





"""simple docstring"""




import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			Tuple        =       HerbertTokenizer
      lowercase_				:			str        =       HerbertTokenizerFast
      lowercase_				:			Tuple        =       True

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        super().setUp()

        # Use a simpler test file without japanese/chinese characters
        with open(F"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt""" ,				encoding='utf-8'  ) as f_data:
          A_  :	int    						=  f_data.read().replace('\n\n' ,				'\n'  ).strip()

        A_  :	int    						=  [
            '<s>',
            '</s>',
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            'w</w>',
            'r</w>',
            't</w>',
            'lo',
            'low',
            'er</w>',
            'low</w>',
            'lowest</w>',
            'newer</w>',
            'wider</w>',
            ',</w>',
            '<unk>',
        ]
        A_  :	int    						=  dict(zip(snake_case_ ,				range(len(snake_case_  )  )  )  )
        A_  :	Any    						=  ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

        A_  :	Union[str, Any]    						=  os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['vocab_file']  )
        A_  :	List[str]    						=  os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['merges_file']  )
        with open(self.vocab_file ,				'w'  ) as fp:
          fp.write(json.dumps(snake_case_  )  )
        with open(self.merges_file ,				'w'  ) as fp:
          fp.write('\n'.join(snake_case_  )  )

      def  lowerCamelCase_      (					self ,				snake_case_  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  'lower newer'
        A_  :	List[Any]    						=  'lower newer'
        return input_text, output_text

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  self.tokenizer_class(vocab_file=self.vocab_file ,				merges_file=self.merges_file  )

        A_  :	str    						=  'lower'
        A_  :	Union[str, Any]    						=  ['low', 'er</w>']
        A_  :	int    						=  tokenizer.tokenize(snake_case_  )
        self.assertListEqual(snake_case_ ,				snake_case_  )

        A_  :	Tuple    						=  tokens + ['<unk>']
        A_  :	Dict    						=  [1_6, 1_7, 2_3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_  ) ,				snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        if not self.test_rust_tokenizer:
          return

        A_  :	Tuple    						=  self.get_tokenizer()
        A_  :	List[str]    						=  self.get_rust_tokenizer()

        A_  :	List[str]    						=  'lower,newer'

        A_  :	List[Any]    						=  tokenizer.tokenize(snake_case_  )
        A_  :	Optional[Any]    						=  rust_tokenizer.tokenize(snake_case_  )
        self.assertListEqual(snake_case_ ,				snake_case_  )

        A_  :	Tuple    						=  tokenizer.encode(snake_case_ ,				add_special_tokens=snake_case_  )
        A_  :	List[str]    						=  rust_tokenizer.encode(snake_case_ ,				add_special_tokens=snake_case_  )
        self.assertListEqual(snake_case_ ,				snake_case_  )

        A_  :	Any    						=  self.get_rust_tokenizer()
        A_  :	Union[str, Any]    						=  tokenizer.encode(snake_case_  )
        A_  :	Optional[Any]    						=  rust_tokenizer.encode(snake_case_  )
        self.assertListEqual(snake_case_ ,				snake_case_  )

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'  )

        A_  :	str    						=  tokenizer.encode('konstruowanie sekwencji' ,				add_special_tokens=snake_case_  )
        A_  :	List[str]    						=  tokenizer.encode('konstruowanie wielu sekwencji' ,				add_special_tokens=snake_case_  )

        A_  :	Dict    						=  tokenizer.build_inputs_with_special_tokens(snake_case_  )
        A_  :	Optional[Any]    						=  tokenizer.build_inputs_with_special_tokens(snake_case_ ,				snake_case_  )

        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + text_a + [2]






"""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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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

  return ma





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  -1 * len(_UpperCAmelCase )
  A_  :	List[str]    						=  list(range(len(tensor.shape[:zero_index] ) ) )
  return tensor.permute(first_inds + [zero_index + i for i in inds] )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 1E-4 ):






  """simple docstring"""





  A_  :	Optional[Any]    						=  mask.expand(*value.shape )
  return torch.sum(mask * value			, dim=_UpperCAmelCase ) / (eps + torch.sum(_UpperCAmelCase			, dim=_UpperCAmelCase ))





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase = 2.3_125			, _UpperCAmelCase = 21.6_875			, _UpperCAmelCase = 64 ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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

  return new_dict





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 0			, _UpperCAmelCase = 0 ):






  """simple docstring"""





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

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


lowerCamelCase_					:						Union[str, Any]      = TypeVar('T')





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Dict[Any, Union[dict, list, tuple, Any]]    						=  {}
  for k, v in dic.items():
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	int    						=  dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Any    						=  tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )

  return new_dict





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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





lowerCamelCase_					:						Optional[int]      = partial(tree_map, leaf_type=torch.Tensor)