"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__SCREAMING_SNAKE_CASE    	=HfArgumentParser(InitializationArguments)
__SCREAMING_SNAKE_CASE    	=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__SCREAMING_SNAKE_CASE    	=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__SCREAMING_SNAKE_CASE    	={
    '''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)
__SCREAMING_SNAKE_CASE    	=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__SCREAMING_SNAKE_CASE    	=AutoModelForCausalLM.from_config(config)

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





snake_case				:    str       				=     '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

snake_case				:    List[Any]       				=     [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
snake_case				:    int       				=     {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





