


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


A      :    Optional[int]		=					logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

A      :    Tuple		=					{
    "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
A      :    List[str]		=					{
    "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
}
A      :    Optional[int]		=					sorted(arg_to_scheduler.keys())
A      :    Optional[Any]		=					"{" + ", ".join(arg_to_scheduler_choices) + "}"







class 			_lowercase (							pl.LightningModule):


       """simple docstring"""




       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			argparse.Namespace					,      __lowerCamelCase							:			List[Any]=None					,      __lowerCamelCase							:			Dict="base"					,      __lowerCamelCase							:			Optional[int]=None					,      __lowerCamelCase							:			Dict=None					,      __lowerCamelCase							:			Tuple=None					,      **__lowerCamelCase							:			Optional[int]					,      ):


             '''simple docstring'''



             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       )
             lowerCamelCase__		:				Dict    =  0
             lowerCamelCase__		:				Optional[int]    =  Path(self.hparams.output_dir       )
             lowerCamelCase__		:				Any    =  self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                   lowerCamelCase__		:				Tuple    =  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:
                   lowerCamelCase__		:				str    =  config

             lowerCamelCase__		:				Dict    =  ("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:
                   lowerCamelCase__		:				Optional[Any]    =  AutoTokenizer.from_pretrained(
                       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,      cache_dir=__lowerCamelCase					,      )
             else:
                   lowerCamelCase__		:				Tuple    =  tokenizer
             lowerCamelCase__		:				Dict    =  MODEL_MODES[mode]
             if model is None:
                   lowerCamelCase__		:				List[Any]    =  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:
                   lowerCamelCase__		:				Any    =  model





       def 						lowerCAmelCase     (   self							:			List[Any]					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.model_type.from_pretrained(*__lowerCamelCase					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  arg_to_scheduler[self.hparams.lr_scheduler]
             lowerCamelCase__		:				Tuple    =  get_schedule_func(
                 self.opt					,      num_warmup_steps=self.hparams.warmup_steps					,      num_training_steps=self.total_steps()       )
             lowerCamelCase__		:				Union[str, Any]    =  {"scheduler": scheduler, "interval": "step", "frequency": 1}
             return scheduler





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.model
             lowerCamelCase__		:				Optional[int]    =  ["bias", "LayerNorm.weight"]
             lowerCamelCase__		:				str    =  [
                 {
                     "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:
                   lowerCamelCase__		:				Tuple    =  Adafactor(
                       __lowerCamelCase					,      lr=self.hparams.learning_rate					,      scale_parameter=__lowerCamelCase					,      relative_step=__lowerCamelCase       )

             else:
                   lowerCamelCase__		:				Optional[Any]    =  AdamW(
                       __lowerCamelCase					,      lr=self.hparams.learning_rate					,      eps=self.hparams.adam_epsilon       )
             lowerCamelCase__		:				Optional[int]    =  optimizer

             lowerCamelCase__		:				Union[str, Any]    =  self.get_lr_scheduler()

             return [optimizer], [scheduler]





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             return self.validation_step(__lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			Tuple       ):


             '''simple docstring'''



             return self.validation_end(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             if stage == "test":
                   lowerCamelCase__		:				Tuple    =  len(self.test_dataloader().dataset       )
             else:
                   lowerCamelCase__		:				str    =  self.get_dataloader("train"					,      self.hparams.train_batch_size					,      shuffle=__lowerCamelCase       )
                   lowerCamelCase__		:				Optional[Any]    =  len(self.train_dataloader().dataset       )





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			str					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			bool = False       ):


             '''simple docstring'''



             raise NotImplementedError("You must implement this for your task"       )





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


             '''simple docstring'''



             return self.train_loader





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             return self.get_dataloader("dev"					,      self.hparams.eval_batch_size					,      shuffle=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             return self.get_dataloader("test"					,      self.hparams.eval_batch_size					,      shuffle=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             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 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			Dict[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  self.output_dir.joinpath("best_tfmr"       )
             lowerCamelCase__		:				Any    =  self.step_count
             self.model.save_pretrained(__lowerCamelCase       )
             self.tokenizer.save_pretrained(__lowerCamelCase       )





       @staticmethod
       def 						lowerCAmelCase     (   __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             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 			_lowercase (							pl.Callback):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             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 			_lowercase (							pl.Callback):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             for name, param in pl_module.model.rag.named_parameters():
                   if param.grad is None:
                         print(__lowerCamelCase       )







class 			_lowercase (							pl.Callback):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  trainer.lr_schedulers[0]["scheduler"]
             lowerCamelCase__		:				Optional[Any]    =  {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
             pl_module.logger.log_metrics(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			pl.Trainer					,      __lowerCamelCase							:			pl.LightningModule       ):


             '''simple docstring'''



             rank_zero_info("***** Validation results *****"       )
             lowerCamelCase__		:				Tuple    =  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 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			pl.Trainer					,      __lowerCamelCase							:			pl.LightningModule       ):


             '''simple docstring'''



             rank_zero_info("***** Test results *****"       )
             lowerCamelCase__		:				Tuple    =  trainer.callback_metrics
             # Log and save results to file
             lowerCamelCase__		:				Dict    =  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       lowercase_		(		_A			:    int    ,					_A			:    Optional[Any]						):

      """simple docstring"""




      parser.add_argument(
          "--output_dir"    ,					default=str(Path(_a						).parent / "test_run" / "model_checkpoints"						)    ,					type=_a    ,					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=_a    ,					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=_a						)
      parser.add_argument("--max_grad_norm"    ,					dest="gradient_clip_val"    ,					default=1.0    ,					type=_a    ,					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=_a    ,					default=1    ,					help="Number of updates steps to accumulate before performing a backward/update pass."    ,					)
      parser.add_argument("--seed"    ,					type=_a    ,					default=42    ,					help="random seed for initialization"						)
      parser.add_argument(
          "--data_dir"    ,					default=str(Path(_a						).parent / "test_run" / "dummy-train-data"						)    ,					type=_a    ,					help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."    ,					)


def       lowercase_		(		_A			:    BaseTransformer    ,					_A			:    argparse.Namespace    ,					_A			:    List[Any]=None    ,					_A			:    Tuple=True    ,					_A			:    int=[]    ,					_A			:    Any=None    ,					_A			:    int=None    ,					**_A			:    Optional[Any]    ,					):

      """simple docstring"""




      pl.seed_everything(args.seed						)

      # init model
      lowerCamelCase__		:				Any    =  Path(model.hparams.output_dir						)
      odir.mkdir(exist_ok=_a						)

      # add custom checkpoints
      if checkpoint_callback is None:
            lowerCamelCase__		:				Dict    =  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(_a						)
      if logging_callback is None:
            lowerCamelCase__		:				int    =  LoggingCallback()

      lowerCamelCase__		:				List[Any]    =  {}

      if args.fpaa:
            lowerCamelCase__		:				Union[str, Any]    =  16

      if args.gpus > 1:
            lowerCamelCase__		:				Tuple    =  "auto"
            lowerCamelCase__		:				List[str]    =  "ddp"

      lowerCamelCase__		:				Any    =  args.accumulate_grad_batches
      lowerCamelCase__		:				Any    =  None
      lowerCamelCase__		:				List[str]    =  "auto"

      lowerCamelCase__		:				Any    =  pl.Trainer.from_argparse_args(
          _a    ,					weights_summary=_a    ,					callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]    ,					logger=_a    ,					val_check_interval=1    ,					num_sanity_val_steps=2    ,					**_a    ,					)

      if args.do_train:
            trainer.fit(_a						)

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








'''simple docstring'''







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
   A     ='pt'
elif is_tf_available():
   A     ='tf'
else:
   A     ='jax'


class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     Optional[Any]   				=					PerceiverTokenizer
       __a				:     str   				=					False
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase			       =		PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname				)
       @cached_property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''				)
       def     A      (	self						:	Union[str, Any]   ,				**lowercase						:	int				):



           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	str   ,				lowercase						:	List[str]=False   ,				lowercase						:	Union[str, Any]=20   ,				lowercase						:	Union[str, Any]=5				):



           '''simple docstring'''

           UpperCAmelCase			       =		[]
           for i in range(len(lowercase				)				):
               try:
                   UpperCAmelCase			       =		tokenizer.decode([i]   ,				clean_up_tokenization_spaces=lowercase				)
               except UnicodeDecodeError:
                   pass
               toks.append((i, tok)				)

           UpperCAmelCase			       =		list(filter(lambda lowercase				: re.match(R'''^[ a-zA-Z]+$'''   ,				t[1]				)   ,				lowercase				)				)
           UpperCAmelCase			       =		list(filter(lambda lowercase				: [t[0]] == tokenizer.encode(t[1]   ,				add_special_tokens=lowercase				)   ,				lowercase				)				)
           if max_length is not None and len(lowercase				) > max_length:
               UpperCAmelCase			       =		toks[:max_length]
           if min_length is not None and len(lowercase				) < min_length and len(lowercase				) > 0:
               while len(lowercase				) < min_length:
                   UpperCAmelCase			       =		toks + toks
        # toks_str = [t[1] for t in toks]
           UpperCAmelCase			       =		[t[0] for t in toks]

           # Ensure consistency
           UpperCAmelCase			       =		tokenizer.decode(lowercase   ,				clean_up_tokenization_spaces=lowercase				)
           if " " not in output_txt and len(lowercase				) > 1:
               UpperCAmelCase			       =		(
                   tokenizer.decode([toks_ids[0]]   ,				clean_up_tokenization_spaces=lowercase				)
                   + ''' '''
                   + tokenizer.decode(toks_ids[1:]   ,				clean_up_tokenization_spaces=lowercase				)
               )
           if with_prefix_space:
               UpperCAmelCase			       =		''' ''' + output_txt
           UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
           return output_txt, output_ids
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		'''Unicode €.'''
           UpperCAmelCase			       =		tokenizer(lowercase				)
           UpperCAmelCase			       =		[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)

           # decoding
           UpperCAmelCase			       =		tokenizer.decode(lowercase				)
           self.assertEqual(lowercase   ,				'''[CLS]Unicode €.[SEP]'''				)

           UpperCAmelCase			       =		tokenizer('''e è é ê ë'''				)
           UpperCAmelCase			       =		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)
           # decoding
           UpperCAmelCase			       =		tokenizer.decode(lowercase				)
           self.assertEqual(lowercase   ,				'''[CLS]e è é ê ë[SEP]'''				)

           # encode/decode, but with `encode` instead of `__call__`
           self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''				)				)   ,				'''[CLS]e è é ê ë[SEP]'''				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           # fmt: off
           UpperCAmelCase			       =		[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
           # fmt: on
           UpperCAmelCase			       =		tokenizer(lowercase   ,				padding=lowercase   ,				return_tensors=lowercase				)
           self.assertIsInstance(lowercase   ,				lowercase				)

           if FRAMEWORK != "jax":
               UpperCAmelCase			       =		list(batch.input_ids.numpy()[0]				)
           else:
               UpperCAmelCase			       =		list(batch.input_ids.tolist()[0]				)

           self.assertListEqual(lowercase   ,				lowercase				)

           self.assertEqual((2, 38)   ,				batch.input_ids.shape				)
           self.assertEqual((2, 38)   ,				batch.attention_mask.shape				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           UpperCAmelCase			       =		tokenizer(lowercase   ,				padding=lowercase   ,				return_tensors=lowercase				)
           # check if input_ids are returned and no decoder_input_ids
           self.assertIn('''input_ids'''   ,				lowercase				)
           self.assertIn('''attention_mask'''   ,				lowercase				)
           self.assertNotIn('''decoder_input_ids'''   ,				lowercase				)
           self.assertNotIn('''decoder_attention_mask'''   ,				lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           UpperCAmelCase			       =		tokenizer(
               text_target=lowercase   ,				max_length=32   ,				padding='''max_length'''   ,				truncation=lowercase   ,				return_tensors=lowercase				)
           self.assertEqual(32   ,				targets['''input_ids'''].shape[1]				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   self.assertNotEqual(tokenizer.model_max_length   ,				42				)

        # Now let's start the test
           UpperCAmelCase			       =		self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCAmelCase			       =		tempfile.mkdtemp()

                   UpperCAmelCase			       =		''' He is very happy, UNwant\u00E9d,running'''
                   UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   tokenizer.save_pretrained(lowercase				)

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase				)
                   UpperCAmelCase			       =		after_tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   self.assertListEqual(lowercase   ,				lowercase				)

                   shutil.rmtree(lowercase				)

           UpperCAmelCase			       =		self.get_tokenizers(model_max_length=42				)
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCAmelCase			       =		tempfile.mkdtemp()

                   UpperCAmelCase			       =		''' He is very happy, UNwant\u00E9d,running'''
                   tokenizer.add_tokens(['''bim''', '''bambam''']				)
                   UpperCAmelCase			       =		tokenizer.additional_special_tokens
                   additional_special_tokens.append('''new_additional_special_token'''				)
                   tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}				)
                   UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   tokenizer.save_pretrained(lowercase				)

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase				)
                   UpperCAmelCase			       =		after_tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   self.assertListEqual(lowercase   ,				lowercase				)
                   self.assertIn('''new_additional_special_token'''   ,				after_tokenizer.additional_special_tokens				)
                   self.assertEqual(after_tokenizer.model_max_length   ,				42				)

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase   ,				model_max_length=43				)
                   self.assertEqual(tokenizer.model_max_length   ,				43				)

                   shutil.rmtree(lowercase				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		[]
           if self.test_slow_tokenizer:
               tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())				)

           if self.test_rust_tokenizer:
               tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())				)

           for tokenizer_class, tokenizer_utils in tokenizer_list:
               with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(lowercase				)

                   with open(os.path.join(lowercase   ,				'''special_tokens_map.json'''				)   ,				encoding='''utf-8'''				) as json_file:
                       UpperCAmelCase			       =		json.load(lowercase				)

                   with open(os.path.join(lowercase   ,				'''tokenizer_config.json'''				)   ,				encoding='''utf-8'''				) as json_file:
                       UpperCAmelCase			       =		json.load(lowercase				)

                   UpperCAmelCase			       =		[f"<extra_id_{i}>" for i in range(125				)]

                   UpperCAmelCase			       =		added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]
                   UpperCAmelCase			       =		added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]

                   with open(os.path.join(lowercase   ,				'''special_tokens_map.json'''				)   ,				'''w'''   ,				encoding='''utf-8'''				) as outfile:
                       json.dump(lowercase   ,				lowercase				)
                   with open(os.path.join(lowercase   ,				'''tokenizer_config.json'''				)   ,				'''w'''   ,				encoding='''utf-8'''				) as outfile:
                       json.dump(lowercase   ,				lowercase				)

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				)
                   self.assertIn(
                       '''an_additional_special_token'''   ,				tokenizer_without_change_in_init.additional_special_tokens				)
                   self.assertEqual(
                       ['''an_additional_special_token''']   ,				tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']				)				)   ,				)

                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   UpperCAmelCase			       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''   ,				lstrip=lowercase				)]
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				additional_special_tokens=lowercase   ,				)

                   self.assertIn('''a_new_additional_special_token'''   ,				tokenizer.additional_special_tokens				)
                   self.assertEqual(
                       ['''a_new_additional_special_token''']   ,				tokenizer.convert_ids_to_tokens(
                           tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']				)				)   ,				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178]				)   ,				'''�'''				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Any				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           pass
       def     A      (	self						:	str				):



           '''simple docstring'''

           pass







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizers(fast=lowercase   ,				do_lower_case=lowercase				)
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   UpperCAmelCase			       =		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                   UpperCAmelCase			       =		tokenizer.convert_tokens_to_string(lowercase				)

                   self.assertIsInstance(lowercase   ,				lowercase				)




