





'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__   :				Any      =   logging.get_logger(__name__)

lowercase__   :				Optional[Any]      =   {
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}


class 			SCREAMING_SNAKE_CASE   (__UpperCamelCase							):
     lowerCAmelCase		      =	'''markuplm'''


     def __init__(						self						,     _UpperCAmelCase=3_0522						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=0						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase=256						,     _UpperCAmelCase=1024						,     _UpperCAmelCase=216						,     _UpperCAmelCase=1001						,     _UpperCAmelCase=32						,     _UpperCAmelCase=50						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__(
              pad_token_id=_UpperCAmelCase						,     bos_token_id=_UpperCAmelCase						,     eos_token_id=_UpperCAmelCase						,     **_UpperCAmelCase						,     )
          __A						:			str								=	vocab_size
          __A						:			Any								=	hidden_size
          __A						:			List[Any]								=	num_hidden_layers
          __A						:			Optional[Any]								=	num_attention_heads
          __A						:			List[str]								=	hidden_act
          __A						:			Optional[Any]								=	intermediate_size
          __A						:			Tuple								=	hidden_dropout_prob
          __A						:			Union[str, Any]								=	attention_probs_dropout_prob
          __A						:			Optional[int]								=	max_position_embeddings
          __A						:			Optional[Any]								=	type_vocab_size
          __A						:			Tuple								=	initializer_range
          __A						:			int								=	layer_norm_eps
          __A						:			Dict								=	position_embedding_type
          __A						:			Optional[Any]								=	use_cache
          __A						:			Dict								=	classifier_dropout
          # additional properties
          __A						:			Optional[Any]								=	max_depth
          __A						:			List[Any]								=	max_xpath_tag_unit_embeddings
          __A						:			str								=	max_xpath_subs_unit_embeddings
          __A						:			Optional[Any]								=	tag_pad_id
          __A						:			str								=	subs_pad_id
          __A						:			int								=	xpath_unit_hidden_size






'''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)