






"""simple docstring"""





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(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       if not inplace:
              lowercase__:							Optional[int]         =     ma + ma
       else:
              ma += ma

       return ma






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							str         =     -1 * len(snake_case_						)
       lowercase__:							int         =     list(range(len(tensor.shape[:zero_index]						)						)						)
       return tensor.permute(first_inds + [zero_index + i for i in inds]						)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





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






def 							A(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_ = 1E-4						):
       """simple docstring"""





       lowercase__:							List[Any]         =     mask.expand(*value.shape						)
       return torch.sum(mask * value		,  dim=snake_case_						) / (eps + torch.sum(snake_case_		,  dim=snake_case_						))






def 							A(    snake_case_		,  snake_case_ = 2.31_25		,  snake_case_ = 21.68_75		,  snake_case_ = 64						):
       """simple docstring"""





       lowercase__:							Tuple         =     torch.linspace(snake_case_		,  snake_case_		,  no_bins - 1		,  device=pts.device						)
       lowercase__:							Dict         =     torch.sqrt(torch.sum((pts.unsqueeze(-2						) - pts.unsqueeze(-3						)) ** 2		,  dim=-1						)						)
       return torch.bucketize(snake_case_		,  snake_case_						)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





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

       return new_dict






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





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






def 							A(    snake_case_		,  snake_case_		,  snake_case_ = 0		,  snake_case_ = 0						):
       """simple docstring"""





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

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


UpperCamelCase					=     TypeVar("""T""")






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





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

       return new_dict






@overload
def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       ...






@overload
def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       ...






@overload
def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       ...






@overload
def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       ...






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





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







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










'''simple docstring'''






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline

