




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


a_ 		=  logging.get_logger(__name__)

a_ 		=  {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Any     =       "qdqbert"







      def __init__(  self       :			Optional[Any]			,							snake_case       :			List[Any]=30522			,							snake_case       :			Tuple=768			,							snake_case       :			int=12			,							snake_case       :			Union[str, Any]=12			,							snake_case       :			Any=3072			,							snake_case       :			Optional[Any]="gelu"			,							snake_case       :			str=0.1			,							snake_case       :			str=0.1			,							snake_case       :			Dict=512			,							snake_case       :			Union[str, Any]=2			,							snake_case       :			Optional[Any]=0.02			,							snake_case       :			Dict=1E-12			,							snake_case       :			Optional[int]=True			,							snake_case       :			Union[str, Any]=1			,							snake_case       :			Union[str, Any]=0			,							snake_case       :			int=2			,							**snake_case       :			Union[str, Any]			,							):
          super().__init__(pad_token_id=snake_case			,							bos_token_id=snake_case			,							eos_token_id=snake_case			,							**snake_case					)

          __UpperCamelCase			=    vocab_size
          __UpperCamelCase			=    max_position_embeddings
          __UpperCamelCase			=    hidden_size
          __UpperCamelCase			=    num_hidden_layers
          __UpperCamelCase			=    num_attention_heads
          __UpperCamelCase			=    intermediate_size
          __UpperCamelCase			=    hidden_act
          __UpperCamelCase			=    hidden_dropout_prob
          __UpperCamelCase			=    attention_probs_dropout_prob
          __UpperCamelCase			=    initializer_range
          __UpperCamelCase			=    type_vocab_size
          __UpperCamelCase			=    layer_norm_eps
          __UpperCamelCase			=    use_cache







import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				Any 			=    (inputs > threshold).type(inputs.type()			)
           if mask.sum() < nb_min:
               # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				List[Any] 			=    mask.flatten()
           __snake_case       :				int 			=    0
           __snake_case       :				Optional[int] 			=    1
           return mask



