





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase_     :						List[str]						=					logging.get_logger(__name__)

class  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : int	    =   """maskformer-swin"""

      lowercase_      : str	    =   {
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }

      def __init__(     self					,      snake_case_=2_2_4					,      snake_case_=4					,      snake_case_=3					,      snake_case_=9_6					,      snake_case_=[2, 2, 6, 2]					,      snake_case_=[3, 6, 1_2, 2_4]					,      snake_case_=7					,      snake_case_=4.0					,      snake_case_=True					,      snake_case_=0.0					,      snake_case_=0.0					,      snake_case_=0.1					,      snake_case_="gelu"					,      snake_case_=False					,      snake_case_=0.02					,      snake_case_=1E-5					,      snake_case_=None					,      snake_case_=None					,      **snake_case_					,      ):

        """simple docstring"""

        super().__init__(**snake_case_  )

        A_  :       List[Any]										=  image_size
        A_  :       str										=  patch_size
        A_  :       Dict										=  num_channels
        A_  :       Optional[Any]										=  embed_dim
        A_  :       Tuple										=  depths
        A_  :       Optional[Any]										=  len(snake_case_  )
        A_  :       Union[str, Any]										=  num_heads
        A_  :       Optional[int]										=  window_size
        A_  :       str										=  mlp_ratio
        A_  :       List[str]										=  qkv_bias
        A_  :       Optional[int]										=  hidden_dropout_prob
        A_  :       str										=  attention_probs_dropout_prob
        A_  :       Optional[Any]										=  drop_path_rate
        A_  :       Dict										=  hidden_act
        A_  :       Any										=  use_absolute_embeddings
        A_  :       Optional[int]										=  layer_norm_eps
        A_  :       Optional[Any]										=  initializer_range
        # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        A_  :       Union[str, Any]										=  int(embed_dim * 2 ** (len(snake_case_  ) - 1)  )
        A_  :       Union[str, Any]										=  ['stem'] + [F"""stage{idx}""" for idx in range(1					,      len(snake_case_  ) + 1  )]
        A_						,       A_  :       int										=  get_aligned_output_features_output_indices(
            out_features=snake_case_					,      out_indices=snake_case_					,      stage_names=self.stage_names  )






"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


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

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

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

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

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