'''simple docstring'''


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


snake_case__	:     Tuple      =    logging.get_logger(__name__)

snake_case__	:     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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:str		       =						'''nystromformer'''


  def __init__(					self ,						snake_case_=3_0_0_0_0 ,						snake_case_=7_6_8 ,						snake_case_=1_2 ,						snake_case_=1_2 ,						snake_case_=3_0_7_2 ,						snake_case_="gelu_new" ,						snake_case_=0.1 ,						snake_case_=0.1 ,						snake_case_=5_1_0 ,						snake_case_=2 ,						snake_case_=6_4 ,						snake_case_=6_4 ,						snake_case_=6_5 ,						snake_case_=False ,						snake_case_=0.02 ,						snake_case_=1E-5 ,						snake_case_=1 ,						snake_case_=0 ,						snake_case_=2 ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = vocab_size
       UpperCAmelCase_				:  Dict  = max_position_embeddings
       UpperCAmelCase_				:  Union[str, Any]  = hidden_size
       UpperCAmelCase_				:  Tuple  = num_hidden_layers
       UpperCAmelCase_				:  Union[str, Any]  = num_attention_heads
       UpperCAmelCase_				:  List[str]  = intermediate_size
       UpperCAmelCase_				:  List[Any]  = hidden_act
       UpperCAmelCase_				:  int  = hidden_dropout_prob
       UpperCAmelCase_				:  Any  = attention_probs_dropout_prob
       UpperCAmelCase_				:  Tuple  = initializer_range
       UpperCAmelCase_				:  int  = type_vocab_size
       UpperCAmelCase_				:  Optional[int]  = segment_means_seq_len
       UpperCAmelCase_				:  List[str]  = num_landmarks
       UpperCAmelCase_				:  List[Any]  = conv_kernel_size
       UpperCAmelCase_				:  List[str]  = inv_coeff_init_option
       UpperCAmelCase_				:  Optional[Any]  = layer_norm_eps
       super().__init__(pad_token_id=snake_case_ ,						bos_token_id=snake_case_ ,						eos_token_id=snake_case_ ,						**snake_case_		)











'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


SCREAMING_SNAKE_CASE__     : int				=   {"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : List[Any]				=   ["""BertGenerationTokenizer"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   [
							    """BertGenerationDecoder""",
							    """BertGenerationEncoder""",
							    """BertGenerationPreTrainedModel""",
							    """load_tf_weights_in_bert_generation""",
							]


if TYPE_CHECKING:
							from .configuration_bert_generation import BertGenerationConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_generation import BertGenerationTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert_generation import (
														    BertGenerationDecoder,
														    BertGenerationEncoder,
														    BertGenerationPreTrainedModel,
														    load_tf_weights_in_bert_generation,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__     : Any				=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
