




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)












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)






