





"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


lowerCamelCase__				  =   [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
lowerCamelCase__				  =   [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

lowerCamelCase__				  =   (
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

lowerCamelCase__				  =   (
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

lowerCamelCase__				  =   [
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		List[Any]:




      """simple docstring"""

      for tf_name, hf_name in patterns:
            _UpperCamelCase      :	List[Any]						=       k.replace(lowercase_		,lowercase_			)
      return k




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       BigBirdPegasusConfig(**lowercase_			)
      _UpperCamelCase      :	Tuple						=       BigBirdPegasusForConditionalGeneration(lowercase_			)
      _UpperCamelCase      :	List[str]						=       torch_model.state_dict()
      _UpperCamelCase      :	int						=       {}

      # separating decoder weights
      _UpperCamelCase      :	Any						=       {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"			)}
      _UpperCamelCase      :	Optional[Any]						=       {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"			)}

      for k, v in tqdm(decoder_weights.items()		,"tf -> hf conversion"			):
            _UpperCamelCase      :	Tuple						=       [k.endswith(lowercase_			) for ending in KEYS_TO_IGNORE]
            if any(lowercase_			):
                  continue
            _UpperCamelCase      :	Tuple						=       DECODER_PATTERNS
            _UpperCamelCase      :	List[Any]						=       rename_state_dict_key(lowercase_		,lowercase_			)
            if new_k not in state_dict:
                  raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''			)
            if any(True if i in k else False for i in ["dense", "query", "key", "value"]			):
                  _UpperCamelCase      :	Dict						=       v.T
            _UpperCamelCase      :	str						=       torch.from_numpy(lowercase_			)
            assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

      for k, v in tqdm(remaining_weights.items()		,"tf -> hf conversion"			):
            _UpperCamelCase      :	Tuple						=       [k.endswith(lowercase_			) for ending in KEYS_TO_IGNORE]
            if any(lowercase_			):
                  continue
            _UpperCamelCase      :	Union[str, Any]						=       REMAINING_PATTERNS
            _UpperCamelCase      :	Union[str, Any]						=       rename_state_dict_key(lowercase_		,lowercase_			)
            if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
                  raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''			)
            if any(True if i in k else False for i in ["dense", "query", "key", "value"]			):
                  _UpperCamelCase      :	List[str]						=       v.T
            _UpperCamelCase      :	Union[str, Any]						=       torch.from_numpy(lowercase_			)
            if k != "pegasus/embeddings/position_embeddings":
                  assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

      _UpperCamelCase      :	List[Any]						=       mapping["model.embed_positions.weight"]
      _UpperCamelCase      :	Dict						=       mapping.pop("model.embed_positions.weight"			)
      _UpperCamelCase,  _UpperCamelCase      :	Tuple						=       torch_model.load_state_dict(lowercase_		,strict=lowercase_			)
      _UpperCamelCase      :	List[Any]						=       [
          k
          for k in missing
          if k
          not in [
              "final_logits_bias",
              "model.encoder.embed_tokens.weight",
              "model.decoder.embed_tokens.weight",
              "lm_head.weight",
          ]
      ]
      assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
      assert extra == [], F'''no matches found for the following tf keys {extra}'''
      return torch_model




def 					lowercase__    (					lowercase_			)					->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       tf.train.list_variables(lowercase_			)
      _UpperCamelCase      :	Tuple						=       {}
      _UpperCamelCase      :	List[Any]						=       ["global_step"]
      for name, shape in tqdm(lowercase_		,desc="converting tf checkpoint to dict"			):
            _UpperCamelCase      :	Tuple						=       any(pat in name for pat in ignore_name			)
            if skip_key:
                  continue
            _UpperCamelCase      :	List[str]						=       tf.train.load_variable(lowercase_		,lowercase_			)
            _UpperCamelCase      :	Tuple						=       array
      return tf_weights




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       get_tf_weights_as_numpy(lowercase_			)
      _UpperCamelCase      :	Optional[Any]						=       convert_bigbird_pegasus(lowercase_		,lowercase_			)
      torch_model.save_pretrained(lowercase_			)


if __name__ == "__main__":
  lowerCamelCase__				  =   argparse.ArgumentParser()
  parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
  parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
  lowerCamelCase__				  =   parser.parse_args()
  lowerCamelCase__				  =   {}
  convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







"""simple docstring"""


import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english



@require_tokenizers
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					RoCBertTokenizer
  SCREAMING_SNAKE_CASE__							:Dict	     =					None
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					False
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					True
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					filter_non_english

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Dict:
        super().setUp()

        _UpperCamelCase      :	Any						=       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
        _UpperCamelCase      :	List[str]						=       {}
        _UpperCamelCase      :	Tuple						=       {}
        for i, value in enumerate(__a	):
              _UpperCamelCase      :	List[str]						=       i
              _UpperCamelCase      :	Optional[Any]						=       i
        _UpperCamelCase      :	Dict						=       os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["vocab_file"]	)
        _UpperCamelCase      :	Optional[Any]						=       os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["word_shape_file"]	)
        _UpperCamelCase      :	Any						=       os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["word_pronunciation_file"]	)
        with open(self.vocab_file					,   "w"					,   encoding="utf-8"	) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)
        with open(self.word_shape_file					,   "w"					,   encoding="utf-8"	) as word_shape_writer:
              json.dump(__a					,   __a					,   ensure_ascii=__a	)
        with open(self.word_pronunciation_file					,   "w"					,   encoding="utf-8"	) as word_pronunciation_writer:
              json.dump(__a					,   __a					,   ensure_ascii=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Optional[int]:
        _UpperCamelCase      :	Tuple						=       self.tokenizer_class(self.vocab_file					,   self.word_shape_file					,   self.word_pronunciation_file	)

        _UpperCamelCase      :	int						=       tokenizer.tokenize("你好[SEP]你是谁"	)
        self.assertListEqual(__a					,   ["你", "好", "[SEP]", "你", "是", "谁"]	)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__a	)					,   [5, 6, 2, 5, 7, 8]	)
        self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__a	)					,   [5, 6, 2, 5, 7, 8]	)
        self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__a	)					,   [5, 6, 2, 5, 7, 8]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Dict:
        _UpperCamelCase      :	Dict						=       RoCBertBasicTokenizer()

        self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"	)					,   ["ah", "\u535A", "\u63A8", "zz"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Any:
        _UpperCamelCase      :	List[Any]						=       RoCBertBasicTokenizer(do_lower_case=__a	)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "	)					,   ["hello", "!", "how", "are", "you", "?"]	)
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)					,   ["hello"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Tuple:
        _UpperCamelCase      :	Optional[Any]						=       RoCBertBasicTokenizer(do_lower_case=__a					,   strip_accents=__a	)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)					,   ["hällo", "!", "how", "are", "you", "?"]	)
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)					,   ["h\u00E9llo"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:
        _UpperCamelCase      :	Dict						=       RoCBertBasicTokenizer(do_lower_case=__a					,   strip_accents=__a	)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)					,   ["hallo", "!", "how", "are", "you", "?"]	)
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)					,   ["hello"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Union[str, Any]:
        _UpperCamelCase      :	List[str]						=       RoCBertBasicTokenizer(do_lower_case=__a	)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)					,   ["hallo", "!", "how", "are", "you", "?"]	)
        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"	)					,   ["hello"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Optional[int]:
        _UpperCamelCase      :	Tuple						=       RoCBertBasicTokenizer(do_lower_case=__a	)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "	)					,   ["HeLLo", "!", "how", "Are", "yoU", "?"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[str]:
        _UpperCamelCase      :	Union[str, Any]						=       RoCBertBasicTokenizer(do_lower_case=__a					,   strip_accents=__a	)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)					,   ["HäLLo", "!", "how", "Are", "yoU", "?"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				List[Any]:
        _UpperCamelCase      :	Tuple						=       RoCBertBasicTokenizer(do_lower_case=__a					,   strip_accents=__a	)

        self.assertListEqual(
            tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)					,   ["HaLLo", "!", "how", "Are", "yoU", "?"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				int:
        _UpperCamelCase      :	int						=       RoCBertBasicTokenizer(do_lower_case=__a					,   never_split=["[UNK]"]	)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"	)					,   ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Optional[Any]:
        _UpperCamelCase      :	Optional[int]						=       ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

        _UpperCamelCase      :	Any						=       {}
        for i, token in enumerate(__a	):
              _UpperCamelCase      :	str						=       i
        _UpperCamelCase      :	Optional[int]						=       RoCBertWordpieceTokenizer(vocab=__a					,   unk_token="[UNK]"	)

        self.assertListEqual(tokenizer.tokenize(""	)					,   []	)

        self.assertListEqual(tokenizer.tokenize("unwanted running"	)					,   ["un", "##want", "##ed", "runn", "##ing"]	)

        self.assertListEqual(tokenizer.tokenize("unwantedX running"	)					,   ["[UNK]", "runn", "##ing"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[Any]:
        self.assertTrue(_is_whitespace(" "	)	)
        self.assertTrue(_is_whitespace("\t"	)	)
        self.assertTrue(_is_whitespace("\r"	)	)
        self.assertTrue(_is_whitespace("\n"	)	)
        self.assertTrue(_is_whitespace("\u00A0"	)	)

        self.assertFalse(_is_whitespace("A"	)	)
        self.assertFalse(_is_whitespace("-"	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Dict:
        self.assertTrue(_is_control("\u0005"	)	)

        self.assertFalse(_is_control("A"	)	)
        self.assertFalse(_is_control(" "	)	)
        self.assertFalse(_is_control("\t"	)	)
        self.assertFalse(_is_control("\r"	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Optional[int]:
        self.assertTrue(_is_punctuation("-"	)	)
        self.assertTrue(_is_punctuation("$"	)	)
        self.assertTrue(_is_punctuation("`"	)	)
        self.assertTrue(_is_punctuation("."	)	)

        self.assertFalse(_is_punctuation("A"	)	)
        self.assertFalse(_is_punctuation(" "	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				List[Any]:
        _UpperCamelCase      :	Optional[Any]						=       self.get_tokenizer()

        # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
        self.assertListEqual([tokenizer.tokenize(__a	) for t in ["Test", "\xad", "test"]]					,   [["[UNK]"], [], ["[UNK]"]]	)

        if self.test_rust_tokenizer:
              _UpperCamelCase      :	Tuple						=       self.get_rust_tokenizer()
              self.assertListEqual(
                  [rust_tokenizer.tokenize(__a	) for t in ["Test", "\xad", "test"]]					,   [["[UNK]"], [], ["[UNK]"]]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Optional[Any]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                    _UpperCamelCase      :	int						=       self.rust_tokenizer_class.from_pretrained(__a					,   **__a	)

                    _UpperCamelCase      :	Union[str, Any]						=       F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
                    _UpperCamelCase      :	Optional[Any]						=       tokenizer_r.encode_plus(
                        __a					,   return_attention_mask=__a					,   return_token_type_ids=__a					,   return_offsets_mapping=__a					,   add_special_tokens=__a					,   )

                    _UpperCamelCase      :	List[Any]						=       tokenizer_r.do_lower_case if hasattr(__a					,   "do_lower_case"	) else False
                    _UpperCamelCase      :	Dict						=       (
                        [
                            ((0, 0), tokenizer_r.cls_token),
                            ((0, 1), "A"),
                            ((1, 2), ","),
                            ((3, 5), "na"),
                            ((5, 6), "##ï"),
                            ((6, 8), "##ve"),
                            ((9, 15), tokenizer_r.mask_token),
                            ((16, 21), "Allen"),
                            ((21, 23), "##NL"),
                            ((23, 24), "##P"),
                            ((25, 33), "sentence"),
                            ((33, 34), "."),
                            ((0, 0), tokenizer_r.sep_token),
                        ]
                        if not do_lower_case
                        else [
                            ((0, 0), tokenizer_r.cls_token),
                            ((0, 1), "a"),
                            ((1, 2), ","),
                            ((3, 8), "naive"),
                            ((9, 15), tokenizer_r.mask_token),
                            ((16, 21), "allen"),
                            ((21, 23), "##nl"),
                            ((23, 24), "##p"),
                            ((25, 33), "sentence"),
                            ((33, 34), "."),
                            ((0, 0), tokenizer_r.sep_token),
                        ]
                    )

                    self.assertEqual(
                        [e[1] for e in expected_results]					,   tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]	)	)
                    self.assertEqual([e[0] for e in expected_results]					,   tokens["offset_mapping"]	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				List[Any]:
        _UpperCamelCase      :	Optional[Any]						=       ["的", "人", "有"]
        _UpperCamelCase      :	int						=       "".join(__a	)
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                    _UpperCamelCase      :	int						=       True
                    _UpperCamelCase      :	Any						=       self.tokenizer_class.from_pretrained(__a					,   **__a	)
                    _UpperCamelCase      :	Optional[Any]						=       self.rust_tokenizer_class.from_pretrained(__a					,   **__a	)

                    _UpperCamelCase      :	int						=       tokenizer_p.encode(__a					,   add_special_tokens=__a	)
                    _UpperCamelCase      :	int						=       tokenizer_r.encode(__a					,   add_special_tokens=__a	)

                    _UpperCamelCase      :	List[Any]						=       tokenizer_r.convert_ids_to_tokens(__a	)
                    _UpperCamelCase      :	Union[str, Any]						=       tokenizer_p.convert_ids_to_tokens(__a	)

                    # it is expected that each Chinese character is not preceded by "##"
                    self.assertListEqual(__a					,   __a	)
                    self.assertListEqual(__a					,   __a	)

                    _UpperCamelCase      :	Any						=       False
                    _UpperCamelCase      :	Dict						=       self.rust_tokenizer_class.from_pretrained(__a					,   **__a	)
                    _UpperCamelCase      :	Any						=       self.tokenizer_class.from_pretrained(__a					,   **__a	)

                    _UpperCamelCase      :	Any						=       tokenizer_r.encode(__a					,   add_special_tokens=__a	)
                    _UpperCamelCase      :	Any						=       tokenizer_p.encode(__a					,   add_special_tokens=__a	)

                    _UpperCamelCase      :	Union[str, Any]						=       tokenizer_r.convert_ids_to_tokens(__a	)
                    _UpperCamelCase      :	Dict						=       tokenizer_p.convert_ids_to_tokens(__a	)

                    # it is expected that only the first Chinese character is not preceded by "##".
                    _UpperCamelCase      :	Any						=       [
                        F'''##{token}''' if idx != 0 else token for idx, token in enumerate(__a	)
                    ]
                    self.assertListEqual(__a					,   __a	)
                    self.assertListEqual(__a					,   __a	)

  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Any:
        _UpperCamelCase      :	Dict						=       self.tokenizer_class(self.vocab_file					,   self.word_shape_file					,   self.word_pronunciation_file	)

        _UpperCamelCase      :	Optional[int]						=       tokenizer.encode("你好"					,   add_special_tokens=__a	)
        _UpperCamelCase      :	Dict						=       tokenizer.encode("你是谁"					,   add_special_tokens=__a	)

        _UpperCamelCase      :	Union[str, Any]						=       tokenizer.build_inputs_with_special_tokens(__a	)
        _UpperCamelCase      :	Tuple						=       tokenizer.build_inputs_with_special_tokens(__a					,   __a	)

        assert encoded_sentence == [1] + text + [2]
        assert encoded_pair == [1] + text + [2] + text_a + [2]


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				int:
        _UpperCamelCase      :	Optional[Any]						=       self.get_tokenizers(do_lower_case=__a	)
        for tokenizer in tokenizers:
              with self.subTest(F'''{tokenizer.__class__.__name__}'''	):
                    _UpperCamelCase      :	int						=       "你好，你是谁"
                    _UpperCamelCase      :	Any						=       tokenizer.tokenize(__a	)
                    _UpperCamelCase      :	Optional[Any]						=       tokenizer.convert_tokens_to_ids(__a	)
                    _UpperCamelCase      :	List[str]						=       tokenizer.convert_tokens_to_shape_ids(__a	)
                    _UpperCamelCase      :	Any						=       tokenizer.convert_tokens_to_pronunciation_ids(__a	)
                    _UpperCamelCase      :	Optional[int]						=       tokenizer.prepare_for_model(
                        __a					,   __a					,   __a					,   add_special_tokens=__a	)

                    _UpperCamelCase      :	Tuple						=       tokenizer.encode_plus(__a					,   add_special_tokens=__a	)

                    self.assertEqual(__a					,   __a	)
