





'''simple docstring'''



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__magic_name__   :						int    =			logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

__magic_name__   :						Union[str, Any]    =			{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__magic_name__   :						List[Any]    =			{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__magic_name__   :						Tuple    =			sorted(arg_to_scheduler.keys())
__magic_name__   :						Tuple    =			"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""


class       __SCREAMING_SNAKE_CASE	(						pl.LightningModule			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=None						,   lowerCamelCase="base"						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None						,   **lowerCamelCase						,   ):
       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(lowerCamelCase	)
       _snake_case     		=				0
       _snake_case     		=				Path(self.hparams.output_dir	)
       _snake_case     		=				self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
         _snake_case     		=				AutoConfig.from_pretrained(
             self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path						,   **({"num_labels": num_labels} if num_labels is not None else {})						,   cache_dir=lowerCamelCase						,   **lowerCamelCase						,   )
       else:
         _snake_case     		=				config

       _snake_case     		=				("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
       for p in extra_model_params:
         if getattr(self.hparams						,   lowerCamelCase						,   lowerCamelCase	):
           assert hasattr(self.config						,   lowerCamelCase	), F'''model config doesn\'t have a `{p}` attribute'''
           setattr(self.config						,   lowerCamelCase						,   getattr(self.hparams						,   lowerCamelCase	)	)

       if tokenizer is None:
         _snake_case     		=				AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,   cache_dir=lowerCamelCase						,   )
       else:
         _snake_case     		=				tokenizer
       _snake_case     		=				MODEL_MODES[mode]
       if model is None:
         _snake_case     		=				self.model_type.from_pretrained(
             self.hparams.model_name_or_path						,   from_tf=bool(".ckpt" in self.hparams.model_name_or_path	)						,   config=self.config						,   cache_dir=lowerCamelCase						,   )
       else:
         _snake_case     		=				model

     def 	UpperCamelCase(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       _snake_case     		=				self.model_type.from_pretrained(*lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				arg_to_scheduler[self.hparams.lr_scheduler]
       _snake_case     		=				get_schedule_func(
           self.opt						,   num_warmup_steps=self.hparams.warmup_steps						,   num_training_steps=self.total_steps()	)
       _snake_case     		=				{"scheduler": scheduler, "interval": "step", "frequency": 1}
       return scheduler

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model
       _snake_case     		=				["bias", "LayerNorm.weight"]
       _snake_case     		=				[
           {
               "params": [
                   p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)
               ],  # check this named paramters
               "weight_decay": self.hparams.weight_decay,
           },
           {
               "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
               "weight_decay": 0.0,
           },
       ]
       if self.hparams.adafactor:
         _snake_case     		=				Adafactor(
             lowerCamelCase						,   lr=self.hparams.learning_rate						,   scale_parameter=lowerCamelCase						,   relative_step=lowerCamelCase	)

       else:
         _snake_case     		=				AdamW(
             lowerCamelCase						,   lr=self.hparams.learning_rate						,   eps=self.hparams.adam_epsilon	)
       _snake_case     		=				optimizer

       _snake_case     		=				self.get_lr_scheduler()

       return [optimizer], [scheduler]

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       return self.validation_step(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.validation_end(lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				max(1						,   self.hparams.gpus	)  # TODO: consider num_tpu_cores
       _snake_case     		=				self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
       return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if stage == "test":
         _snake_case     		=				len(self.test_dataloader().dataset	)
       else:
         _snake_case     		=				self.get_dataloader("train"						,   self.hparams.train_batch_size						,   shuffle=lowerCamelCase	)
         _snake_case     		=				len(self.train_dataloader().dataset	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = False	):
       raise NotImplementedError("You must implement this for your task"	)

     def 	UpperCamelCase(					self	):
       return self.train_loader

     def 	UpperCamelCase(					self	):
       return self.get_dataloader("dev"						,   self.hparams.eval_batch_size						,   shuffle=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       return self.get_dataloader("test"						,   self.hparams.eval_batch_size						,   shuffle=lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return os.path.join(
           self.hparams.data_dir						,   "cached_{}_{}_{}".format(
               lowerCamelCase						,   list(filter(lowerCamelCase						,   self.hparams.model_name_or_path.split("/"	)	)	).pop()						,   str(self.hparams.max_seq_length	)						,   )						,   )

     @pl.utilities.rank_zero_only
     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.output_dir.joinpath("best_tfmr"	)
       _snake_case     		=				self.step_count
       self.model.save_pretrained(lowerCamelCase	)
       self.tokenizer.save_pretrained(lowerCamelCase	)






     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase	):
       parser.add_argument(
           "--model_name_or_path"						,   default=lowerCamelCase						,   type=lowerCamelCase						,   required=lowerCamelCase						,   help="Path to pretrained model or model identifier from huggingface.co/models"						,   )
       parser.add_argument(
           "--config_name"						,   default=""						,   type=lowerCamelCase						,   help="Pretrained config name or path if not the same as model_name"	)
       parser.add_argument(
           "--tokenizer_name"						,   default=lowerCamelCase						,   type=lowerCamelCase						,   help="Pretrained tokenizer name or path if not the same as model_name"						,   )
       parser.add_argument(
           "--cache_dir"						,   default=str(Path(lowerCamelCase	).parent / "test_run" / "cache"	)						,   type=lowerCamelCase						,   help="Where do you want to store the pre-trained models downloaded from huggingface.co"						,   )
       parser.add_argument(
           "--encoder_layerdrop"						,   type=lowerCamelCase						,   help="Encoder layer dropout probability (Optional). Goes into model.config"						,   )
       parser.add_argument(
           "--decoder_layerdrop"						,   type=lowerCamelCase						,   help="Decoder layer dropout probability (Optional). Goes into model.config"						,   )
       parser.add_argument(
           "--dropout"						,   type=lowerCamelCase						,   help="Dropout probability (Optional). Goes into model.config"						,   )
       parser.add_argument(
           "--attention_dropout"						,   type=lowerCamelCase						,   help="Attention dropout probability (Optional). Goes into model.config"						,   )
       parser.add_argument("--learning_rate"						,   default=5e-5						,   type=lowerCamelCase						,   help="The initial learning rate for Adam."	)
       parser.add_argument(
           "--lr_scheduler"						,   default="linear"						,   choices=lowerCamelCase						,   metavar=lowerCamelCase						,   type=lowerCamelCase						,   help="Learning rate scheduler"						,   )
       parser.add_argument("--weight_decay"						,   default=0.0						,   type=lowerCamelCase						,   help="Weight decay if we apply some."	)
       parser.add_argument("--adam_epsilon"						,   default=1e-8						,   type=lowerCamelCase						,   help="Epsilon for Adam optimizer."	)
       parser.add_argument("--warmup_steps"						,   default=0						,   type=lowerCamelCase						,   help="Linear warmup over warmup_steps."	)
       parser.add_argument("--num_workers"						,   default=4						,   type=lowerCamelCase						,   help="kwarg passed to DataLoader"	)
       parser.add_argument("--num_train_epochs"						,   dest="max_epochs"						,   default=3						,   type=lowerCamelCase	)
       parser.add_argument("--train_batch_size"						,   default=32						,   type=lowerCamelCase	)
       parser.add_argument("--eval_batch_size"						,   default=32						,   type=lowerCamelCase	)
       parser.add_argument("--adafactor"						,   action="store_true"	)







class       __SCREAMING_SNAKE_CASE	(						pl.Callback			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       if (
           trainer.is_global_zero and trainer.global_rank == 0
       ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
         pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class       __SCREAMING_SNAKE_CASE	(						pl.Callback			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       # print(pl_module.model.rag)
       for name, param in pl_module.model.rag.named_parameters():
         if param.grad is None:
           print(lowerCamelCase	)







class       __SCREAMING_SNAKE_CASE	(						pl.Callback			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				trainer.lr_schedulers[0]["scheduler"]
       _snake_case     		=				{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
       pl_module.logger.log_metrics(lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       rank_zero_info("***** Validation results *****"	)
       _snake_case     		=				trainer.callback_metrics
       # Log results
       for key in sorted(lowerCamelCase	):
         if key not in ["log", "progress_bar"]:
           rank_zero_info("{} = {}\n".format(lowerCamelCase						,   str(metrics[key]	)	)	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       rank_zero_info("***** Test results *****"	)
       _snake_case     		=				trainer.callback_metrics
       # Log and save results to file
       _snake_case     		=				os.path.join(pl_module.hparams.output_dir						,   "test_results.txt"	)
       with open(lowerCamelCase						,   "w"	) as writer:
         for key in sorted(lowerCamelCase	):
           if key not in ["log", "progress_bar"]:
             rank_zero_info("{} = {}\n".format(lowerCamelCase						,   str(metrics[key]	)	)	)
             writer.write("{} = {}\n".format(lowerCamelCase						,   str(metrics[key]	)	)	)







def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  parser.add_argument(
      "--output_dir"					,	default=str(Path(SCREAMING_SNAKE_CASE__       ).parent / "test_run" / "model_checkpoints"       )					,	type=SCREAMING_SNAKE_CASE__					,	help="The output directory where the model predictions and checkpoints will be written."					,	)
  parser.add_argument(
      "--fp16"					,	action="store_true"					,	help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"					,	)

  parser.add_argument(
      "--fp16_opt_level"					,	type=SCREAMING_SNAKE_CASE__					,	default="O2"					,	help=(
          "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
          "See details at https://nvidia.github.io/apex/amp.html"
      )					,	)
  parser.add_argument("--n_tpu_cores"					,	dest="tpu_cores"					,	type=SCREAMING_SNAKE_CASE__       )
  parser.add_argument("--max_grad_norm"					,	dest="gradient_clip_val"					,	default=1.0					,	type=SCREAMING_SNAKE_CASE__					,	help="Max gradient norm"       )
  parser.add_argument("--do_train"					,	action="store_true"					,	help="Whether to run training."       )
  parser.add_argument("--do_predict"					,	action="store_true"					,	help="Whether to run predictions on the test set."       )
  parser.add_argument(
      "--gradient_accumulation_steps"					,	dest="accumulate_grad_batches"					,	type=SCREAMING_SNAKE_CASE__					,	default=1					,	help="Number of updates steps to accumulate before performing a backward/update pass."					,	)
  parser.add_argument("--seed"					,	type=SCREAMING_SNAKE_CASE__					,	default=42					,	help="random seed for initialization"       )
  parser.add_argument(
      "--data_dir"					,	default=str(Path(SCREAMING_SNAKE_CASE__       ).parent / "test_run" / "dummy-train-data"       )					,	type=SCREAMING_SNAKE_CASE__					,	help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."					,	)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__=[]					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None					,	**SCREAMING_SNAKE_CASE__					,	):







  '''simple docstring'''



  pl.seed_everything(args.seed       )

  # init model
  _snake_case     		=				Path(model.hparams.output_dir       )
  odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )

  # add custom checkpoints
  if checkpoint_callback is None:
    _snake_case     		=				pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir					,	prefix="checkpoint"					,	monitor="val_loss"					,	mode="min"					,	save_top_k=1       )
  if early_stopping_callback:
    extra_callbacks.append(SCREAMING_SNAKE_CASE__       )
  if logging_callback is None:
    _snake_case     		=				LoggingCallback()

  _snake_case     		=				{}

  if args.fpaa:
    _snake_case     		=				16

  if args.gpus > 1:
    _snake_case     		=				"auto"
    _snake_case     		=				"ddp"

  _snake_case     		=				args.accumulate_grad_batches
  _snake_case     		=				None
  _snake_case     		=				"auto"

  _snake_case     		=				pl.Trainer.from_argparse_args(
      SCREAMING_SNAKE_CASE__					,	weights_summary=SCREAMING_SNAKE_CASE__					,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					,	logger=SCREAMING_SNAKE_CASE__					,	val_check_interval=1					,	num_sanity_val_steps=2					,	**SCREAMING_SNAKE_CASE__					,	)

  if args.do_train:
    trainer.fit(SCREAMING_SNAKE_CASE__       )

  else:
    print("RAG modeling tests with new set functions successfuly executed!"       )
  return trainer









'''simple docstring'''



import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


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



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   LongformerTokenizer
     UpperCAmelCase__       :						Dict      =   True
     UpperCAmelCase__       :						Union[str, Any]      =   LongformerTokenizerFast
     UpperCAmelCase__       :						List[str]      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

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

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

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       kwargs.update(self.special_tokens_map	)
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       kwargs.update(self.special_tokens_map	)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				"lower newer"
       _snake_case     		=				"lower newer"
       return input_text, output_text

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.tokenizer_class(self.vocab_file						,   self.merges_file						,   **self.special_tokens_map	)
       _snake_case     		=				"lower newer"
       _snake_case     		=				["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       _snake_case     		=				tokenizer.tokenize(lowerCamelCase	)  # , add_prefix_space=True)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokens + [tokenizer.unk_token]
       _snake_case     		=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       self.assertListEqual(tokenizer.encode("Hello world!"						,   add_special_tokens=lowerCamelCase	)						,   [0, 31_414, 232, 328, 2]	)
       self.assertListEqual(
           tokenizer.encode("Hello world! cécé herlolip 418"						,   add_special_tokens=lowerCamelCase	)						,   [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]						,   )

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"	)

       _snake_case     		=				tokenizer.encode("sequence builders"						,   add_special_tokens=lowerCamelCase	)
       _snake_case     		=				tokenizer.encode("multi-sequence build"						,   add_special_tokens=lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(
           "sequence builders"						,   add_special_tokens=lowerCamelCase						,   add_prefix_space=lowerCamelCase	)
       _snake_case     		=				tokenizer.encode(
           "sequence builders"						,   "multi-sequence build"						,   add_special_tokens=lowerCamelCase						,   add_prefix_space=lowerCamelCase	)

       _snake_case     		=				tokenizer.build_inputs_with_special_tokens(lowerCamelCase	)
       _snake_case     		=				tokenizer.build_inputs_with_special_tokens(lowerCamelCase						,   lowerCamelCase	)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				"Encode this sequence."
       _snake_case     		=				tokenizer.byte_encoder[" ".encode("utf-8"	)[0]]

       # Testing encoder arguments
       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   add_prefix_space=lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
       self.assertNotEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   add_prefix_space=lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
       self.assertEqual(lowerCamelCase						,   lowerCamelCase	)

       tokenizer.add_special_tokens({"bos_token": "<s>"}	)
       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[1]	)[0]
       self.assertNotEqual(lowerCamelCase						,   lowerCamelCase	)

       # Testing spaces after special tokens
       _snake_case     		=				"<mask>"
       tokenizer.add_special_tokens(
           {"mask_token": AddedToken(lowerCamelCase						,   lstrip=lowerCamelCase						,   rstrip=lowerCamelCase	)}	)  # mask token has a left space
       _snake_case     		=				tokenizer.convert_tokens_to_ids(lowerCamelCase	)

       _snake_case     		=				"Encode <mask> sequence"
       _snake_case     		=				"Encode <mask>sequence"

       _snake_case     		=				tokenizer.encode(lowerCamelCase	)
       _snake_case     		=				encoded.index(lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
       self.assertEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(lowerCamelCase	)
       _snake_case     		=				encoded.index(lowerCamelCase	)
       _snake_case     		=				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
       self.assertNotEqual(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
           _snake_case     		=				self.tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
           _snake_case     		=				"A, <mask> AllenNLP sentence."
           _snake_case     		=				tokenizer_r.encode_plus(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)
           _snake_case     		=				tokenizer_p.encode_plus(lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   return_token_type_ids=lowerCamelCase	)

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r["token_type_ids"]	)						,   sum(tokens_p["token_type_ids"]	)	)

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)						,   sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)						,   )

           _snake_case     		=				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
           _snake_case     		=				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p["input_ids"]						,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
           self.assertSequenceEqual(tokens_r["input_ids"]						,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

           self.assertSequenceEqual(
               lowerCamelCase						,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
           self.assertSequenceEqual(
               lowerCamelCase						,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)

     def 	UpperCamelCase(					self	):
       for trim_offsets, add_prefix_space in itertools.product([True, False]						,   repeat=2	):
         _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
             self.tmpdirname						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)

         _snake_case     		=				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
         _snake_case     		=				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()	)

         self.assertEqual(pre_tokenizer_state["add_prefix_space"]						,   lowerCamelCase	)

         self.assertEqual(post_processor_state["add_prefix_space"]						,   lowerCamelCase	)
         self.assertEqual(post_processor_state["trim_offsets"]						,   lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
       # `trim_offsets`
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
           _snake_case     		=				F'''{text_of_1_token} {text_of_1_token}'''

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (len(lowerCamelCase	) + 1, len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (len(lowerCamelCase	) + 1, len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (len(lowerCamelCase	), len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (len(lowerCamelCase	), len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				F''' {text}'''

           # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
           #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
           # )
           # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
           # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
           # self.assertEqual(
           #     encoding.offset_mapping[1],
           #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
           # )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (1, 1 + len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (1 + len(lowerCamelCase	) + 1, 1 + len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, 1 + len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (1 + len(lowerCamelCase	), 1 + len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )

           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(
               lowerCamelCase						,   use_fast=lowerCamelCase						,   add_prefix_space=lowerCamelCase						,   trim_offsets=lowerCamelCase	)
           _snake_case     		=				tokenizer_r(lowerCamelCase						,   return_offsets_mapping=lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
           self.assertEqual(encoding.offset_mapping[0]						,   (0, 1 + len(lowerCamelCase	))	)
           self.assertEqual(
               encoding.offset_mapping[1]						,   (1 + len(lowerCamelCase	), 1 + len(lowerCamelCase	) + 1 + len(lowerCamelCase	))						,   )


