





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						str     		=				field(
       metadata={"""help""": """The output directory where the model will be written."""}		,)
   _UpperCamelCase :						str     		=				field(
       metadata={
           """help""": (
               """The encoder model checkpoint for weights initialization."""
               """Don't set if you want to train an encoder model from scratch."""
           )
       }		,)
   _UpperCamelCase :						str     		=				field(
       metadata={
           """help""": (
               """The decoder model checkpoint for weights initialization."""
               """Don't set if you want to train a decoder model from scratch."""
           )
       }		,)
   _UpperCamelCase :						Optional[str]     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}  )







def       _A  (      ):
   a__	:						List[Any]      		=  HfArgumentParser((ModelArguments,)   )
   ((a__)		,     )	:						Tuple      		=  parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

   # Use explicit specified encoder config
   if model_args.encoder_config_name:
      a__	:						Tuple      		=  AutoConfig.from_pretrained(model_args.encoder_config_name   )
   # Use pretrained encoder model's config
   else:
      a__	:						List[str]      		=  AutoConfig.from_pretrained(model_args.encoder_model_name_or_path   )

   # Use explicit specified decoder config
   if model_args.decoder_config_name:
      a__	:						int      		=  AutoConfig.from_pretrained(model_args.decoder_config_name   )
   # Use pretrained decoder model's config
   else:
      a__	:						List[Any]      		=  AutoConfig.from_pretrained(model_args.decoder_model_name_or_path   )

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   a__	:						List[str]      		=  True
   a__	:						Optional[Any]      		=  True

   a__	:						Dict      		=  FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
       encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path							, decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path							, encoder_config=lowerCamelCase							, decoder_config=lowerCamelCase							, )

   # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
   a__	:						Dict      		=  decoder_config.decoder_start_token_id
   a__	:						List[str]      		=  decoder_config.pad_token_id
   if decoder_start_token_id is None:
      a__	:						Optional[Any]      		=  decoder_config.bos_token_id
   if pad_token_id is None:
      a__	:						Dict      		=  decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   a__	:						Any      		=  decoder_config.eos_token_id
   a__	:						Dict      		=  decoder_start_token_id
   a__	:						Optional[int]      		=  pad_token_id

   a__	:						int      		=  AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

   a__	:						Optional[int]      		=  AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path   )
   a__	:						List[Any]      		=  tokenizer.convert_ids_to_tokens(model.config.pad_token_id   )

   model.save_pretrained(model_args.output_dir   )
   image_processor.save_pretrained(model_args.output_dir   )
   tokenizer.save_pretrained(model_args.output_dir   )


if __name__ == "__main__":
  main()








import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

