

from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case							:	Optional[int]											=       HfArgumentParser(InitializationArguments)
__snake_case							:	Union[str, Any]											=       parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case							:	Optional[int]											=       AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case							:	Optional[int]											=       {
    '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)
__snake_case							:	Dict											=       AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case							:	str											=       AutoModelForCausalLM.from_config(config)

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




'''simple docstring'''




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

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)





@dataclass
class  A				(							__snake_case						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipeline_if import IFPipeline
      from .pipeline_if_imgaimg import IFImgaImgPipeline
      from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
      from .pipeline_if_inpainting import IFInpaintingPipeline
      from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
      from .pipeline_if_superresolution import IFSuperResolutionPipeline
      from .safety_checker import IFSafetyChecker
      from .watermark import IFWatermarker




