


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE       :		int         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	List[Any]									="""data2vec-text"""
   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-12   ,							UpperCamelCase_=1   ,							UpperCamelCase_=0   ,							UpperCamelCase_=2   ,							UpperCamelCase_="absolute"   ,							UpperCamelCase_=True   ,							UpperCamelCase_=None   ,							**UpperCamelCase_   ,							):
        super().__init__(pad_token_id=UpperCamelCase_   ,							bos_token_id=UpperCamelCase_   ,							eos_token_id=UpperCamelCase_   ,							**UpperCamelCase_		)

        lowercase_       :List[str]     =       vocab_size
        lowercase_       :Tuple     =       hidden_size
        lowercase_       :List[str]     =       num_hidden_layers
        lowercase_       :str     =       num_attention_heads
        lowercase_       :Tuple     =       hidden_act
        lowercase_       :Optional[Any]     =       intermediate_size
        lowercase_       :Dict     =       hidden_dropout_prob
        lowercase_       :str     =       attention_probs_dropout_prob
        lowercase_       :Any     =       max_position_embeddings
        lowercase_       :Any     =       type_vocab_size
        lowercase_       :str     =       initializer_range
        lowercase_       :List[str]     =       layer_norm_eps
        lowercase_       :List[Any]     =       position_embedding_type
        lowercase_       :Any     =       use_cache
        lowercase_       :Union[str, Any]     =       classifier_dropout






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @property
   def 		UpperCamelCase  (     self		):
        if self.task == "multiple-choice":
             lowercase_       :str     =       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
             lowercase_       :List[Any]     =       {0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ]		)





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

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

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

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

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

