


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__        =				logging.get_logger(__name__)

UpperCAmelCase__        =				{
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     '''regnet'''
     SCREAMING_SNAKE_CASE__      =     ['''x''', '''y''']





     def __init__(self						,	UpperCAmelCase=3						,	UpperCAmelCase=3_2						,	UpperCAmelCase=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						,	UpperCAmelCase=[2, 6, 1_2, 2]						,	UpperCAmelCase=6_4						,	UpperCAmelCase="y"						,	UpperCAmelCase="relu"						,	**UpperCAmelCase						,	)	->  Tuple:
      super().__init__(**UpperCAmelCase			)
      if layer_type not in self.layer_types:
       raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
      _lowercase				 =num_channels
      _lowercase				 =embedding_size
      _lowercase				 =hidden_sizes
      _lowercase				 =depths
      _lowercase				 =groups_width
      _lowercase				 =layer_type
      _lowercase				 =hidden_act
      # always downsample in the first stage
      _lowercase				 =True





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__        =				logging.get_logger(__name__)

UpperCAmelCase__        =				{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     '''rembert'''





     def __init__(self						,	UpperCAmelCase=2_5_0_3_0_0						,	UpperCAmelCase=1_1_5_2						,	UpperCAmelCase=3_2						,	UpperCAmelCase=1_8						,	UpperCAmelCase=2_5_6						,	UpperCAmelCase=1_6_6_4						,	UpperCAmelCase=4_6_0_8						,	UpperCAmelCase="gelu"						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.1						,	UpperCAmelCase=5_1_2						,	UpperCAmelCase=2						,	UpperCAmelCase=0.02						,	UpperCAmelCase=1e-12						,	UpperCAmelCase=True						,	UpperCAmelCase=0						,	UpperCAmelCase=3_1_2						,	UpperCAmelCase=3_1_3						,	**UpperCAmelCase						,	)	->  Tuple:
      super().__init__(pad_token_id=UpperCAmelCase						,	bos_token_id=UpperCAmelCase						,	eos_token_id=UpperCAmelCase						,	**UpperCAmelCase			)

      _lowercase				 =vocab_size
      _lowercase				 =input_embedding_size
      _lowercase				 =output_embedding_size
      _lowercase				 =max_position_embeddings
      _lowercase				 =hidden_size
      _lowercase				 =num_hidden_layers
      _lowercase				 =num_attention_heads
      _lowercase				 =intermediate_size
      _lowercase				 =hidden_act
      _lowercase				 =hidden_dropout_prob
      _lowercase				 =attention_probs_dropout_prob
      _lowercase				 =classifier_dropout_prob
      _lowercase				 =initializer_range
      _lowercase				 =type_vocab_size
      _lowercase				 =layer_norm_eps
      _lowercase				 =use_cache
      _lowercase				 =False







class  lowerCamelCase__  (			lowerCAmelCase):





     @property
     def 	__A   (self			)	->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       _lowercase				 ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       _lowercase				 ={0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]			)





     @property
     def 	__A   (self			)	->  float:
      return 1e-4

