






"""simple docstring"""



import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class 			a	(  TensorFormatter[Mapping, np.ndarray, Mapping]      ):







    def __init__(     self				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      super().__init__(features=_lowerCamelCase			)
      lowercase				       =			np_array_kwargs







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if isinstance(_lowerCamelCase				,						_lowerCamelCase			):
        if column and all(
            isinstance(_lowerCamelCase				,						np.ndarray			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
          return np.stack(_lowerCamelCase			)
        else:
          # don't use np.array(column, dtype=object)
          # since it fails in certain cases
          # see https://stackoverflow.com/q/51005699
          lowercase				       =			np.empty(len(_lowerCamelCase			)				,						dtype=_lowerCamelCase			)
          lowercase				       =			column
          return out
      return column







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if isinstance(_lowerCamelCase				,						(str, bytes, type(_lowerCamelCase			))			):
        return value
      elif isinstance(_lowerCamelCase				,						(np.character, np.ndarray)			) and np.issubdtype(value.dtype				,						np.character			):
        return value
      elif isinstance(_lowerCamelCase				,						np.number			):
        return value

      lowercase				       =			{}

      if isinstance(_lowerCamelCase				,						np.ndarray			) and np.issubdtype(value.dtype				,						np.integer			):
        lowercase				       =			{'dtype': np.intaa}
      elif isinstance(_lowerCamelCase				,						np.ndarray			) and np.issubdtype(value.dtype				,						np.floating			):
        lowercase				       =			{'dtype': np.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(_lowerCamelCase				,						PIL.Image.Image			):
          return np.asarray(_lowerCamelCase				,						**self.np_array_kwargs			)

      return np.asarray(_lowerCamelCase				,						**{**default_dtype, **self.np_array_kwargs}			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
        import torch

        if isinstance(_lowerCamelCase				,						torch.Tensor			):
          return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
      if hasattr(_lowerCamelCase				,						'__array__'			) and not isinstance(_lowerCamelCase				,						(np.ndarray, np.character, np.number)			):
        lowercase				       =			data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_lowerCamelCase				,						np.ndarray			):
        if data_struct.dtype == object:
          return self._consolidate([self.recursive_tensorize(_lowerCamelCase			) for substruct in data_struct]			)
      if isinstance(_lowerCamelCase				,						(list, tuple)			):
        return self._consolidate([self.recursive_tensorize(_lowerCamelCase			) for substruct in data_struct]			)
      return self._tensorize(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      return map_nested(self._recursive_tensorize				,						_lowerCamelCase				,						map_list=_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_row(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_row(_lowerCamelCase			)
      return self.recursive_tensorize(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_column(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_column(_lowerCamelCase				,						pa_table.column_names[0]			)
      lowercase				       =			self.recursive_tensorize(_lowerCamelCase			)
      lowercase				       =			self._consolidate(_lowerCamelCase			)
      return column







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_batch(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_batch(_lowerCamelCase			)
      lowercase				       =			self.recursive_tensorize(_lowerCamelCase			)
      for column_name in batch:
        lowercase				       =			self._consolidate(batch[column_name]			)
      return batch








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__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   bool      ):





       '''simple docstring'''



       if not inplace:
              __lowerCamelCase  =		ma + ma
       else:
              ma += ma

       return ma




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   List[int]      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   int      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   int    ,     A__			:   float = 1E-4      ):





       '''simple docstring'''



       __lowerCamelCase  =		mask.expand(*value.shape      )
       return torch.sum(mask * value    ,     dim=A__      ) / (eps + torch.sum(A__    ,     dim=A__      ))




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.types.Number = 2.3_125    ,     A__			:   torch.types.Number = 21.6_875    ,     A__			:   int = 64      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.linspace(A__    ,     A__    ,     no_bins - 1    ,     device=pts.device      )
       __lowerCamelCase  =		torch.sqrt(torch.sum((pts.unsqueeze(-2      ) - pts.unsqueeze(-3      )) ** 2    ,     dim=-1      )      )
       return torch.bucketize(A__    ,     A__      )




def    lowerCamelCase__			(		A__			:   Callable[[list], Any]    ,     A__			:   List[dict]      ):





       '''simple docstring'''



       __lowerCamelCase  =		dicts[0]
       __lowerCamelCase  =		{}
       for k, v in first.items():
              __lowerCamelCase  =		[d[k] for d in dicts]
              if isinstance(A__    ,     A__      ):
                     __lowerCamelCase  =		dict_multimap(A__    ,     A__      )
              else:
                     __lowerCamelCase  =		fn(A__      )

       return new_dict




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor      ):





       '''simple docstring'''



       __lowerCamelCase  =		v_bins.view(((1,) * len(x.shape      )) + (len(A__      ),)      )
       __lowerCamelCase  =		x[..., None] - reshaped_bins
       __lowerCamelCase  =		torch.argmin(torch.abs(A__      )    ,     dim=-1      )
       return nn.functional.one_hot(A__    ,     num_classes=len(A__      )      ).float()




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   int = 0    ,     A__			:   int = 0      ):





       '''simple docstring'''



       __lowerCamelCase  =		[]
       for i, s in enumerate(data.shape[:no_batch_dims]      ):
              __lowerCamelCase  =		torch.arange(A__      )
              __lowerCamelCase  =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape      ) - i - 1)))      )
              ranges.append(A__      )

       __lowerCamelCase  =		[slice(A__      ) for _ in range(len(data.shape      ) - no_batch_dims      )]
       __lowerCamelCase  =		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__      )]


UpperCAmelCase_   							=      TypeVar('T')




def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   Dict[Any, Union[dict, list, tuple, T]]    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       __lowerCamelCase  =		{}
       for k, v in dic.items():
              if isinstance(A__    ,     A__      ):
                     __lowerCamelCase  =		dict_map(A__    ,     A__    ,     A__      )
              else:
                     __lowerCamelCase  =		tree_map(A__    ,     A__    ,     A__      )

       return new_dict




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   T    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   dict    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   list    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   tuple    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




def    lowerCamelCase__			(		A__			:   Dict    ,     A__			:   List[str]    ,     A__			:   List[str]      ):





       '''simple docstring'''



       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"""      )




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

