



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


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

_lowercase		:				Dict      		={
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}

class        snake_case__    (A__ ,     A__		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					"convnextv2"

						def __init__(       self ,       __lowercase=3 ,       __lowercase=4 ,       __lowercase=4 ,       __lowercase=None ,       __lowercase=None ,       __lowercase="gelu" ,       __lowercase=0.0_2 ,       __lowercase=1E-12 ,       __lowercase=0.0 ,       __lowercase=2_2_4 ,       __lowercase=None ,       __lowercase=None ,       **__lowercase ,       ) ->				str:
									"""simple docstring"""




									super().__init__(**__lowercase  )

									a__       :						List[str]							=  num_channels
									a__       :						int							=  patch_size
									a__       :						Optional[Any]							=  num_stages
									a__       :						Optional[int]							=  [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
									a__       :						Any							=  [3, 3, 9, 3] if depths is None else depths
									a__       :						Any							=  hidden_act
									a__       :						Tuple							=  initializer_range
									a__       :						Dict							=  layer_norm_eps
									a__       :						Any							=  drop_path_rate
									a__       :						List[str]							=  image_size
									a__       :						List[str]							=  ["""stem"""] + [F'''stage{idx}''' for idx in range(1 ,       len(self.depths  ) + 1  )]
									a__	,						a__       :						Optional[Any]							=  get_aligned_output_features_output_indices(
									    out_features=__lowercase ,       out_indices=__lowercase ,       stage_names=self.stage_names  )









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




