


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 	A (		A__		,		A__		,		A__			):
     # The first operation in a checkpoint can't be in-place, but it's
     # nice to have in-place addition during inference. Thus...
     if not inplace:
          UpperCAmelCase    :					Tuple			=      ma + ma
     else:
          ma += ma

     return ma







def 	A (		A__		,		A__			):
     UpperCAmelCase    :					Union[str, Any]			=      -1 * len(A__			)
     UpperCAmelCase    :					List[str]			=      list(range(len(tensor.shape[:zero_index]			)			)			)
     return tensor.permute(first_inds + [zero_index + i for i in inds]			)







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







def 	A (		A__		,		A__		,		A__		,		A__ = 1E-4			):
     UpperCAmelCase    :					Tuple			=      mask.expand(*value.shape			)
     return torch.sum(mask * value		,		dim=A__			) / (eps + torch.sum(A__		,		dim=A__			))







def 	A (		A__		,		A__ = 2.3125		,		A__ = 21.6875		,		A__ = 6_4			):
     UpperCAmelCase    :					str			=      torch.linspace(A__		,		A__		,		no_bins - 1		,		device=pts.device			)
     UpperCAmelCase    :					str			=      torch.sqrt(torch.sum((pts.unsqueeze(-2			) - pts.unsqueeze(-3			)) ** 2		,		dim=-1			)			)
     return torch.bucketize(A__		,		A__			)







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

     return new_dict







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







def 	A (		A__		,		A__		,		A__ = 0		,		A__ = 0			):
     UpperCAmelCase    :					List[Union[slice, torch.Tensor]]			=      []
     for i, s in enumerate(data.shape[:no_batch_dims]			):
          UpperCAmelCase    :					Any			=      torch.arange(A__			)
          UpperCAmelCase    :					Optional[Any]			=      r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape			) - i - 1)))			)
          ranges.append(A__			)

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


_snake_case				=			TypeVar("T")







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

     return new_dict







@overload
def 	A (		A__		,		A__		,		A__			):
     ...







@overload
def 	A (		A__		,		A__		,		A__			):
     ...







@overload
def 	A (		A__		,		A__		,		A__			):
     ...







@overload
def 	A (		A__		,		A__		,		A__			):
     ...







def 	A (		A__		,		A__		,		A__			):
     if isinstance(A__		,		A__			):
          return dict_map(A__		,		A__		,		A__			)
     elif isinstance(A__		,		A__			):
          return [tree_map(A__		,		A__		,		A__			) for x in tree]
     elif isinstance(A__		,		A__			):
          return tuple(tree_map(A__		,		A__		,		A__			) for x in tree			)
     elif isinstance(A__		,		A__			):
          return fn(A__			)
     else:
          print(type(A__			)			)
          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_oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig"],
    "processing_oneformer": ["OneFormerProcessor"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			["OneFormerImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "OneFormerForUniversalSegmentation",
          "OneFormerModel",
          "OneFormerPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure)



