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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


snake_case :		List[str]							      =  logging.getLogger(__name__)





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    field(
			    default=0.0				,						metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}				)
			UpperCamelCase__          =    field(default=snake_case				,						metadata={'help': 'Whether to SortishSamler or not.'}				)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}				)
			UpperCamelCase__          =    field(default=snake_case				,						metadata={'help': 'whether to use adafactor'}				)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}				)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}				)
			UpperCamelCase__          =    field(default=snake_case				,						metadata={'help': 'Dropout probability. Goes into model.config.'}				)
			UpperCamelCase__          =    field(
			    default=snake_case				,						metadata={'help': 'Attention dropout probability. Goes into model.config.'}				)
			UpperCamelCase__          =    field(
			    default='linear'				,						metadata={'help': F'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'}				,						)


from collections import OrderedDict
from typing import Mapping

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


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'convbert'







			def __init__(				self						,   _a=30_522						,   _a=768						,   _a=12						,   _a=12						,   _a=3_072						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=2						,   _a=0.02						,   _a=1e-12						,   _a=1						,   _a=0						,   _a=2						,   _a=768						,   _a=2						,   _a=9						,   _a=1						,   _a=None						,   **_a						,   ):
				super().__init__(
				    pad_token_id=_a						,   bos_token_id=_a						,   eos_token_id=_a						,   **_a						,   )

				__magic_name__		:		Tuple	   =     vocab_size
				__magic_name__		:		List[Any]	   =     hidden_size
				__magic_name__		:		Union[str, Any]	   =     num_hidden_layers
				__magic_name__		:		List[Any]	   =     num_attention_heads
				__magic_name__		:		str	   =     intermediate_size
				__magic_name__		:		Any	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		Tuple	   =     max_position_embeddings
				__magic_name__		:		str	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     initializer_range
				__magic_name__		:		Tuple	   =     layer_norm_eps
				__magic_name__		:		List[Any]	   =     embedding_size
				__magic_name__		:		List[Any]	   =     head_ratio
				__magic_name__		:		str	   =     conv_kernel_size
				__magic_name__		:		Dict	   =     num_groups
				__magic_name__		:		str	   =     classifier_dropout







class       _snake_case					(     snake_case				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.task == "multiple-choice":
					__magic_name__		:		Dict	   =     {0: "batch", 1: "choice", 2: "sequence"}
				else:
					__magic_name__		:		Dict	   =     {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)
