






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
 __UpperCamelCase      :       Any      			=      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.""",
 )

 __UpperCamelCase      :       Optional[Any]      			=      parser.parse_args()

 __UpperCamelCase      :       int      			=      UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

 __UpperCamelCase      :       str      			=      CLIPImageProcessor()
 __UpperCamelCase      :       Dict      			=      CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

 __UpperCamelCase      :       Optional[Any]      			=      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)







'''simple docstring'''



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
 import torch

if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm


__UpperCamelCase      :       Optional[Any]      			=      logging.get_logger(__name__)
@dataclass
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=[
          "no_inference",
          "no_cuda",
          "no_tpu",
          "no_speed",
          "no_memory",
          "no_env_print",
          "no_multi_process",
      ]




      def __init__(  self ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
               __a				     =    deprecated_arg[3:]
               setattr(self ,   lowerCamelCase ,   not kwargs.pop(lowerCamelCase      )      )
               logger.warning(
                   F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                   F""" {positive_arg}={kwargs[positive_arg]}"""      )

         __a				     =    kwargs.pop('torchscript' ,   self.torchscript      )
         __a				     =    kwargs.pop('torch_xla_tpu_print_metrics' ,   self.torch_xla_tpu_print_metrics      )
         __a				     =    kwargs.pop('fp16_opt_level' ,   self.fpaa_opt_level      )
         super().__init__(**lowerCamelCase      )

      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Trace the models using torchscript"}     )
      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Print Xla/PyTorch tpu metrics"}     )
      __a  					=field(
          default="O1"      ,      metadata={
              "help": (
                  "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
                  "See details at https://nvidia.github.io/apex/amp.html"
              )
          }      ,      )




      @cached_property
      def  __UpperCamelCase							(  self      )							->Tuple["torch.device", int]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         logger.info('PyTorch: setting up devices'      )
         if not self.cuda:
            __a				     =    torch.device('cpu'      )
            __a				     =    0
         elif is_torch_tpu_available():
            __a				     =    xm.xla_device()
            __a				     =    0
         else:
            __a				     =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
            __a				     =    torch.cuda.device_count()
         return device, n_gpu




      @property
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         return is_torch_tpu_available() and self.tpu




      @property
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         # TODO(PVP): currently only single GPU is supported
         return torch.cuda.current_device()




      @property
      def  __UpperCamelCase							(  self      )							->"torch.device":



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[0]




      @property
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[1]






      @property
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         return self.n_gpu > 0