code: 
  
  
  
  
  from functools import partial
  from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
  
  import torch
  import torch.nn as nn
  import torch.types
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							if not inplace:
  														SCREAMING_SNAKE_CASE			: Any	     =ma + ma
  							else:
  														ma += ma
  
  							return ma
  def 						lowerCAmelCase_       (	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =-1 * len(lowercase_ )
  							SCREAMING_SNAKE_CASE			: Any	     =list(range(len(tensor.shape[:zero_index] ) ) )
  							return tensor.permute(first_inds + [zero_index + i for i in inds] )
  def 						lowerCAmelCase_       (	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							return t.reshape(t.shape[:-no_dims] + (-1,) )
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a = 1E-4 ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =mask.expand(*value.shape )
  							return torch.sum(mask * value	,	dim=lowercase_ ) / (eps + torch.sum(lowercase_	,	dim=lowercase_ ))
  def 						lowerCAmelCase_       (	__a	,	__a = 2.3125	,	__a = 21.6875	,	__a = 64 ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =torch.linspace(lowercase_	,	lowercase_	,	no_bins - 1	,	device=pts.device )
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2	,	dim=-1 ) )
  							return torch.bucketize(lowercase_	,	lowercase_ )
  def 						lowerCAmelCase_       (	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =dicts[0]
  							SCREAMING_SNAKE_CASE			: str	     ={}
  							for k, v in first.items():
  														SCREAMING_SNAKE_CASE			: List[Any]	     =[d[k] for d in dicts]
  														if isinstance(lowercase_	,	lowercase_ ):
  																					SCREAMING_SNAKE_CASE			: Any	     =dict_multimap(lowercase_	,	lowercase_ )
  														else:
  																					SCREAMING_SNAKE_CASE			: Dict	     =fn(lowercase_ )
  
  							return new_dict
  def 						lowerCAmelCase_       (	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =v_bins.view(((1,) * len(x.shape )) + (len(lowercase_ ),) )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =x[..., None] - reshaped_bins
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.argmin(torch.abs(lowercase_ )	,	dim=-1 )
  							return nn.functional.one_hot(lowercase_	,	num_classes=len(lowercase_ ) ).float()
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a = 0	,	__a = 0 ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[Union[slice, torch.Tensor]]	     =[]
  							for i, s in enumerate(data.shape[:no_batch_dims] ):
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.arange(lowercase_ )
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
  														ranges.append(lowercase_ )
  
  							SCREAMING_SNAKE_CASE			: List[Union[slice, torch.Tensor]]	     =[slice(lowercase_ ) for _ in range(len(data.shape ) - no_batch_dims )]
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =inds
  							ranges.extend(lowercase_ )
  							# Matt note: Editing this to get around the behaviour of using a list as an array index changing
  							# in recent Numpy versions
  							return data[tuple(lowercase_ )]
  
  
  _A							    =	TypeVar("""T""")
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict[Any, Union[dict, list, tuple, Any]]	     ={}
  							for k, v in dic.items():
  														if isinstance(lowercase_	,	lowercase_ ):
  																					SCREAMING_SNAKE_CASE			: Dict	     =dict_map(lowercase_	,	lowercase_	,	lowercase_ )
  														else:
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =tree_map(lowercase_	,	lowercase_	,	lowercase_ )
  
  							return new_dict
  @overload
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							...
  @overload
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							...
  @overload
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							...
  @overload
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							...
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							if isinstance(lowercase_	,	lowercase_ ):
  														return dict_map(lowercase_	,	lowercase_	,	lowercase_ )
  							elif isinstance(lowercase_	,	lowercase_ ):
  														return [tree_map(lowercase_	,	lowercase_	,	lowercase_ ) for x in tree]
  							elif isinstance(lowercase_	,	lowercase_ ):
  														return tuple(tree_map(lowercase_	,	lowercase_	,	lowercase_ ) for x in tree )
  							elif isinstance(lowercase_	,	lowercase_ ):
  														return fn(lowercase_ )
  							else:
  														print(type(lowercase_ ) )
  														raise ValueError('''Not supported''' )
  _A							    =	partial(tree_map, leaf_type=torch.Tensor)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  