






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
       import transformers
def 		lowercase__  (				_UpperCamelCase) ->			List[str]:
      """simple docstring"""

      random.seed(_UpperCamelCase)
      np.random.seed(_UpperCamelCase)
      torch.manual_seed(_UpperCamelCase)
      torch.cuda.manual_seed_all(_UpperCamelCase)
      # ^^ safe to call this function even if cuda is not available





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Iterable[torch.nn.Parameter]   ,						_SCREAMING_SNAKE_CASE      :    float = 0.9_9_9_9   ,						_SCREAMING_SNAKE_CASE      :    float = 0.0   ,						_SCREAMING_SNAKE_CASE      :    int = 0   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    Union[float, int] = 1.0   ,						_SCREAMING_SNAKE_CASE      :    Union[float, int] = 2 / 3   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] = None   ,						_SCREAMING_SNAKE_CASE      :    Dict[str, Any] = None   ,						**_SCREAMING_SNAKE_CASE      :    Tuple   ,						):






           """simple docstring"""




           if isinstance(_SCREAMING_SNAKE_CASE   ,						torch.nn.Module ):
                 UpperCamelCase					       =  (
                     'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
                     'Please pass the parameters of the module instead.'
                 )
                 deprecate(
                     'passing a `torch.nn.Module` to `ExponentialMovingAverage`'   ,						'1.0.0'   ,						_SCREAMING_SNAKE_CASE   ,						standard_warn=_SCREAMING_SNAKE_CASE   ,						)
                 UpperCamelCase					       =  parameters.parameters()

                 # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                 UpperCamelCase					       =  True

           if kwargs.get('max_value'   ,						_SCREAMING_SNAKE_CASE ) is not None:
                 UpperCamelCase					       =  'The `max_value` argument is deprecated. Please use `decay` instead.'
                 deprecate('max_value'   ,						'1.0.0'   ,						_SCREAMING_SNAKE_CASE   ,						standard_warn=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  kwargs['max_value']

           if kwargs.get('min_value'   ,						_SCREAMING_SNAKE_CASE ) is not None:
                 UpperCamelCase					       =  'The `min_value` argument is deprecated. Please use `min_decay` instead.'
                 deprecate('min_value'   ,						'1.0.0'   ,						_SCREAMING_SNAKE_CASE   ,						standard_warn=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  kwargs['min_value']

           UpperCamelCase					       =  list(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  [p.clone().detach() for p in parameters]

           if kwargs.get('device'   ,						_SCREAMING_SNAKE_CASE ) is not None:
                 UpperCamelCase					       =  'The `device` argument is deprecated. Please use `to` instead.'
                 deprecate('device'   ,						'1.0.0'   ,						_SCREAMING_SNAKE_CASE   ,						standard_warn=_SCREAMING_SNAKE_CASE )
                 self.to(device=kwargs['device'] )

           UpperCamelCase					       =  None

           UpperCamelCase					       =  decay
           UpperCamelCase					       =  min_decay
           UpperCamelCase					       =  update_after_step
           UpperCamelCase					       =  use_ema_warmup
           UpperCamelCase					       =  inv_gamma
           UpperCamelCase					       =  power
           UpperCamelCase					       =  0
           UpperCamelCase					       =  None  # set in `step()`

           UpperCamelCase					       =  model_cls
           UpperCamelCase					       =  model_config







     @classmethod
     def   _SCREAMING_SNAKE_CASE     ( cls      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase       ,  UpperCamelCase					       =  model_cls.load_config(_SCREAMING_SNAKE_CASE   ,						return_unused_kwargs=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  model_cls.from_pretrained(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  cls(model.parameters()   ,						model_cls=_SCREAMING_SNAKE_CASE   ,						model_config=model.config )

           ema_model.load_state_dict(_SCREAMING_SNAKE_CASE )
           return ema_model







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    int ):






           """simple docstring"""




           if self.model_cls is None:
                 raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.' )

           if self.model_config is None:
                 raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.' )

           UpperCamelCase					       =  self.model_cls.from_config(self.model_config )
           UpperCamelCase					       =  self.state_dict()
           state_dict.pop('shadow_params'   ,						_SCREAMING_SNAKE_CASE )

           model.register_to_config(**_SCREAMING_SNAKE_CASE )
           self.copy_to(model.parameters() )
           model.save_pretrained(_SCREAMING_SNAKE_CASE )







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






           """simple docstring"""




           UpperCamelCase					       =  max(0   ,						optimization_step - self.update_after_step - 1 )

           if step <= 0:
                 return 0.0

           if self.use_ema_warmup:
                 UpperCamelCase					       =  1 - (1 + step / self.inv_gamma) ** -self.power
           else:
                 UpperCamelCase					       =  (1 + step) / (10 + step)

           UpperCamelCase					       =  min(_SCREAMING_SNAKE_CASE   ,						self.decay )
           # make sure decay is not smaller than min_decay
           UpperCamelCase					       =  max(_SCREAMING_SNAKE_CASE   ,						self.min_decay )
           return cur_decay_value







     @torch.no_grad()
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Iterable[torch.nn.Parameter] ):






           """simple docstring"""




           if isinstance(_SCREAMING_SNAKE_CASE   ,						torch.nn.Module ):
                 UpperCamelCase					       =  (
                     'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
                     'Please pass the parameters of the module instead.'
                 )
                 deprecate(
                     'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'   ,						'1.0.0'   ,						_SCREAMING_SNAKE_CASE   ,						standard_warn=_SCREAMING_SNAKE_CASE   ,						)
                 UpperCamelCase					       =  parameters.parameters()

           UpperCamelCase					       =  list(_SCREAMING_SNAKE_CASE )

           self.optimization_step += 1

           # Compute the decay factor for the exponential moving average.
           UpperCamelCase					       =  self.get_decay(self.optimization_step )
           UpperCamelCase					       =  decay
           UpperCamelCase					       =  1 - decay

           UpperCamelCase					       =  contextlib.nullcontext
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                 import deepspeed

           for s_param, param in zip(self.shadow_params   ,						_SCREAMING_SNAKE_CASE ):
                 if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                       UpperCamelCase					       =  deepspeed.zero.GatheredParameters(_SCREAMING_SNAKE_CASE   ,						modifier_rank=_SCREAMING_SNAKE_CASE )

                 with context_manager():
                       if param.requires_grad:
                             s_param.sub_(one_minus_decay * (s_param - param) )
                       else:
                             s_param.copy_(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Iterable[torch.nn.Parameter] ):






           """simple docstring"""




           UpperCamelCase					       =  list(_SCREAMING_SNAKE_CASE )
           for s_param, param in zip(self.shadow_params   ,						_SCREAMING_SNAKE_CASE ):
                 param.data.copy_(s_param.to(param.device ).data )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]=None   ,						_SCREAMING_SNAKE_CASE      :    str=None ):






           """simple docstring"""




           UpperCamelCase					       =  [
               p.to(device=_SCREAMING_SNAKE_CASE   ,						dtype=_SCREAMING_SNAKE_CASE ) if p.is_floating_point() else p.to(device=_SCREAMING_SNAKE_CASE )
               for p in self.shadow_params
           ]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           return {
               "decay": self.decay,
               "min_decay": self.min_decay,
               "optimization_step": self.optimization_step,
               "update_after_step": self.update_after_step,
               "use_ema_warmup": self.use_ema_warmup,
               "inv_gamma": self.inv_gamma,
               "power": self.power,
               "shadow_params": self.shadow_params,
           }







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Iterable[torch.nn.Parameter] ):






           """simple docstring"""




           UpperCamelCase					       =  [param.detach().cpu().clone() for param in parameters]







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Iterable[torch.nn.Parameter] ):






           """simple docstring"""




           if self.temp_stored_params is None:
                 raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`' )
           for c_param, param in zip(self.temp_stored_params   ,						_SCREAMING_SNAKE_CASE ):
                 param.data.copy_(c_param.data )

           # Better memory-wise.
           UpperCamelCase					       =  None







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    dict ):






           """simple docstring"""




           UpperCamelCase					       =  copy.deepcopy(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  state_dict.get('decay'   ,						self.decay )
           if self.decay < 0.0 or self.decay > 1.0:
                 raise ValueError('Decay must be between 0 and 1' )

           UpperCamelCase					       =  state_dict.get('min_decay'   ,						self.min_decay )
           if not isinstance(self.min_decay   ,						_SCREAMING_SNAKE_CASE ):
                 raise ValueError('Invalid min_decay' )

           UpperCamelCase					       =  state_dict.get('optimization_step'   ,						self.optimization_step )
           if not isinstance(self.optimization_step   ,						_SCREAMING_SNAKE_CASE ):
                 raise ValueError('Invalid optimization_step' )

           UpperCamelCase					       =  state_dict.get('update_after_step'   ,						self.update_after_step )
           if not isinstance(self.update_after_step   ,						_SCREAMING_SNAKE_CASE ):
                 raise ValueError('Invalid update_after_step' )

           UpperCamelCase					       =  state_dict.get('use_ema_warmup'   ,						self.use_ema_warmup )
           if not isinstance(self.use_ema_warmup   ,						_SCREAMING_SNAKE_CASE ):
                 raise ValueError('Invalid use_ema_warmup' )

           UpperCamelCase					       =  state_dict.get('inv_gamma'   ,						self.inv_gamma )
           if not isinstance(self.inv_gamma   ,						(float, int) ):
                 raise ValueError('Invalid inv_gamma' )

           UpperCamelCase					       =  state_dict.get('power'   ,						self.power )
           if not isinstance(self.power   ,						(float, int) ):
                 raise ValueError('Invalid power' )

           UpperCamelCase					       =  state_dict.get('shadow_params'   ,						_SCREAMING_SNAKE_CASE )
           if shadow_params is not None:
                 UpperCamelCase					       =  shadow_params
                 if not isinstance(self.shadow_params   ,						_SCREAMING_SNAKE_CASE ):
                       raise ValueError('shadow_params must be a list' )
                 if not all(isinstance(_SCREAMING_SNAKE_CASE   ,						torch.Tensor ) for p in self.shadow_params ):
                       raise ValueError('shadow_params must all be Tensors' )











import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__   :  Optional[int]        =							get_tests_dir('''fixtures/spiece.model''')





@require_sentencepiece
@require_tokenizers
class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					DebertaVaTokenizer
     snake_case__					=					DebertaVaTokenizerFast
     snake_case__					=					True
     snake_case__					=					True







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           super().setUp()

           # We have a SentencePiece fixture for testing
           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						unk_token='<unk>' )
           tokenizer.save_pretrained(self.tmpdirname )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'this is a test'
           UpperCamelCase					       =  'this is a test'
           return input_text, output_text







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  '<pad>'
           UpperCamelCase					       =  0

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  list(self.get_tokenizer().get_vocab().keys() )
           self.assertEqual(vocab_keys[0]   ,						'<pad>' )
           self.assertEqual(vocab_keys[1]   ,						'<unk>' )
           self.assertEqual(vocab_keys[-1]   ,						'[PAD]' )
           self.assertEqual(len(_SCREAMING_SNAKE_CASE )   ,						3_0001 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           self.assertEqual(self.get_tokenizer().vocab_size   ,						3_0000 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  ' \tHeLLo!how  \n Are yoU?  '
           UpperCamelCase					       =  ['▁hello', '!', 'how', '▁are', '▁you', '?']
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           pass







     @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  ' \tHeLLo!how  \n Are yoU?  '
           UpperCamelCase					       =  ['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
           # fmt: on

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE   ,						split_by_punct=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  self.get_tokenizer()
           UpperCamelCase					       =  self.get_rust_tokenizer()

           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'

           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE ) )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_rust_tokenizer()
           UpperCamelCase					       =  tokenizer.encode(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  'This is a test'
           UpperCamelCase					       =  [13, 1, 4398, 25, 21, 1289]
           UpperCamelCase					       =  ['▁', 'T', 'his', '▁is', '▁a', '▁test']
           UpperCamelCase					       =  ['▁', '<unk>', 'his', '▁is', '▁a', '▁test']

           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE   ,						keep_accents=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  DebertaVaTokenizerFast(_SCREAMING_SNAKE_CASE   ,						keep_accents=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           # fmt: off
           UpperCamelCase					       =  'I was born in 92000, and this is falsé.'
           UpperCamelCase					       =  [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
           UpperCamelCase					       =  ['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
           UpperCamelCase					       =  ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
           # fmt: on

           UpperCamelCase					       =  tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  rust_tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  DebertaVaTokenizer(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  tokenizer.encode('sequence builders' )
           UpperCamelCase					       =  tokenizer.encode('multi-sequence build' )

           UpperCamelCase					       =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]   ,						_SCREAMING_SNAKE_CASE )
           self.assertEqual(
               [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]   ,						_SCREAMING_SNAKE_CASE   ,						)







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  {'input_ids': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 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], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 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]], 'token_type_ids': [[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, 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, 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, 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, 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], [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, 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=_SCREAMING_SNAKE_CASE   ,						model_name='microsoft/deberta-v2-xlarge'   ,						revision='ad6e42c1532ddf3a15c39246b63f5559d558b670'   ,						)



