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_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	torch.Tensor:






      '''simple docstring'''





      if not inplace:
            UpperCAmelCase     :			str     	=ma + ma
      else:
            ma += ma

      return ma





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	torch.Tensor:






      '''simple docstring'''





      UpperCAmelCase     :			Dict     	=-1 * len(__lowerCAmelCase    )
      UpperCAmelCase     :			Dict     	=list(range(len(tensor.shape[:zero_index]    )    )    )
      return tensor.permute(first_inds + [zero_index + i for i in inds]    )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	torch.Tensor:






      '''simple docstring'''





      return t.reshape(t.shape[:-no_dims] + (-1,)    )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = 1e-4    )->	torch.Tensor:






      '''simple docstring'''





      UpperCAmelCase     :			List[str]     	=mask.expand(*value.shape    )
      return torch.sum(mask * value		,       dim=__lowerCAmelCase    ) / (eps + torch.sum(__lowerCAmelCase		,       dim=__lowerCAmelCase    ))





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase = 2.3125		,       __lowerCAmelCase = 21.6875		,       __lowerCAmelCase = 64    )->	torch.Tensor:






      '''simple docstring'''





      UpperCAmelCase     :			Union[str, Any]     	=torch.linspace(__lowerCAmelCase		,       __lowerCAmelCase		,       no_bins - 1		,       device=pts.device    )
      UpperCAmelCase     :			int     	=torch.sqrt(torch.sum((pts.unsqueeze(-2    ) - pts.unsqueeze(-3    )) ** 2		,       dim=-1    )    )
      return torch.bucketize(__lowerCAmelCase		,       __lowerCAmelCase    )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	dict:






      '''simple docstring'''





      UpperCAmelCase     :			str     	=dicts[0]
      UpperCAmelCase     :			Any     	={}
      for k, v in first.items():
            UpperCAmelCase     :			List[Any]     	=[d[k] for d in dicts]
            if isinstance(__lowerCAmelCase		,       __lowerCAmelCase    ):
                  UpperCAmelCase     :			str     	=dict_multimap(__lowerCAmelCase		,       __lowerCAmelCase    )
            else:
                  UpperCAmelCase     :			List[Any]     	=fn(__lowerCAmelCase    )

      return new_dict





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	torch.Tensor:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	=v_bins.view(((1,) * len(x.shape    )) + (len(__lowerCAmelCase    ),)    )
      UpperCAmelCase     :			Union[str, Any]     	=x[..., None] - reshaped_bins
      UpperCAmelCase     :			int     	=torch.argmin(torch.abs(__lowerCAmelCase    )		,       dim=-1    )
      return nn.functional.one_hot(__lowerCAmelCase		,       num_classes=len(__lowerCAmelCase    )    ).float()





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = 0		,       __lowerCAmelCase = 0    )->	torch.Tensor:






      '''simple docstring'''





      UpperCAmelCase     :			List[Union[slice, torch.Tensor]]     	=[]
      for i, s in enumerate(data.shape[:no_batch_dims]    ):
            UpperCAmelCase     :			List[str]     	=torch.arange(__lowerCAmelCase    )
            UpperCAmelCase     :			Optional[Any]     	=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape    ) - i - 1)))    )
            ranges.append(__lowerCAmelCase    )

      UpperCAmelCase     :			List[Union[slice, torch.Tensor]]     	=[slice(__lowerCAmelCase    ) for _ in range(len(data.shape    ) - no_batch_dims    )]
      UpperCAmelCase     :			Optional[int]     	=inds
      ranges.extend(__lowerCAmelCase    )
      # 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(__lowerCAmelCase    )]


__snake_case         =     TypeVar('''T''')





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Dict[Any, Union[dict, list, tuple, Any]]:






      '''simple docstring'''





      UpperCAmelCase     :			Dict[Any, Union[dict, list, tuple, Any]]     	={}
      for k, v in dic.items():
            if isinstance(__lowerCAmelCase		,       __lowerCAmelCase    ):
                  UpperCAmelCase     :			Optional[Any]     	=dict_map(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )
            else:
                  UpperCAmelCase     :			List[str]     	=tree_map(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )

      return new_dict





@overload
def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Any:






      '''simple docstring'''





      ...





@overload
def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	dict:






      '''simple docstring'''





      ...





@overload
def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	list:






      '''simple docstring'''





      ...





@overload
def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	tuple:






      '''simple docstring'''





      ...





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[int]:






      '''simple docstring'''





      if isinstance(__lowerCAmelCase		,       __lowerCAmelCase    ):
            return dict_map(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )
      elif isinstance(__lowerCAmelCase		,       __lowerCAmelCase    ):
            return [tree_map(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    ) for x in tree]
      elif isinstance(__lowerCAmelCase		,       __lowerCAmelCase    ):
            return tuple(tree_map(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    ) for x in tree    )
      elif isinstance(__lowerCAmelCase		,       __lowerCAmelCase    ):
            return fn(__lowerCAmelCase    )
      else:
            print(type(__lowerCAmelCase    )    )
            raise ValueError('''Not supported'''    )


__snake_case         =     partial(tree_map, leaf_type=torch.Tensor)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__snake_case         =     {
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MobileViTV2ForImageClassification''',
         '''MobileViTV2ForSemanticSegmentation''',
         '''MobileViTV2Model''',
         '''MobileViTV2PreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_mobilevitva import (
         MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileViTVaConfig,
         MobileViTVaOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevitva import (
              MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTVaForImageClassification,
              MobileViTVaForSemanticSegmentation,
              MobileViTVaModel,
              MobileViTVaPreTrainedModel,
          )

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
