





'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)






'''simple docstring'''




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'''):
  lowercase__   :				Dict      =   {
      '''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:
  lowercase__   :				Any      =   {
      '''linear''': PIL.Image.LINEAR,
      '''bilinear''': PIL.Image.BILINEAR,
      '''bicubic''': PIL.Image.BICUBIC,
      '''lanczos''': PIL.Image.LANCZOS,
      '''nearest''': PIL.Image.NEAREST,
  }






def 	_lowerCAmelCase      (  __snake_case  :			Any      )					->					Optional[Any]:
     __A						:			Dict								=	(images / 2 + 0.5).clamp(0			,							1      )
     __A						:			str								=	images.cpu().permute(0			,							2			,							3			,							1      ).float().numpy()
     __A						:			Dict								=	numpy_to_pil(__snake_case      )
     return images







def 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Optional[Any]:
     if images.ndim == 3:
          __A						:			List[Any]								=	images[None, ...]
     __A						:			List[str]								=	(images * 2_55).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						:			str								=	[Image.fromarray(__snake_case      ) for image in images]

     return pil_images