

'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


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

lowerCamelCase				:   Optional[Any]      				=  {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class 							A__					( A__						):
  A__					=       MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  A__					=       'mobilebert'





  def __init__(	self   :			Optional[Any]							,      _a   :			Optional[Any]=3_0522							,      _a   :			Optional[Any]=512							,      _a   :			str=24							,      _a   :			List[str]=4							,      _a   :			Tuple=512							,      _a   :			Any="relu"							,      _a   :			Optional[Any]=0.0							,      _a   :			str=0.1							,      _a   :			List[Any]=512							,      _a   :			Any=2							,      _a   :			Any=0.02							,      _a   :			Tuple=1e-12							,      _a   :			Union[str, Any]=0							,      _a   :			str=128							,      _a   :			List[Any]=True							,      _a   :			List[str]=True							,      _a   :			Tuple=128							,      _a   :			Optional[Any]=False							,      _a   :			Any=True							,      _a   :			Union[str, Any]=4							,      _a   :			Optional[Any]="no_norm"							,      _a   :			Optional[Any]=True							,      _a   :			str=None							,      **_a   :			Any							,      )			->					Tuple:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       _SCREAMING_SNAKE_CASE												=embedding_size
       _SCREAMING_SNAKE_CASE												=trigram_input
       _SCREAMING_SNAKE_CASE												=use_bottleneck
       _SCREAMING_SNAKE_CASE												=intra_bottleneck_size
       _SCREAMING_SNAKE_CASE												=use_bottleneck_attention
       _SCREAMING_SNAKE_CASE												=key_query_shared_bottleneck
       _SCREAMING_SNAKE_CASE												=num_feedforward_networks
       _SCREAMING_SNAKE_CASE												=normalization_type
       _SCREAMING_SNAKE_CASE												=classifier_activation

       if self.use_bottleneck:
            _SCREAMING_SNAKE_CASE												=intra_bottleneck_size
       else:
            _SCREAMING_SNAKE_CASE												=hidden_size

       _SCREAMING_SNAKE_CASE												=classifier_dropout







class 							A__					( A__						):





  @property
  def 		A						(	self   :			List[Any]				)			->					Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






       if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE												={0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            _SCREAMING_SNAKE_CASE												={0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]				)






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


lowerCamelCase				:   Dict      				=  logging.get_logger(__name__)

lowerCamelCase				:   Dict      				=  {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}







class 							A__					( A__						):
  A__					=       'nystromformer'





  def __init__(	self   :			List[str]							,      _a   :			Dict=3_0000							,      _a   :			Dict=768							,      _a   :			Union[str, Any]=12							,      _a   :			Optional[int]=12							,      _a   :			Union[str, Any]=3072							,      _a   :			Optional[Any]="gelu_new"							,      _a   :			Dict=0.1							,      _a   :			Dict=0.1							,      _a   :			Union[str, Any]=510							,      _a   :			Any=2							,      _a   :			List[Any]=64							,      _a   :			int=64							,      _a   :			Optional[Any]=65							,      _a   :			str=False							,      _a   :			List[Any]=0.02							,      _a   :			List[str]=1e-5							,      _a   :			Optional[Any]=1							,      _a   :			Optional[Any]=0							,      _a   :			List[Any]=2							,      **_a   :			List[Any]							,      )			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=segment_means_seq_len
       _SCREAMING_SNAKE_CASE												=num_landmarks
       _SCREAMING_SNAKE_CASE												=conv_kernel_size
       _SCREAMING_SNAKE_CASE												=inv_coeff_init_option
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       super().__init__(pad_token_id=_a							,      bos_token_id=_a							,      eos_token_id=_a							,      **_a				)
