


"""simple docstring"""







import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__      				=   logging.getLogger(__name__)


lowerCAmelCase__      				=   list(MODEL_WITH_LM_HEAD_MAPPING.keys())
lowerCAmelCase__      				=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={
               "help": (
                   "The model checkpoint for weights initialization. Leave None if you want to train a model from"
                   " scratch."
               )
           }   ,    )
       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowercase				)}   ,    )
       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,    )


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={"help": "The input training data file (a text file)."}				)
       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={
               "help": (
                   "The input training data files (multiple files in glob format). "
                   "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
               )
           }   ,    )
       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}   ,    )
       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}   ,    )
       a  :				Optional[str]   =field(
           default=lowercase   ,    metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}   ,    )
       a  :				bool   =field(
           default=lowercase   ,    metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}   ,    )

       a  :				bool   =field(
           default=lowercase   ,    metadata={"help": "Train with masked-language modeling loss instead of language modeling."}				)
       a  :				bool   =field(default=lowercase   ,    metadata={"help": "Whether ot not to use whole word mask."}				)
       a  :				float   =field(
           default=0.15   ,    metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}				)
       a  :				float   =field(
           default=1 / 6   ,    metadata={
               "help": (
                   "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
                   " modeling."
               )
           }   ,    )
       a  :				int   =field(
           default=5   ,    metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}				)

       a  :				int   =field(
           default=-1   ,    metadata={
               "help": (
                   "Optional input sequence length after tokenization."
                   "The training dataset will be truncated in block of this size for training."
                   "Default to the model max input length for single sentence inputs (take into account special tokens)."
               )
           }   ,    )
       a  :				bool   =field(
           default=lowercase   ,    metadata={"help": "Overwrite the cached training and evaluation sets"}				)

def 					a__			(				SCREAMING_SNAKE_CASE    :    DataTrainingArguments				,      SCREAMING_SNAKE_CASE    :    PreTrainedTokenizer				,      SCREAMING_SNAKE_CASE    :    bool = False				,      SCREAMING_SNAKE_CASE    :    Optional[str] = None				,      ):


  '''simple docstring'''







  def _dataset(SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    List[str]=None				):
    if args.line_by_line:
      if ref_path is not None:
        if not args.whole_word_mask or not args.mlm:
          raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask"				)
        return LineByLineWithRefDataset(
            tokenizer=SCREAMING_SNAKE_CASE				,      file_path=SCREAMING_SNAKE_CASE				,      block_size=args.block_size				,      ref_path=SCREAMING_SNAKE_CASE				,      )

      return LineByLineTextDataset(tokenizer=SCREAMING_SNAKE_CASE				,      file_path=SCREAMING_SNAKE_CASE				,      block_size=args.block_size				)
    else:
      return TextDataset(
          tokenizer=SCREAMING_SNAKE_CASE				,      file_path=SCREAMING_SNAKE_CASE				,      block_size=args.block_size				,      overwrite_cache=args.overwrite_cache				,      cache_dir=SCREAMING_SNAKE_CASE				,      )

  if evaluate:
    return _dataset(args.eval_data_file				,      args.eval_ref_file				)
  elif args.train_data_files:
    return ConcatDataset([_dataset(SCREAMING_SNAKE_CASE				) for f in glob(args.train_data_files				)]				)
  else:
    return _dataset(args.train_data_file				,      args.train_ref_file				)
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							parser.parse_args_into_dataclasses()

  if data_args.eval_data_file is None and training_args.do_eval:
    raise ValueError(
        "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
        "or remove the --do_eval argument."				)
  if (
      os.path.exists(training_args.output_dir				)
      and os.listdir(training_args.output_dir				)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
        " --overwrite_output_dir to overcome."				)

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,      datefmt="%m/%d/%Y %H:%M:%S"				,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,      )
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"				,      training_args.local_rank				,      training_args.device				,      training_args.n_gpu				,      bool(training_args.local_rank != -1				)				,      training_args.fpaa				,      )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank				):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s"				,      SCREAMING_SNAKE_CASE				)

  # Set seed
  set_seed(training_args.seed				)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  if model_args.config_name:
    lowerCAmelCase     :     int								=							AutoConfig.from_pretrained(model_args.config_name				,      cache_dir=model_args.cache_dir				)
  elif model_args.model_name_or_path:
    lowerCAmelCase     :     Union[str, Any]								=							AutoConfig.from_pretrained(model_args.model_name_or_path				,      cache_dir=model_args.cache_dir				)
  else:
    lowerCAmelCase     :     Optional[Any]								=							CONFIG_MAPPING[model_args.model_type]()
    logger.warning("You are instantiating a new config instance from scratch."				)

  if model_args.tokenizer_name:
    lowerCAmelCase     :     Any								=							AutoTokenizer.from_pretrained(model_args.tokenizer_name				,      cache_dir=model_args.cache_dir				)
  elif model_args.model_name_or_path:
    lowerCAmelCase     :     Dict								=							AutoTokenizer.from_pretrained(model_args.model_name_or_path				,      cache_dir=model_args.cache_dir				)
  else:
    raise ValueError(
        "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"
        " script, save it,and load it from here, using --tokenizer_name"				)

  if model_args.model_name_or_path:
    lowerCAmelCase     :     Dict								=							AutoModelWithLMHead.from_pretrained(
        model_args.model_name_or_path				,      from_tf=bool(".ckpt" in model_args.model_name_or_path				)				,      config=SCREAMING_SNAKE_CASE				,      cache_dir=model_args.cache_dir				,      )
  else:
    logger.info("Training new model from scratch"				)
    lowerCAmelCase     :     int								=							AutoModelWithLMHead.from_config(SCREAMING_SNAKE_CASE				)

  model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE				)				)

  if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
    raise ValueError(
        "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
        "--mlm flag (masked language modeling)."				)

  if data_args.block_size <= 0:
    lowerCAmelCase     :     Dict								=							tokenizer.max_len
    # Our input block size will be the max possible for the model
  else:
    lowerCAmelCase     :     Optional[int]								=							min(data_args.block_size				,      tokenizer.max_len				)

  # Get datasets

  lowerCAmelCase     :     List[Any]								=							(
      get_dataset(SCREAMING_SNAKE_CASE				,      tokenizer=SCREAMING_SNAKE_CASE				,      cache_dir=model_args.cache_dir				) if training_args.do_train else None
  )
  lowerCAmelCase     :     Union[str, Any]								=							(
      get_dataset(SCREAMING_SNAKE_CASE				,      tokenizer=SCREAMING_SNAKE_CASE				,      evaluate=SCREAMING_SNAKE_CASE				,      cache_dir=model_args.cache_dir				)
      if training_args.do_eval
      else None
  )
  if config.model_type == "xlnet":
    lowerCAmelCase     :     int								=							DataCollatorForPermutationLanguageModeling(
        tokenizer=SCREAMING_SNAKE_CASE				,      plm_probability=data_args.plm_probability				,      max_span_length=data_args.max_span_length				,      )
  else:
    if data_args.mlm and data_args.whole_word_mask:
      lowerCAmelCase     :     Any								=							DataCollatorForWholeWordMask(
          tokenizer=SCREAMING_SNAKE_CASE				,      mlm_probability=data_args.mlm_probability				)
    else:
      lowerCAmelCase     :     List[str]								=							DataCollatorForLanguageModeling(
          tokenizer=SCREAMING_SNAKE_CASE				,      mlm=data_args.mlm				,      mlm_probability=data_args.mlm_probability				)

    # Initialize our Trainer
  lowerCAmelCase     :     Any								=							Trainer(
      model=SCREAMING_SNAKE_CASE				,      args=SCREAMING_SNAKE_CASE				,      data_collator=SCREAMING_SNAKE_CASE				,      train_dataset=SCREAMING_SNAKE_CASE				,      eval_dataset=SCREAMING_SNAKE_CASE				,      prediction_loss_only=SCREAMING_SNAKE_CASE				,      )

  # Training
  if training_args.do_train:
    lowerCAmelCase     :     List[str]								=							(
        model_args.model_name_or_path
        if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path				)
        else None
    )
    trainer.train(model_path=SCREAMING_SNAKE_CASE				)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
  lowerCAmelCase     :     str								=							{}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"				)

    lowerCAmelCase     :     List[str]								=							trainer.evaluate()

    lowerCAmelCase     :     Union[str, Any]								=							math.exp(eval_output["eval_loss"]				)
    lowerCAmelCase     :     Tuple								=							{"perplexity": perplexity}

    lowerCAmelCase     :     int								=							os.path.join(training_args.output_dir				,      "eval_results_lm.txt"				)
    if trainer.is_world_master():
      with open(SCREAMING_SNAKE_CASE				,      "w"				) as writer:
        logger.info("***** Eval results *****"				)
        for key in sorted(result.keys()				):
          logger.info("  %s = %s"				,      SCREAMING_SNAKE_CASE				,      str(result[key]				)				)
          writer.write("%s = %s\n" % (key, str(result[key]				))				)

    results.update(SCREAMING_SNAKE_CASE				)

  return results
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[Any]				):


  '''simple docstring'''







  main()


if __name__ == "__main__":
     main()











"""simple docstring"""


import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					CodeGenTokenizer
 lowercase__ 					=					CodeGenTokenizerFast
 lowercase__ 					=					True
 lowercase__ 					=					{"add_prefix_space": True}
 lowercase__ 					=					False


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _snake_case							:		Tuple								=  [
       """l""",
       """o""",
       """w""",
       """e""",
       """r""",
       """s""",
       """t""",
       """i""",
       """d""",
       """n""",
       """\u0120""",
       """\u0120l""",
       """\u0120n""",
       """\u0120lo""",
       """\u0120low""",
       """er""",
       """\u0120lowest""",
       """\u0120newer""",
       """\u0120wider""",
       """<unk>""",
       """<|endoftext|>""",
   ]
   _snake_case							:		Tuple								=  dict(zip(a_,      range(len(a_		)		)		)		)
   _snake_case							:		str								=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
   _snake_case							:		List[Any]								=  {"""unk_token""": """<unk>"""}

   _snake_case							:		Optional[int]								=  os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["""vocab_file"""]		)
   _snake_case							:		Optional[Any]								=  os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["""merges_file"""]		)
   with open(self.vocab_file,      """w""",      encoding="""utf-8"""		) as fp:
     fp.write(json.dumps(a_		) + """\n"""		)
   with open(self.merges_file,      """w""",      encoding="""utf-8"""		) as fp:
     fp.write("""\n""".join(a_		)		)


 def  UpperCamelCase_   (				self:		Any,      **a_:		int		):




   '''simple docstring'''



   kwargs.update(self.special_tokens_map		)
   return CodeGenTokenizer.from_pretrained(self.tmpdirname,      **a_		)


 def  UpperCamelCase_   (				self:		Any,      **a_:		str		):




   '''simple docstring'''



   kwargs.update(self.special_tokens_map		)
   return CodeGenTokenizerFast.from_pretrained(self.tmpdirname,      **a_		)


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		Dict		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  """lower newer"""
   _snake_case							:		Tuple								=  """lower newer"""
   return input_text, output_text


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  CodeGenTokenizer(self.vocab_file,      self.merges_file,      **self.special_tokens_map		)
   _snake_case							:		Optional[Any]								=  """lower newer"""
   _snake_case							:		Optional[int]								=  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
   _snake_case							:		int								=  tokenizer.tokenize(a_,      add_prefix_space=a_		)
   self.assertListEqual(a_,      a_		)

   _snake_case							:		str								=  tokens + [tokenizer.unk_token]
   _snake_case							:		Optional[int]								=  [14, 15, 10, 9, 3, 2, 15, 19]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		),      a_		)


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




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		int								=  self.get_tokenizer()
   _snake_case							:		int								=  self.get_rust_tokenizer(add_prefix_space=a_		)

   _snake_case							:		Dict								=  """lower newer"""

   # Testing tokenization
   _snake_case							:		Dict								=  tokenizer.tokenize(a_,      add_prefix_space=a_		)
   _snake_case							:		List[str]								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,      a_		)

   # Testing conversion to ids without special tokens
   _snake_case							:		Optional[Any]								=  tokenizer.encode(a_,      add_special_tokens=a_,      add_prefix_space=a_		)
   _snake_case							:		Tuple								=  rust_tokenizer.encode(a_,      add_special_tokens=a_		)
   self.assertListEqual(a_,      a_		)

   # Testing conversion to ids with special tokens
   _snake_case							:		Tuple								=  self.get_rust_tokenizer(add_prefix_space=a_		)
   _snake_case							:		int								=  tokenizer.encode(a_,      add_prefix_space=a_		)
   _snake_case							:		Optional[Any]								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,      a_		)

   # Testing the unknown token
   _snake_case							:		Tuple								=  tokens + [rust_tokenizer.unk_token]
   _snake_case							:		List[Any]								=  [14, 15, 10, 9, 3, 2, 15, 19]
   self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a_		),      a_		)


 def  UpperCamelCase_   (				self:		Dict,      *a_:		Dict,      **a_:		int		):




   '''simple docstring'''



   pass


 def  UpperCamelCase_   (				self:		int,      a_:		List[Any]=15		):




   '''simple docstring'''



   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
       _snake_case							:		List[Any]								=  self.rust_tokenizer_class.from_pretrained(a_,      **a_		)

       # Simple input
       _snake_case							:		Any								=  """This is a simple input"""
       _snake_case							:		Optional[int]								=  ["""This is a simple input 1""", """This is a simple input 2"""]
       _snake_case							:		Optional[int]								=  ("""This is a simple input""", """This is a pair""")
       _snake_case							:		Optional[Any]								=  [
           ("""This is a simple input 1""", """This is a simple input 2"""),
           ("""This is a simple pair 1""", """This is a simple pair 2"""),
       ]

       # Simple input tests
       self.assertRaises(a_,      tokenizer_r.encode,      a_,      max_length=a_,      padding="""max_length"""		)

       # Simple input
       self.assertRaises(a_,      tokenizer_r.encode_plus,      a_,      max_length=a_,      padding="""max_length"""		)

       # Simple input
       self.assertRaises(
           a_,      tokenizer_r.batch_encode_plus,      a_,      max_length=a_,      padding="""max_length""",      )

       # Pair input
       self.assertRaises(a_,      tokenizer_r.encode,      a_,      max_length=a_,      padding="""max_length"""		)

       # Pair input
       self.assertRaises(a_,      tokenizer_r.encode_plus,      a_,      max_length=a_,      padding="""max_length"""		)

       # Pair input
       self.assertRaises(
           a_,      tokenizer_r.batch_encode_plus,      a_,      max_length=a_,      padding="""max_length""",      )


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  CodeGenTokenizer.from_pretrained(self.tmpdirname,      pad_token="""<pad>"""		)

   # Simple input
   _snake_case							:		List[Any]								=  """This is a simple input"""
   _snake_case							:		int								=  ["""This is a simple input looooooooong""", """This is a simple input"""]
   _snake_case							:		Any								=  ("""This is a simple input""", """This is a pair""")
   _snake_case							:		str								=  [
       ("""This is a simple input loooooong""", """This is a simple input"""),
       ("""This is a simple pair loooooong""", """This is a simple pair"""),
   ]

   _snake_case							:		str								=  tokenizer.pad_token_id

   _snake_case							:		Optional[int]								=  tokenizer(a_,      padding="""max_length""",      max_length=30,      return_tensors="""np"""		)
   _snake_case							:		Dict								=  tokenizer(a_,      padding=a_,      truncate=a_,      return_tensors="""np"""		)
   _snake_case							:		Tuple								=  tokenizer(*a_,      padding="""max_length""",      max_length=60,      return_tensors="""np"""		)
   _snake_case							:		Optional[Any]								=  tokenizer(a_,      padding=a_,      truncate=a_,      return_tensors="""np"""		)

   # s
   # test single string max_length padding
   self.assertEqual(out_s["""input_ids"""].shape[-1],      30		)
   self.assertTrue(pad_token_id in out_s["""input_ids"""]		)
   self.assertTrue(0 in out_s["""attention_mask"""]		)

   # s2
   # test automatic padding
   self.assertEqual(out_sa["""input_ids"""].shape[-1],      33		)
   # long slice doesn't have padding
   self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]		)
   self.assertFalse(0 in out_sa["""attention_mask"""][0]		)
   # short slice does have padding
   self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]		)
   self.assertTrue(0 in out_sa["""attention_mask"""][1]		)

   # p
   # test single pair max_length padding
   self.assertEqual(out_p["""input_ids"""].shape[-1],      60		)
   self.assertTrue(pad_token_id in out_p["""input_ids"""]		)
   self.assertTrue(0 in out_p["""attention_mask"""]		)

   # p2
   # test automatic padding pair
   self.assertEqual(out_pa["""input_ids"""].shape[-1],      52		)
   # long slice pair doesn't have padding
   self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]		)
   self.assertFalse(0 in out_pa["""attention_mask"""][0]		)
   # short slice pair does have padding
   self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]		)
   self.assertTrue(0 in out_pa["""attention_mask"""][1]		)


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




   '''simple docstring'''



   _snake_case							:		Tuple								=  """$$$"""
   _snake_case							:		List[Any]								=  CodeGenTokenizer.from_pretrained(self.tmpdirname,      bos_token=a_,      add_bos_token=a_		)

   _snake_case							:		str								=  """This is a simple input"""
   _snake_case							:		int								=  ["""This is a simple input 1""", """This is a simple input 2"""]

   _snake_case							:		Union[str, Any]								=  tokenizer.bos_token_id

   _snake_case							:		Tuple								=  tokenizer(a_		)
   _snake_case							:		Optional[Any]								=  tokenizer(a_		)

   self.assertEqual(out_s.input_ids[0],      a_		)
   self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids		)		)

   _snake_case							:		Optional[int]								=  tokenizer.decode(out_s.input_ids		)
   _snake_case							:		int								=  tokenizer.batch_decode(out_sa.input_ids		)

   self.assertEqual(decode_s.split()[0],      a_		)
   self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa		)		)


 @slow
 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""		)

   _snake_case							:		Dict								=  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
   _snake_case							:		Union[str, Any]								=  """\nif len_a > len_b:      result = a\nelse:      result = b"""

   _snake_case							:		Optional[Any]								=  tokenizer.encode(a_		)
   _snake_case							:		Dict								=  ["""^#""", re.escape("""<|endoftext|>"""		), """^'''""", """^\"\"\"""", """\n\n\n"""]
   _snake_case							:		Optional[Any]								=  tokenizer.decode(a_,      truncate_before_pattern=a_		)
   self.assertEqual(a_,      a_		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   pass


