






"""simple docstring"""


import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= []
  if isinstance(__UpperCamelCase		,       __UpperCamelCase ):
    for v in tree.values():
      shapes.extend(_fetch_dims(__UpperCamelCase ) )
  elif isinstance(__UpperCamelCase		,       (list, tuple) ):
    for t in tree:
      shapes.extend(_fetch_dims(__UpperCamelCase ) )
  elif isinstance(__UpperCamelCase		,       torch.Tensor ):
    shapes.append(tree.shape )
  else:
    raise ValueError('''Not supported''' )

  return shapes



@torch.jit.ignore
def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= []
  for d in reversed(__UpperCamelCase ):
    idx.append(flat_idx % d )
    __A									= flat_idx // d

  return tuple(reversed(__UpperCamelCase ) )



@torch.jit.ignore
def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase = None		,       __UpperCamelCase = None		,       ):




  """simple docstring"""







  def reduce_edge_list(__UpperCamelCase ) -> None:
    __A									= True
    for i in range(len(__UpperCamelCase ) ):
      __A									= -1 * (i + 1)
      l[reversed_idx] &= tally
      __A									= l[reversed_idx]

  if start_edges is None:
    __A									= [s == 0 for s in start]
    reduce_edge_list(__UpperCamelCase )
  if end_edges is None:
    __A									= [e == (d - 1) for e, d in zip(__UpperCamelCase		,       __UpperCamelCase )]
    reduce_edge_list(__UpperCamelCase )

  # Base cases. Either start/end are empty and we're done, or the final,
  # one-dimensional tensor can be simply sliced
  if len(__UpperCamelCase ) == 0:
    return [()]
  elif len(__UpperCamelCase ) == 1:
    return [(slice(start[0]		,       end[0] + 1 ),)]

  __A									= []
  __A									= []

  # Dimensions common to start and end can be selected directly
  for s, e in zip(__UpperCamelCase		,       __UpperCamelCase ):
    if s == e:
      path_list.append(slice(__UpperCamelCase		,       s + 1 ) )
    else:
      break

  __A									= tuple(__UpperCamelCase )
  __A									= len(__UpperCamelCase )

  # start == end, and we're done
  if divergence_idx == len(__UpperCamelCase ):
    return [path]

  def upper() -> Tuple[Tuple[slice, ...], ...]:
    assert start_edges is not None
    assert end_edges is not None

    __A									= start[divergence_idx]
    return tuple(
        path + (slice(__UpperCamelCase		,       sdi + 1 ),) + s
        for s in _get_minimal_slice_set(
            start[divergence_idx + 1 :]		,       [d - 1 for d in dims[divergence_idx + 1 :]]		,       dims[divergence_idx + 1 :]		,       start_edges=start_edges[divergence_idx + 1 :]		,       end_edges=[True for _ in end_edges[divergence_idx + 1 :]]		,       ) )

  def lower() -> Tuple[Tuple[slice, ...], ...]:
    assert start_edges is not None
    assert end_edges is not None

    __A									= end[divergence_idx]
    return tuple(
        path + (slice(__UpperCamelCase		,       edi + 1 ),) + s
        for s in _get_minimal_slice_set(
            [0 for _ in start[divergence_idx + 1 :]]		,       end[divergence_idx + 1 :]		,       dims[divergence_idx + 1 :]		,       start_edges=[True for _ in start_edges[divergence_idx + 1 :]]		,       end_edges=end_edges[divergence_idx + 1 :]		,       ) )

  # If both start and end are at the edges of the subtree rooted at
  # divergence_idx, we can just select the whole subtree at once
  if start_edges[divergence_idx] and end_edges[divergence_idx]:
    slices.append(path + (slice(start[divergence_idx]		,       end[divergence_idx] + 1 ),) )
  # If just start is at the edge, we can grab almost all of the subtree,
  # treating only the ragged bottom edge as an edge case
  elif start_edges[divergence_idx]:
    slices.append(path + (slice(start[divergence_idx]		,       end[divergence_idx] ),) )
    slices.extend(lower() )
  # Analogous to the previous case, but the top is ragged this time
  elif end_edges[divergence_idx]:
    slices.extend(upper() )
    slices.append(path + (slice(start[divergence_idx] + 1		,       end[divergence_idx] + 1 ),) )
  # If both sides of the range are ragged, we need to handle both sides
  # separately. If there's contiguous meat in between them, we can index it
  # in one big chunk
  else:
    slices.extend(upper() )
    __A									= end[divergence_idx] - start[divergence_idx]
    if middle_ground > 1:
      slices.append(path + (slice(start[divergence_idx] + 1		,       end[divergence_idx] ),) )
    slices.extend(lower() )

  return slices



@torch.jit.ignore
def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= t.shape[:no_batch_dims]
  __A									= list(_flat_idx_to_idx(__UpperCamelCase		,       __UpperCamelCase ) )
  # _get_minimal_slice_set is inclusive
  __A									= list(_flat_idx_to_idx(flat_end - 1		,       __UpperCamelCase ) )

  # Get an ordered list of slices to perform
  __A									= _get_minimal_slice_set(
      __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       )

  __A									= [t[s] for s in slices]

  return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase = False		,       __UpperCamelCase = None		,       __UpperCamelCase = False		,       ):




  """simple docstring"""







  if not (len(__UpperCamelCase ) > 0):
    raise ValueError('''Must provide at least one input''' )

  __A									= [shape[:no_batch_dims] for shape in _fetch_dims(__UpperCamelCase )]
  __A									= tuple([max(__UpperCamelCase ) for s in zip(*__UpperCamelCase )] )

  def _prep_inputs(__UpperCamelCase ) -> torch.Tensor:
    if not low_mem:
      if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
        __A									= t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
      __A									= t.reshape(-1		,       *t.shape[no_batch_dims:] )
    else:
      __A									= t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
    return t

  __A									= tensor_tree_map(_prep_inputs		,       __UpperCamelCase )
  __A									= None
  if _out is not None:
    __A									= tensor_tree_map(lambda __UpperCamelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) )		,       _out )

  __A									= 1
  for d in orig_batch_dims:
    flat_batch_dim *= d

  __A									= flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

  def _select_chunk(__UpperCamelCase ) -> torch.Tensor:
    return t[i : i + chunk_size] if t.shape[0] != 1 else t

  __A									= 0
  __A									= prepped_outputs
  for _ in range(__UpperCamelCase ):
    # Chunk the input
    if not low_mem:
      __A									= _select_chunk
    else:
      __A									= partial(
          _chunk_slice		,       flat_start=__UpperCamelCase		,       flat_end=min(__UpperCamelCase		,       i + chunk_size )		,       no_batch_dims=len(__UpperCamelCase )		,       )

    __A									= tensor_tree_map(__UpperCamelCase		,       __UpperCamelCase )

    # Run the layer on the chunk
    __A									= layer(**__UpperCamelCase )

    # Allocate space for the output
    if out is None:
      __A									= tensor_tree_map(lambda __UpperCamelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] )		,       __UpperCamelCase )

    # Put the chunk in its pre-allocated space
    if isinstance(__UpperCamelCase		,       __UpperCamelCase ):

      def assign(__UpperCamelCase		,       __UpperCamelCase ) -> None:
        for k, v in da.items():
          if isinstance(__UpperCamelCase		,       __UpperCamelCase ):
            assign(__UpperCamelCase		,       da[k] )
          else:
            if _add_into_out:
              v[i : i + chunk_size] += da[k]
            else:
              __A									= da[k]

      assign(__UpperCamelCase		,       __UpperCamelCase )
    elif isinstance(__UpperCamelCase		,       __UpperCamelCase ):
      for xa, xa in zip(__UpperCamelCase		,       __UpperCamelCase ):
        if _add_into_out:
          xa[i : i + chunk_size] += xa
        else:
          __A									= xa
    elif isinstance(__UpperCamelCase		,       torch.Tensor ):
      if _add_into_out:
        out[i : i + chunk_size] += output_chunk
      else:
        __A									= output_chunk
    else:
      raise ValueError('''Not supported''' )

    i += chunk_size

  __A									= tensor_tree_map(lambda __UpperCamelCase : t.view(orig_batch_dims + t.shape[1:] )		,       __UpperCamelCase )

  return out





class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     int = 5_12,       ):

     '''simple docstring'''
     __A									= max_chunk_size
     __A									= None
     __A									= None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     Callable,       _lowerCamelCase :     tuple,       _lowerCamelCase :     int  ):

     '''simple docstring'''
     logging.info('''Tuning chunk size...'''  )

     if min_chunk_size >= self.max_chunk_size:
       return min_chunk_size

     __A									= [2**l for l in range(int(math.log(self.max_chunk_size,       2  )  ) + 1  )]
     __A									= [c for c in candidates if c > min_chunk_size]
     __A									= [min_chunk_size] + candidates
     candidates[-1] += 4

     def test_chunk_size(_lowerCamelCase :     int  ) -> bool:
       try:
         with torch.no_grad():
           fn(*_lowerCamelCase,       chunk_size=_lowerCamelCase  )
         return True
       except RuntimeError:
         return False

     __A									= 0
     __A									= len(_lowerCamelCase  ) - 1
     while i > min_viable_chunk_size_index:
       __A									= test_chunk_size(candidates[i]  )
       if not viable:
         __A									= (min_viable_chunk_size_index + i) // 2
       else:
         __A									= i
         __A									= (i + len(_lowerCamelCase  ) - 1) // 2

     return candidates[min_viable_chunk_size_index]


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     Iterable,       _lowerCamelCase :     Iterable  ):

     '''simple docstring'''
     __A									= True
     for aa, aa in zip(_lowerCamelCase,       _lowerCamelCase  ):
       assert type(_lowerCamelCase  ) == type(_lowerCamelCase  )
       if isinstance(_lowerCamelCase,       (list, tuple)  ):
         consistent &= self._compare_arg_caches(_lowerCamelCase,       _lowerCamelCase  )
       elif isinstance(_lowerCamelCase,       _lowerCamelCase  ):
         __A									= [v for _, v in sorted(aa.items(),       key=lambda _lowerCamelCase  : x[0]  )]
         __A									= [v for _, v in sorted(aa.items(),       key=lambda _lowerCamelCase  : x[0]  )]
         consistent &= self._compare_arg_caches(_lowerCamelCase,       _lowerCamelCase  )
       else:
         consistent &= aa == aa

     return consistent






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Callable,       _lowerCamelCase :     tuple,       _lowerCamelCase :     int,       ):

     '''simple docstring'''
     __A									= True
     __A									= tree_map(lambda _lowerCamelCase  : a.shape if isinstance(_lowerCamelCase,       torch.Tensor  ) else a,       _lowerCamelCase,       _lowerCamelCase  )
     if self.cached_arg_data is not None:
       # If args have changed shape/value, we need to re-tune
       assert len(self.cached_arg_data  ) == len(_lowerCamelCase  )
       __A									= self._compare_arg_caches(self.cached_arg_data,       _lowerCamelCase  )
     else:
       # Otherwise, we can reuse the precomputed value
       __A									= False

     if not consistent:
       __A									= self._determine_favorable_chunk_size(
           _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       )
       __A									= arg_data

     assert self.cached_chunk_size is not None

     return self.cached_chunk_size












"""simple docstring"""


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_				     =     get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Optional[Any]						 = PegasusTokenizer
   A_						:	int						 = PegasusTokenizerFast
   A_						:	Optional[Any]						 = True
   A_						:	Union[str, Any]						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     super().setUp()

     # We have a SentencePiece fixture for testing
     __A									= PegasusTokenizer(_lowerCamelCase  )
     tokenizer.save_pretrained(self.tmpdirname  )


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/pegasus-large'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       **_lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= '''</s>'''
     __A									= 1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase  ),       _lowerCamelCase  )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase  ),       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= list(self.get_tokenizer().get_vocab().keys()  )

     self.assertEqual(vocab_keys[0],       '''<pad>'''  )
     self.assertEqual(vocab_keys[1],       '''</s>'''  )
     self.assertEqual(vocab_keys[-1],       '''v'''  )
     self.assertEqual(len(_lowerCamelCase  ),       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     self.assertEqual(self.get_tokenizer().vocab_size,       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
         ''' </s> <pad> <pad> <pad>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self._large_tokenizer
     # <mask_1> masks whole sentence while <mask_2> masks single word
     __A									= '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
     __A									= [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self._large_tokenizer
     # The tracebacks for the following asserts are **better** without messages or self.assertEqual
     assert tokenizer.vocab_size == 9_61_03
     assert tokenizer.pad_token_id == 0
     assert tokenizer.eos_token_id == 1
     assert tokenizer.offset == 1_03
     assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
     assert tokenizer.unk_token == "<unk>"
     assert tokenizer.model_max_length == 10_24
     __A									= '''To ensure a smooth flow of bank resolutions.'''
     __A									= [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )
     assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 1_50, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 10_24)
     assert batch.attention_mask.shape == (2, 10_24)
     assert targets["input_ids"].shape == (2, 5)
     assert len(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # fmt: off
     __A									= {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_lowerCamelCase,       model_name='''google/bigbird-pegasus-large-arxiv''',       revision='''ba85d0851d708441f91440d509690f1ab6353415''',       )





@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = PegasusTokenizer
   A_						:	Union[str, Any]						 = PegasusTokenizerFast
   A_						:	Any						 = True
   A_						:	str						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     super().setUp()

     # We have a SentencePiece fixture for testing
     __A									= PegasusTokenizer(_lowerCamelCase,       offset=0,       mask_token_sent=_lowerCamelCase,       mask_token='''[MASK]'''  )
     tokenizer.save_pretrained(self.tmpdirname  )


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
         ''' <pad> <pad> <pad>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 10_00, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 40_96)
     assert batch.attention_mask.shape == (2, 40_96)
     assert targets["input_ids"].shape == (2, 5)
     assert len(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= (
         '''This is an example string that is used to test the original TF implementation against the HF'''
         ''' implementation'''
     )

     __A									= self._large_tokenizer(_lowerCamelCase  ).input_ids

     self.assertListEqual(
         _lowerCamelCase,       [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1],       )




