					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = copy.deepcopy(self.__dict__			)
   __lowercase							  = self.encoder.to_dict()
   __lowercase							  = self.decoder.to_dict()
   __lowercase							  = self.__class__.model_type
   return output






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Dict							=			version.parse('1.11'      )






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   return OrderedDict(
       [
           ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
       ]			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return 1e-4






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}			)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = OrderedDict()
   __lowercase							  = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
   __lowercase							  = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
   __lowercase							  = {0: '''batch''', 1: '''encoder_sequence'''}

   return common_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     "PreTrainedTokenizerBase"					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     Optional["TensorType"] = None					,):
   import torch

   __lowercase							  = OrderedDict()

   __lowercase							  = super().generate_dummy_inputs(
       lowercase__					,batch_size=lowercase__					,seq_length=lowercase__					,is_pair=lowercase__					,framework=lowercase__			)

   __lowercase  ,	__lowercase							  = dummy_input['''input_ids'''].shape
   __lowercase							  = (batch, encoder_sequence, self._config.encoder_hidden_size)
   __lowercase							  = dummy_input.pop('''input_ids'''			)
   __lowercase							  = dummy_input.pop('''attention_mask'''			)
   __lowercase							  = torch.zeros(lowercase__			)

   return common_inputs






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     PretrainedConfig			):
   return VisionEncoderDecoderEncoderOnnxConfig(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     PretrainedConfig					,lowercase__   :     PretrainedConfig					,lowercase__   :     str = "default"			):
   __lowercase							  = encoder_config.hidden_size
   return VisionEncoderDecoderDecoderOnnxConfig(lowercase__					,lowercase__			)




'''simple docstring'''






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)

