"""simple docstring"""







import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
__SCREAMING_SNAKE_CASE								=[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]   ):
     for attribute in key.split('.'   ):
          lowercase_			:       int									=			getattr(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     if weight_type is not None:
          lowercase_			:       Optional[int]									=			getattr(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ).shape
     else:
          lowercase_			:       str									=			hf_pointer.shape

     assert hf_shape == value.shape, (
         F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
         F''' {value.shape} for {full_name}'''
     )

     if weight_type == "weight":
          lowercase_			:       Tuple									=			value
     elif weight_type == "weight_g":
          lowercase_			:       Tuple									=			value
     elif weight_type == "weight_v":
          lowercase_			:       str									=			value
     elif weight_type == "bias":
          lowercase_			:       str									=			value
     else:
          lowercase_			:       int									=			value

     logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       int									=			[]
     lowercase_			:       List[str]									=			fairseq_model.state_dict()

     lowercase_			:       List[str]									=			hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase_			:       List[Any]									=			False
          if "conv_layers" in name:
               load_conv_layer(
                   __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    hf_model.config.feat_extract_norm == 'group'  ,    )
               lowercase_			:       List[str]									=			True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
                         lowercase_			:       Any									=			True
                         if "*" in mapped_key:
                              lowercase_			:       Any									=			name.split(__SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
                              lowercase_			:       List[str]									=			mapped_key.replace('*'  ,    __SCREAMING_SNAKE_CASE   )
                         if "weight_g" in name:
                              lowercase_			:       str									=			'weight_g'
                         elif "weight_v" in name:
                              lowercase_			:       Dict									=			'weight_v'
                         elif "bias" in name and "relative_attention_bias" not in name:
                              lowercase_			:       str									=			'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowercase_			:       Any									=			'weight'
                         else:
                              lowercase_			:       Tuple									=			None

                         set_recursively(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
                    continue
          if not is_used:
               unused_weights.append(__SCREAMING_SNAKE_CASE   )

     logger.warning(F'''Unused weights: {unused_weights}'''   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  str   ):
     lowercase_			:       Any									=			full_name.split('conv_layers.'   )[-1]
     lowercase_			:       Tuple									=			name.split('.'   )
     lowercase_			:       Tuple									=			int(items[0]   )
     lowercase_			:       Optional[int]									=			int(items[1]   )

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
               )
               lowercase_			:       str									=			value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''   )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
               )
               lowercase_			:       int									=			value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''   )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                   " found."
               )
               lowercase_			:       Optional[Any]									=			value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
               )
               lowercase_			:       int									=			value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
     else:
          unused_weights.append(__SCREAMING_SNAKE_CASE   )



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Tuple=None   ):
     # load the pre-trained checkpoints
     lowercase_			:       Union[str, Any]									=			torch.load(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Union[str, Any]									=			WavLMConfigOrig(checkpoint['cfg']   )
     lowercase_			:       Optional[Any]									=			WavLMOrig(__SCREAMING_SNAKE_CASE   )
     model.load_state_dict(checkpoint['model']   )
     model.eval()

     if config_path is not None:
          lowercase_			:       Union[str, Any]									=			WavLMConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     else:
          lowercase_			:       Union[str, Any]									=			WavLMConfig()

     lowercase_			:       Dict									=			WavLMModel(__SCREAMING_SNAKE_CASE   )

     recursively_load_weights(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     hf_wavlm.save_pretrained(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE								=argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       __SCREAMING_SNAKE_CASE								=parser.parse_args()
       convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


"""simple docstring"""







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


__SCREAMING_SNAKE_CASE								=get_tests_dir("fixtures/spiece.model")







@require_sentencepiece
@require_tokenizers
class  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						DebertaVaTokenizer
   lowercase							=						DebertaVaTokenizerFast
   lowercase							=						True
   lowercase							=						True
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase_			:       Any									=			DebertaVaTokenizer(__UpperCamelCase  ,unk_token='<unk>'    )
        tokenizer.save_pretrained(self.tmpdirname    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			'this is a test'
        lowercase_			:       str									=			'this is a test'
        return input_text, output_text
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			'<pad>'
        lowercase_			:       str									=			0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase    )  ,__UpperCamelCase    )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase    )  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			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(__UpperCamelCase    )  ,3_0001    )
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        self.assertEqual(self.get_tokenizer().vocab_size  ,3_0000    )
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


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

        lowercase_			:       int									=			DebertaVaTokenizer(__UpperCamelCase  ,do_lower_case=__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			DebertaVaTokenizerFast(__UpperCamelCase  ,do_lower_case=__UpperCamelCase    )
        lowercase_			:       Dict									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'    )
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        pass
   @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        pass
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


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

        lowercase_			:       List[str]									=			DebertaVaTokenizer(__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Any									=			DebertaVaTokenizerFast(__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       Tuple									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


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

        lowercase_			:       List[str]									=			DebertaVaTokenizer(__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       Dict									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Tuple									=			DebertaVaTokenizerFast(__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       Dict									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


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

        lowercase_			:       Union[str, Any]									=			DebertaVaTokenizer(__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       Dict									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Tuple									=			DebertaVaTokenizerFast(__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       Tuple									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


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

        lowercase_			:       Optional[int]									=			DebertaVaTokenizer(__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       str									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Union[str, Any]									=			DebertaVaTokenizerFast(__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       List[Any]									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


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

        lowercase_			:       str									=			DebertaVaTokenizer(__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       int									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Union[str, Any]									=			DebertaVaTokenizerFast(__UpperCamelCase  ,do_lower_case=__UpperCamelCase  ,split_by_punct=__UpperCamelCase    )
        lowercase_			:       List[Any]									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )

        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			self.get_tokenizer()
        lowercase_			:       int									=			self.get_rust_tokenizer()

        lowercase_			:       Dict									=			'I was born in 92000, and this is falsé.'

        lowercase_			:       Any									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )
        lowercase_			:       Union[str, Any]									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )
        lowercase_			:       str									=			rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			self.get_rust_tokenizer()
        lowercase_			:       Optional[Any]									=			tokenizer.encode(__UpperCamelCase    )
        lowercase_			:       Dict									=			rust_tokenizer.encode(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			'This is a test'
        lowercase_			:       Any									=			[13, 1, 4398, 25, 21, 1289]
        lowercase_			:       Union[str, Any]									=			['▁', 'T', 'his', '▁is', '▁a', '▁test']
        lowercase_			:       str									=			['▁', '<unk>', 'his', '▁is', '▁a', '▁test']

        lowercase_			:       Optional[int]									=			DebertaVaTokenizer(__UpperCamelCase  ,keep_accents=__UpperCamelCase    )
        lowercase_			:       str									=			DebertaVaTokenizerFast(__UpperCamelCase  ,keep_accents=__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       List[Any]									=			tokenizer.tokenize(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Any									=			tokenizer.convert_ids_to_tokens(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       str									=			rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			rust_tokenizer.tokenize(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       str									=			rust_tokenizer.convert_ids_to_tokens(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

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

        lowercase_			:       List[str]									=			tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       List[str]									=			tokenizer.tokenize(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       List[Any]									=			tokenizer.convert_ids_to_tokens(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Tuple									=			rust_tokenizer.encode(__UpperCamelCase  ,add_special_tokens=__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			rust_tokenizer.tokenize(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Dict									=			rust_tokenizer.convert_ids_to_tokens(__UpperCamelCase    )
        self.assertListEqual(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Dict									=			DebertaVaTokenizer(__UpperCamelCase    )

        lowercase_			:       int									=			tokenizer.encode('sequence builders'    )
        lowercase_			:       Tuple									=			tokenizer.encode('multi-sequence build'    )

        lowercase_			:       Dict									=			tokenizer.build_inputs_with_special_tokens(__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			tokenizer.build_inputs_with_special_tokens(__UpperCamelCase  ,__UpperCamelCase    )

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







   @slow
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			{'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=__UpperCamelCase  ,model_name='microsoft/deberta-v2-xlarge'  ,revision='ad6e42c1532ddf3a15c39246b63f5559d558b670'  ,)

