
"""simple docstring"""




import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class 		a__						(				nn.Linear						):
   def __init__(      self     ,   _a     ,   _a     ,   _a = True     ,   _a = "constant"     ,   _a = 0.0     ,   _a = "topK"     ,   ):
          super(_a     ,   self    ).__init__(in_features=_a     ,   out_features=_a     ,   bias=_a    )
          assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
          lowercase   :    List[Any]			=							pruning_method

          if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                 lowercase   :    Union[str, Any]			=							mask_scale
                 lowercase   :    Optional[Any]			=							mask_init
                 lowercase   :    List[Any]			=							nn.Parameter(torch.empty(self.weight.size()    )    )
                 self.init_mask()
   def       __magic_name__      (      self    ):
          if self.mask_init == "constant":
                 init.constant_(self.mask_scores     ,   val=self.mask_scale    )
          elif self.mask_init == "uniform":
                 init.uniform_(self.mask_scores     ,   a=-self.mask_scale     ,   b=self.mask_scale    )
          elif self.mask_init == "kaiming":
                 init.kaiming_uniform_(self.mask_scores     ,   a=math.sqrt(5    )    )

   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          # Get the mask
          if self.pruning_method == "topK":
                 lowercase   :    List[str]			=							TopKBinarizer.apply(self.mask_scores     ,   _a    )
          elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                 lowercase   :    Any			=							"sigmoied" in self.pruning_method
                 lowercase   :    str			=							ThresholdBinarizer.apply(self.mask_scores     ,   _a     ,   _a    )
          elif self.pruning_method == "magnitude":
                 lowercase   :    Tuple			=							MagnitudeBinarizer.apply(self.weight     ,   _a    )
          elif self.pruning_method == "l0":
                 lowercase   :    List[str]			=							-0.1, 1.1, 2 / 3
                 if self.training:
                        lowercase   :    List[Any]			=							torch.zeros_like(self.mask_scores    ).uniform_().clamp(0.0_0_0_1     ,   0.9_9_9_9    )
                        lowercase   :    Tuple			=							torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b    )
                 else:
                        lowercase   :    List[str]			=							torch.sigmoid(self.mask_scores    )
                 lowercase   :    List[Any]			=							s * (r - l) + l
                 lowercase   :    Dict			=							s_bar.clamp(min=0.0     ,   max=1.0    )
          # Mask weights with computed mask
          lowercase   :    Dict			=							mask * self.weight
          # Compute output (linear layer) with masked weights
          return nn.functional.linear(_a     ,   _a     ,   self.bias    )




"""simple docstring"""




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


_A      :	str       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """tvlt"""
   def __init__(      self     ,   _a=224     ,   _a=2_048     ,   _a=128     ,   _a=[16, 16]     ,   _a=[16, 16]     ,   _a=3     ,   _a=1     ,   _a=8     ,   _a=768     ,   _a=12     ,   _a=12     ,   _a=3_072     ,   _a="gelu"     ,   _a=0.0     ,   _a=0.0     ,   _a=0.0_2     ,   _a=1E-6     ,   _a=True     ,   _a=False     ,   _a=16     ,   _a=512     ,   _a=8     ,   _a=2_048     ,   _a=0.7_5     ,   _a=0.1_5     ,   _a="frame-level"     ,   _a=True     ,   _a=True     ,   _a="classification"     ,   **_a     ,   ):
          super().__init__(**_a    )

          if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
                     f"""got {audio_mask_type}"""    )

          lowercase   :    Union[str, Any]			=							image_size
          lowercase   :    Dict			=							spectrogram_length
          lowercase   :    Union[str, Any]			=							frequency_length
          lowercase   :    Union[str, Any]			=							image_patch_size
          lowercase   :    Any			=							audio_patch_size
          lowercase   :    int			=							num_image_channels
          lowercase   :    Union[str, Any]			=							num_audio_channels
          lowercase   :    Any			=							num_frames

          lowercase   :    List[str]			=							hidden_size
          lowercase   :    List[str]			=							num_hidden_layers
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    int			=							intermediate_size
          lowercase   :    Tuple			=							hidden_act
          lowercase   :    int			=							hidden_dropout_prob
          lowercase   :    Dict			=							attention_probs_dropout_prob
          lowercase   :    List[Any]			=							initializer_range
          lowercase   :    Union[str, Any]			=							layer_norm_eps
          lowercase   :    Dict			=							qkv_bias
          lowercase   :    List[Any]			=							use_mean_pooling

          lowercase   :    List[Any]			=							decoder_num_attention_heads
          lowercase   :    Union[str, Any]			=							decoder_hidden_size
          lowercase   :    Dict			=							decoder_num_hidden_layers
          lowercase   :    Union[str, Any]			=							decoder_intermediate_size
          lowercase   :    List[str]			=							pixel_mask_ratio
          lowercase   :    str			=							audio_mask_ratio
          lowercase   :    Any			=							audio_mask_type

          lowercase   :    Union[str, Any]			=							task_matching
          lowercase   :    Optional[Any]			=							task_mae
          lowercase   :    List[str]			=							loss_type


