





import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_UpperCAmelCase             =							2






class        snake_case_    :






  def __init__(							self			:					str       ,  *,  # begin keyword-only arguments
      _snake_case			:					Dict="<s>"       ,  _snake_case			:					Optional[Any]="<pad>"       ,  _snake_case			:					List[Any]="</s>"       ,  _snake_case			:					Tuple="<unk>"       ,  _snake_case			:					Tuple=None       ,  )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =     bos, unk, pad, eos
   __lowerCAmelCase					:	Any      =     []
   __lowerCAmelCase					:	Dict      =     []
   __lowerCAmelCase					:	Union[str, Any]      =     {}
   __lowerCAmelCase					:	List[str]      =     self.add_symbol(lowerCAmelCase__      )
   __lowerCAmelCase					:	Optional[int]      =     self.add_symbol(lowerCAmelCase__      )
   __lowerCAmelCase					:	int      =     self.add_symbol(lowerCAmelCase__      )
   __lowerCAmelCase					:	List[str]      =     self.add_symbol(lowerCAmelCase__      )
   if extra_special_symbols:
    for s in extra_special_symbols:
     self.add_symbol(lowerCAmelCase__      )
   __lowerCAmelCase					:	Tuple      =     len(self.symbols      )






  def __eq__(							self			:					int       ,  _snake_case			:					Optional[Any]      )->Dict:






   '''simple docstring'''

   return self.indices == other.indices






  def __getitem__(							self			:					Dict       ,  _snake_case			:					Union[str, Any]      )->List[Any]:






   '''simple docstring'''

   if idx < len(self.symbols      ):
    return self.symbols[idx]
   return self.unk_word






  def __len__(							self			:					Dict      )->int:






   '''simple docstring'''

   return len(self.symbols      )






  def __contains__(							self			:					Tuple       ,  _snake_case			:					Optional[Any]      )->Tuple:






   '''simple docstring'''

   return sym in self.indices






  @classmethod
  def 				UpperCAmelCase__						(							cls			:					Any       ,  _snake_case			:					Any      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =     cls()
   d.add_from_file(lowerCAmelCase__      )
   return d






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					Dict       ,  _snake_case			:					str=1       ,  _snake_case			:					List[str]=False      )->Dict:






   '''simple docstring'''

   if word in self.indices and not overwrite:
    __lowerCAmelCase					:	Dict      =     self.indices[word]
    __lowerCAmelCase					:	Optional[Any]      =     self.count[idx] + n
    return idx
   else:
    __lowerCAmelCase					:	Dict      =     len(self.symbols      )
    __lowerCAmelCase					:	Dict      =     idx
    self.symbols.append(lowerCAmelCase__      )
    self.count.append(lowerCAmelCase__      )
    return idx






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					int      )->List[str]:






   '''simple docstring'''

   return 0






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  _snake_case			:					Any      )->Any:






   '''simple docstring'''

   if isinstance(lowerCAmelCase__       ,  lowerCAmelCase__      ):
    try:
     with open(lowerCAmelCase__       ,  """r"""       ,  encoding="""utf-8"""      ) as fd:
      self.add_from_file(lowerCAmelCase__      )
    except FileNotFoundError as fnfe:
     raise fnfe
    except UnicodeError:
     raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(lowerCAmelCase__      )      )
    return

   __lowerCAmelCase					:	List[str]      =     f.readlines()
   __lowerCAmelCase					:	Any      =     self._load_meta(lowerCAmelCase__      )

   for line in lines[indices_start_line:]:
    try:
     __lowerCAmelCase					:	Union[str, Any]      =     line.rstrip().rsplit(""" """       ,  1      )
     if field == "#fairseq:overwrite":
      __lowerCAmelCase					:	Optional[Any]      =     True
      __lowerCAmelCase					:	List[str]      =     line.rsplit(""" """       ,  1      )
     else:
      __lowerCAmelCase					:	Tuple      =     False
     __lowerCAmelCase					:	List[str]      =     int(lowerCAmelCase__      )
     __lowerCAmelCase					:	int      =     line
     if word in self and not overwrite:
      raise RuntimeError(
          """Duplicate word found when loading Dictionary: '{}'. """
          """Duplicate words can overwrite earlier ones by adding the """
          """#fairseq:overwrite flag at the end of the corresponding row """
          """in the dictionary file. If using the Camembert model, please """
          """download an updated copy of the model file.""".format(lowerCAmelCase__      )      )
     self.add_symbol(lowerCAmelCase__       ,  n=lowerCAmelCase__       ,  overwrite=lowerCAmelCase__      )
    except ValueError:
     raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""      )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[Any]   )      ->   Any:
 # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
 # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
 __lowerCAmelCase					:	Any      =     dict((re.sub(r"""@@$"""   ,   """"""   ,   a_   ), v) if k.endswith("""@@"""   ) else (re.sub(r"""$"""   ,   """</w>"""   ,   a_   ), v) for k, v in d.items()   )
 __lowerCAmelCase					:	Union[str, Any]      =     "<s> <pad> </s> <unk>".split()
 # restore the special tokens
 for k in keep_keys:
  del da[F'''{k}</w>''']
  __lowerCAmelCase					:	Tuple      =     d[k]  # restore
 return da






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:List[str]   )      ->   Union[str, Any]:
 # prep
 if not os.path.exists(a_   ):
  raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''   )
 os.makedirs(a_   ,   exist_ok=a_   )
 print(F'''Writing results to {pytorch_dump_folder_path}'''   )

 # handle various types of models

 __lowerCAmelCase					:	List[Any]      =     os.path.join(a_   ,   """checkpoint.pt"""   )
 if not os.path.isfile(a_   ):
  raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''   )
 __lowerCAmelCase					:	Dict      =     torch.load(a_   ,   map_location="""cpu"""   )

 __lowerCAmelCase					:	List[Any]      =     chkpt["cfg"]["model"]

 # dicts
 __lowerCAmelCase					:	Any      =     os.path.join(a_   ,   """dict.txt"""   )
 if not os.path.isfile(a_   ):
  raise ValueError(F'''path to the file {dict_file} does not exist!'''   )
 __lowerCAmelCase					:	Any      =     Dictionary.load(a_   )
 __lowerCAmelCase					:	Dict      =     rewrite_dict_keys(src_dict.indices   )
 __lowerCAmelCase					:	List[str]      =     len(a_   )
 __lowerCAmelCase					:	int      =     os.path.join(a_   ,   VOCAB_FILES_NAMES["""vocab_file"""]   )
 print(F'''Generating {src_vocab_file} of {src_vocab_size} records'''   )
 with open(a_   ,   """w"""   ,   encoding="""utf-8"""   ) as f:
  f.write(json.dumps(a_   ,   ensure_ascii=a_   ,   indent=a_   )   )

 # merges_file (bpecodes)
 __lowerCAmelCase					:	int      =     os.path.join(a_   ,   """bpecodes"""   )
 if not os.path.isfile(a_   ):
  raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''   )

 __lowerCAmelCase					:	int      =     os.path.join(a_   ,   VOCAB_FILES_NAMES["""merges_file"""]   )
 shutil.copyfile(a_   ,   a_   )

 # model config
 __lowerCAmelCase					:	int      =     os.path.join(a_   ,   """config.json"""   )

 __lowerCAmelCase					:	Dict      =     {
     "activation_dropout": args["activation_dropout"],
     "architectures": ["BioGptForCausalLM"],
     "attention_probs_dropout_prob": args["attention_dropout"],
     "bos_token_id": 0,
     "eos_token_id": 2,
     "hidden_act": args["activation_fn"],
     "hidden_dropout_prob": args["dropout"],
     "hidden_size": args["decoder_embed_dim"],
     "initializer_range": 0.02,
     "intermediate_size": args["decoder_ffn_embed_dim"],
     "layer_norm_eps": 1e-12,
     "layerdrop": args["decoder_layerdrop"],
     "max_position_embeddings": args["max_target_positions"],
     "model_type": "biogpt",
     "num_attention_heads": args["decoder_attention_heads"],
     "num_hidden_layers": args["decoder_layers"],
     "pad_token_id": 1,
     "scale_embedding": not args["no_scale_embedding"],
     "tie_word_embeddings": args["share_decoder_input_output_embed"],
     "vocab_size": src_vocab_size,
 }

 # good hparam defaults to start with

 print(F'''Generating {biogpt_model_config_file}'''   )
 with open(a_   ,   """w"""   ,   encoding="""utf-8"""   ) as f:
  f.write(json.dumps(a_   ,   ensure_ascii=a_   ,   indent=a_   )   )

 # tokenizer config
 __lowerCAmelCase					:	Dict      =     os.path.join(a_   ,   a_   )

 __lowerCAmelCase					:	Optional[Any]      =     {
     "bos_token": "<s>",
     "eos_token": "</s>",
     "model_max_length": 1_024,
     "pad_token": "<pad>",
     "special_tokens_map_file": None,
     "tokenizer_class": "BioGptTokenizer",
     "unk_token": "<unk>",
 }

 print(F'''Generating {biogpt_tokenizer_config_file}'''   )
 with open(a_   ,   """w"""   ,   encoding="""utf-8"""   ) as f:
  f.write(json.dumps(a_   ,   ensure_ascii=a_   ,   indent=a_   )   )

 # model
 __lowerCAmelCase					:	Tuple      =     chkpt["model"]

 # remove unneeded keys
 __lowerCAmelCase					:	int      =     [
     "decoder.version",
 ]
 for k in ignore_keys:
  model_state_dict.pop(a_   ,   a_   )

 __lowerCAmelCase					:	Any      =     list(model_state_dict.keys()   )
 for layer_name in layer_names:
  if layer_name.endswith("""output_projection.weight"""   ):
   __lowerCAmelCase					:	int      =     model_state_dict.pop(a_   )
  else:
   __lowerCAmelCase					:	str      =     model_state_dict.pop(a_   )

 __lowerCAmelCase					:	Union[str, Any]      =     BioGptConfig.from_pretrained(a_   )
 __lowerCAmelCase					:	Optional[int]      =     BioGptForCausalLM(a_   )

 # check that it loads ok
 model_new.load_state_dict(a_   )

 # save
 __lowerCAmelCase					:	Tuple      =     os.path.join(a_   ,   a_   )
 print(F'''Generating {pytorch_weights_dump_path}'''   )
 torch.save(a_   ,   a_   )

 print("""Conversion is done!"""   )


if __name__ == "__main__":
      _UpperCAmelCase             =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--biogpt_checkpoint_path',
          default=None,
          type=str,
          required=True,
          help=(
              'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
              ' bpecodes, etc.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      _UpperCAmelCase             =							parser.parse_args()
      convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






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


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






@require_sentencepiece
@require_tokenizers
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   PegasusTokenizer
  A_      							=   PegasusTokenizerFast
  A_      							=   True
  A_      							=   True






  def 				UpperCAmelCase__						(							self			:					List[str]      )->Dict:






   '''simple docstring'''

   super().setUp()

   # We have a SentencePiece fixture for testing
   __lowerCAmelCase					:	Optional[int]      =       PegasusTokenizer(_snake_case      )
   tokenizer.save_pretrained(self.tmpdirname      )






  @cached_property
  def 				UpperCAmelCase__						(							self			:					str      )->Dict:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained("""google/pegasus-large"""      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  **_snake_case			:					Tuple      )->PegasusTokenizer:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained(self.tmpdirname       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					List[Any]      )->Tuple:






   '''simple docstring'''

   return ("This is a test", "This is a test")






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       """</s>"""
   __lowerCAmelCase					:	int      =       1

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






  def 				UpperCAmelCase__						(							self			:					int      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       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(_snake_case      )       ,  1103      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Optional[int]:






   '''simple docstring'''

   self.assertEqual(self.get_tokenizer().vocab_size       ,  1103      )






  def 				UpperCAmelCase__						(							self			:					Dict      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	Dict      =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	str      =       (
       """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>"""
   )
   __lowerCAmelCase					:	str      =       rust_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   __lowerCAmelCase					:	Tuple      =       py_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self._large_tokenizer
   # <mask_1> masks whole sentence while <mask_2> masks single word
   __lowerCAmelCase					:	Tuple      =       """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
   __lowerCAmelCase					:	List[str]      =       [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
   __lowerCAmelCase					:	str      =       tokenizer([raw_input_str]       ,  return_tensors=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[str]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       self._large_tokenizer
   # The tracebacks for the following asserts are **better** without messages or self.assertEqual
   assert tokenizer.vocab_size == 96103
   assert tokenizer.pad_token_id == 0
   assert tokenizer.eos_token_id == 1
   assert tokenizer.offset == 103
   assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
   assert tokenizer.unk_token == "<unk>"
   assert tokenizer.model_max_length == 1024
   __lowerCAmelCase					:	Tuple      =       """To ensure a smooth flow of bank resolutions."""
   __lowerCAmelCase					:	Optional[Any]      =       [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
   __lowerCAmelCase					:	int      =       tokenizer([raw_input_str]       ,  return_tensors=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )
   assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






  @require_torch
  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       ["""This is going to be way too long.""" * 150, """short example"""]
   __lowerCAmelCase					:	Union[str, Any]      =       ["""not super long but more than 5 tokens""", """tiny"""]
   __lowerCAmelCase					:	Dict      =       self._large_tokenizer(_snake_case       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )
   __lowerCAmelCase					:	Tuple      =       self._large_tokenizer(
       text_target=_snake_case       ,  max_length=5       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )

   assert batch.input_ids.shape == (2, 1024)
   assert batch.attention_mask.shape == (2, 1024)
   assert targets["input_ids"].shape == (2, 5)
   assert len(_snake_case      ) == 2  # input_ids, attention_mask.






  @slow
  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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=_snake_case       ,  model_name="""google/bigbird-pegasus-large-arxiv"""       ,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""       ,  )






@require_sentencepiece
@require_tokenizers
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   PegasusTokenizer
  A_      							=   PegasusTokenizerFast
  A_      							=   True
  A_      							=   True






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Tuple:






   '''simple docstring'''

   super().setUp()

   # We have a SentencePiece fixture for testing
   __lowerCAmelCase					:	Any      =       PegasusTokenizer(_snake_case       ,  offset=0       ,  mask_token_sent=_snake_case       ,  mask_token="""[MASK]"""      )
   tokenizer.save_pretrained(self.tmpdirname      )






  @cached_property
  def 				UpperCAmelCase__						(							self			:					Any      )->str:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""      )






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  **_snake_case			:					Optional[Any]      )->PegasusTokenizer:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained(self.tmpdirname       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Optional[int]      )->Union[str, Any]:






   '''simple docstring'''

   return ("This is a test", "This is a test")






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	List[str]      =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	int      =       (
       """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
       """ <pad> <pad> <pad>"""
   )
   __lowerCAmelCase					:	str      =       rust_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   __lowerCAmelCase					:	Tuple      =       py_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






  @require_torch
  def 				UpperCAmelCase__						(							self			:					str      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       ["""This is going to be way too long.""" * 1000, """short example"""]
   __lowerCAmelCase					:	Optional[int]      =       ["""not super long but more than 5 tokens""", """tiny"""]
   __lowerCAmelCase					:	str      =       self._large_tokenizer(_snake_case       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )
   __lowerCAmelCase					:	List[Any]      =       self._large_tokenizer(
       text_target=_snake_case       ,  max_length=5       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )

   assert batch.input_ids.shape == (2, 4096)
   assert batch.attention_mask.shape == (2, 4096)
   assert targets["input_ids"].shape == (2, 5)
   assert len(_snake_case      ) == 2  # input_ids, attention_mask.






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       (
       """This is an example string that is used to test the original TF implementation against the HF"""
       """ implementation"""
   )

   __lowerCAmelCase					:	Optional[Any]      =       self._large_tokenizer(_snake_case      ).input_ids

   self.assertListEqual(
       _snake_case       ,  [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]       ,  )