


'''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      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      bool    ):





     '''simple docstring'''






     if not inplace:
          __UpperCAmelCase      :					Tuple    				=     ma + ma
     else:
          ma += ma

     return ma




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      List[int]    ):





     '''simple docstring'''






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




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      int    ):





     '''simple docstring'''






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




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      int     ,   _lowercase   :      float = 1E-4    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     mask.expand(*value.shape    )
     return torch.sum(mask * value     ,   dim=_lowercase    ) / (eps + torch.sum(_lowercase     ,   dim=_lowercase    ))




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      torch.types.Number = 2.3125     ,   _lowercase   :      torch.types.Number = 21.6875     ,   _lowercase   :      int = 64    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Optional[Any]    				=     torch.linspace(_lowercase     ,   _lowercase     ,   no_bins - 1     ,   device=pts.device    )
     __UpperCAmelCase      :					str    				=     torch.sqrt(torch.sum((pts.unsqueeze(-2    ) - pts.unsqueeze(-3    )) ** 2     ,   dim=-1    )    )
     return torch.bucketize(_lowercase     ,   _lowercase    )




def       _a      (		_lowercase   :      Callable[[list], Any]     ,   _lowercase   :      List[dict]    ):





     '''simple docstring'''






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

     return new_dict




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor    ):





     '''simple docstring'''






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




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      int = 0     ,   _lowercase   :      int = 0    ):





     '''simple docstring'''






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

     __UpperCAmelCase      :					List[Union[slice, torch.Tensor]]    				=     [slice(_lowercase    ) for _ in range(len(data.shape    ) - no_batch_dims    )]
     __UpperCAmelCase      :					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    )]


__UpperCAmelCase     :List[str]												=  TypeVar("T")




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





     '''simple docstring'''






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

     return new_dict




@overload
def       _a      (		_lowercase   :      Callable[[T], Any]     ,   _lowercase   :      T     ,   _lowercase   :      Type[T]    ):





     '''simple docstring'''






     ...




@overload
def       _a      (		_lowercase   :      Callable[[T], Any]     ,   _lowercase   :      dict     ,   _lowercase   :      Type[T]    ):





     '''simple docstring'''






     ...




@overload
def       _a      (		_lowercase   :      Callable[[T], Any]     ,   _lowercase   :      list     ,   _lowercase   :      Type[T]    ):





     '''simple docstring'''






     ...




@overload
def       _a      (		_lowercase   :      Callable[[T], Any]     ,   _lowercase   :      tuple     ,   _lowercase   :      Type[T]    ):





     '''simple docstring'''






     ...




def       _a      (		_lowercase   :      int     ,   _lowercase   :      Tuple     ,   _lowercase   :      Optional[int]    ):





     '''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'''    )

__UpperCAmelCase     :List[Any]												=  partial(tree_map, leaf_type=torch.Tensor)



'''simple docstring'''



import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir



@dataclass
class       a							:


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					str



class       a							(						unittest.TestCase			):


     """simple docstring"""






     def 					lowerCamelCase__					(							self	:						str     )		->				Dict:
          __UpperCAmelCase      :					str    				=     get_tests_dir('''fixtures/test_sentencepiece.model'''     )
          __UpperCAmelCase      :					List[Any]    				=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''     )

          __UpperCAmelCase      :					Tuple    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

          with warnings.catch_warnings(record=snake_case     ) as w:
               __UpperCAmelCase      :					Optional[int]    				=     SpmConverter(snake_case     )
          self.assertEqual(len(snake_case     )				,					0     )

          __UpperCAmelCase      :					str    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

          with warnings.catch_warnings(record=snake_case     ) as w:
               __UpperCAmelCase      :					Optional[int]    				=     SpmConverter(snake_case     )
          self.assertEqual(len(snake_case     )				,					1     )

          self.assertIn(
              '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
              ''' which is not implemented in the fast tokenizers.'''				,					str(w[0].message     )				,					)