
"""simple docstring"""




import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm


def   __magic_name__     (				__snake_case    :	Tuple					,			__snake_case    :	Optional[int]  )			->    List[Any]:
       if isinstance(__snake_case					,			(list, tuple)  ):
              return honor_type(__snake_case					,			(move_to_device(__snake_case					,			__snake_case  ) for t in state)  )
       elif isinstance(__snake_case					,			__snake_case  ):
              return type(__snake_case  )({k: move_to_device(__snake_case					,			__snake_case  ) for k, v in state.items()}  )
       elif isinstance(__snake_case					,			torch.Tensor  ):
              return state.to(__snake_case  )
       return state

class 		a__						(				torch.optim.Optimizer						):
   def __init__(      self     ,   _a     ,   _a=True     ,   _a=None    ):
          lowercase   :    str			=							optimizer
          lowercase   :    Union[str, Any]			=							scaler
          lowercase   :    Optional[Any]			=							AcceleratorState()
          lowercase   :    Optional[int]			=							GradientState()
          lowercase   :    Optional[int]			=							device_placement
          lowercase   :    Any			=							False
          lowercase   :    Optional[Any]			=							None

          # Handle device placement
          if device_placement:
                 lowercase   :    Union[str, Any]			=							self.optimizer.state_dict()
                 if self.accelerator_state.distributed_type == DistributedType.TPU:
                        xm.send_cpu_data_to_device(_a     ,   self.accelerator_state.device    )
                 else:
                        lowercase   :    List[str]			=							move_to_device(_a     ,   self.accelerator_state.device    )
                 self.optimizer.load_state_dict(_a    )
   @property
   def       __magic_name__      (      self    ):
          return self.optimizer.state
   @state.setter
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Optional[Any]			=							state
   @property
   def       __magic_name__      (      self    ):
          return self.optimizer.param_groups
   @param_groups.setter
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    str			=							param_groups
   @property
   def       __magic_name__      (      self    ):
          return self.optimizer.defaults
   @defaults.setter
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    int			=							defaults
   def       __magic_name__      (      self     ,   _a    ):
          self.optimizer.add_param_group(_a    )
   def       __magic_name__      (      self     ,   _a    ):
          if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
                 xm.send_cpu_data_to_device(_a     ,   self.accelerator_state.device    )
          self.optimizer.load_state_dict(_a    )
   def       __magic_name__      (      self    ):
          return self.optimizer.state_dict()
   def       __magic_name__      (      self     ,   _a=None    ):
          if self.gradient_state.sync_gradients:
                 lowercase   :    Dict			=							"set_to_none" in inspect.signature(self.optimizer.zero_grad    ).parameters
                 if accept_arg:
                        if set_to_none is None:
                               lowercase   :    Union[str, Any]			=							False
                        self.optimizer.zero_grad(set_to_none=_a    )
                 else:
                        if set_to_none is not None:
                               raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."    )
                        self.optimizer.zero_grad()
   def       __magic_name__      (      self     ,   _a=None    ):
          if self.gradient_state.sync_gradients:
                 if self.accelerator_state.distributed_type == DistributedType.TPU:
                        lowercase   :    Union[str, Any]			=							{"closure": closure} if closure is not None else {}
                        xm.optimizer_step(self.optimizer     ,   optimizer_args=_a    )
                 elif self.scaler is not None:
                        lowercase   :    str			=							False
                        if self._last_scale is None:
                               # `get_scale` is an async operation requiring full synchronization
                               # on CPU and GPUs before finishing. As a result, we store away
                               # the prior one to reduce the call overhead
                               lowercase   :    Any			=							self.scaler.get_scale()
                               lowercase   :    Optional[int]			=							True
                        self.scaler.step(self.optimizer     ,   _a    )
                        self.scaler.update()
                        lowercase   :    Union[str, Any]			=							self.scaler.get_scale()
                        if not new_scale:
                               # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
                               lowercase   :    Dict			=							scale_after < self._last_scale
                        lowercase   :    Dict			=							scale_after

                 else:
                        self.optimizer.step(_a    )
   def       __magic_name__      (      self     ,   _a    ):
          for param_group in self.optimizer.param_groups:
                 lowercase   :    str			=							[parameters_map.get(_a     ,   _a    ) for p in param_group["params"]]
   @property
   def       __magic_name__      (      self    ):
          warnings.warn(
              "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
              "`optimizer.step_was_skipped` instead."     ,   _a     ,   )
          return self._is_overflow
   @property
   def       __magic_name__      (      self    ):
          return self._is_overflow
   def __getstate__(      self    ):
          return self.__dict__.copy()

   def __setstate__(      self     ,   _a    ):
          self.__dict__.update(_a    )




"""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


