


'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


__snake_case				 =	logging.get_logger(__name__)

# TODO: upload to AWS
__snake_case				 =	{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							'retribert'




		def __init__(						self       ,  UpperCamelCase_=30522       ,  UpperCamelCase_=768       ,  UpperCamelCase_=8       ,  UpperCamelCase_=12       ,  UpperCamelCase_=3072       ,  UpperCamelCase_="gelu"       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=512       ,  UpperCamelCase_=2       ,  UpperCamelCase_=0.02       ,  UpperCamelCase_=1e-12       ,  UpperCamelCase_=True       ,  UpperCamelCase_=128       ,  UpperCamelCase_=0       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_       ,  **UpperCamelCase_				)

							UpperCamelCase__  :Optional[int]				   =					vocab_size
							UpperCamelCase__  :int				   =					hidden_size
							UpperCamelCase__  :Union[str, Any]				   =					num_hidden_layers
							UpperCamelCase__  :str				   =					num_attention_heads
							UpperCamelCase__  :Dict				   =					hidden_act
							UpperCamelCase__  :Optional[int]				   =					intermediate_size
							UpperCamelCase__  :Dict				   =					hidden_dropout_prob
							UpperCamelCase__  :int				   =					attention_probs_dropout_prob
							UpperCamelCase__  :int				   =					max_position_embeddings
							UpperCamelCase__  :str				   =					type_vocab_size
							UpperCamelCase__  :int				   =					initializer_range
							UpperCamelCase__  :Optional[int]				   =					layer_norm_eps
							UpperCamelCase__  :Dict				   =					share_encoders
							UpperCamelCase__  :Dict				   =					projection_dim



'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

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