




import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

_lowerCamelCase			:	Any							 =     logging.getLogger(__name__)


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   field(
        default=128	,      metadata={
            '''help''': (
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''Whether to pad all samples to `max_seq_length`. '''
                '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                '''value if set.'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                '''value if set.'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
                '''value if set.'''
            )
        }	,      )


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Train language if it is different from the evaluation language.'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}	,      )
    UpperCAmelCase__													=   field(
        default='''main'''	,      metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={
            '''help''': (
                '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                '''with private models).'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCAmelCase__	,      metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}	,      )







def SCREAMING_SNAKE_CASE       ( )     -> str:



       """simple docstring"""

       A__   				=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
       A__				,							A__				,							A__   				=					parser.parse_args_into_dataclasses()

       # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
       # information sent is the one passed as arguments along with your Python/PyTorch versions.
       send_example_telemetry('''run_xnli'''				,       lowercase_		)

       # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,       datefmt='''%m/%d/%Y %H:%M:%S'''				,       handlers=[logging.StreamHandler(sys.stdout		)]				,       )

       if training_args.should_log:
              # The default of training_args.log_level is passive, so we set log level at info here to have that default.
              transformers.utils.logging.set_verbosity_info()

       A__   				=					training_args.get_process_log_level()
       logger.setLevel(lowercase_		)
       datasets.utils.logging.set_verbosity(lowercase_		)
       transformers.utils.logging.set_verbosity(lowercase_		)
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()

       # Log on each process the small summary:
       logger.warning(
           f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + f"""distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}"""		)
       logger.info(f"""Training/evaluation parameters {training_args}"""		)

       # Detecting last checkpoint.
       A__   				=					None
       if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
              A__   				=					get_last_checkpoint(training_args.output_dir		)
              if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
                     raise ValueError(
                         f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         '''Use --overwrite_output_dir to overcome.'''		)
              elif last_checkpoint is not None:
                     logger.info(
                         f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''		)

    # Set seed before initializing model.
       set_seed(training_args.seed		)

       # In distributed training, the load_dataset function guarantees that only one local process can concurrently
       # download the dataset.
       # Downloading and loading xnli dataset from the hub.
       if training_args.do_train:
              if model_args.train_language is None:
                     A__   				=					load_dataset(
                         '''xnli'''				,       model_args.language				,       split='''train'''				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )
              else:
                     A__   				=					load_dataset(
                         '''xnli'''				,       model_args.train_language				,       split='''train'''				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )
              A__   				=					train_dataset.features['''label'''].names

       if training_args.do_eval:
              A__   				=					load_dataset(
                  '''xnli'''				,       model_args.language				,       split='''validation'''				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )
              A__   				=					eval_dataset.features['''label'''].names

       if training_args.do_predict:
              A__   				=					load_dataset(
                  '''xnli'''				,       model_args.language				,       split='''test'''				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )
              A__   				=					predict_dataset.features['''label'''].names

       # Labels
       A__   				=					len(lowercase_		)

       # Load pretrained model and tokenizer
       # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       A__   				=					AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,       num_labels=lowercase_				,       idalabel={str(lowercase_		): label for i, label in enumerate(lowercase_		)}				,       labelaid={label: i for i, label in enumerate(lowercase_		)}				,       finetuning_task='''xnli'''				,       cache_dir=model_args.cache_dir				,       revision=model_args.model_revision				,       use_auth_token=True if model_args.use_auth_token else None				,       )
       A__   				=					AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,       do_lower_case=model_args.do_lower_case				,       cache_dir=model_args.cache_dir				,       use_fast=model_args.use_fast_tokenizer				,       revision=model_args.model_revision				,       use_auth_token=True if model_args.use_auth_token else None				,       )
       A__   				=					AutoModelForSequenceClassification.from_pretrained(
           model_args.model_name_or_path				,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)				,       config=lowercase_				,       cache_dir=model_args.cache_dir				,       revision=model_args.model_revision				,       use_auth_token=True if model_args.use_auth_token else None				,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes				,       )

       # Preprocessing the datasets
       # Padding strategy
       if data_args.pad_to_max_length:
              A__   				=					'''max_length'''
       else:
              # We will pad later, dynamically at batch creation, to the max sequence length in each batch
              A__   				=					False

       def preprocess_function(lowercase_		):
              # Tokenize the texts
              return tokenizer(
                  examples['''premise''']				,       examples['''hypothesis''']				,       padding=lowercase_				,       max_length=data_args.max_seq_length				,       truncation=lowercase_				,       )

       if training_args.do_train:
              if data_args.max_train_samples is not None:
                     A__   				=					min(len(lowercase_		)				,       data_args.max_train_samples		)
                     A__   				=					train_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''train dataset map pre-processing'''		):
                     A__   				=					train_dataset.map(
                         lowercase_				,       batched=lowercase_				,       load_from_cache_file=not data_args.overwrite_cache				,       desc='''Running tokenizer on train dataset'''				,       )
              # Log a few random samples from the training set:
              for index in random.sample(range(len(lowercase_		)		)				,       3		):
                     logger.info(f"""Sample {index} of the training set: {train_dataset[index]}."""		)

       if training_args.do_eval:
              if data_args.max_eval_samples is not None:
                     A__   				=					min(len(lowercase_		)				,       data_args.max_eval_samples		)
                     A__   				=					eval_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''validation dataset map pre-processing'''		):
                     A__   				=					eval_dataset.map(
                         lowercase_				,       batched=lowercase_				,       load_from_cache_file=not data_args.overwrite_cache				,       desc='''Running tokenizer on validation dataset'''				,       )

       if training_args.do_predict:
              if data_args.max_predict_samples is not None:
                     A__   				=					min(len(lowercase_		)				,       data_args.max_predict_samples		)
                     A__   				=					predict_dataset.select(range(lowercase_		)		)
              with training_args.main_process_first(desc='''prediction dataset map pre-processing'''		):
                     A__   				=					predict_dataset.map(
                         lowercase_				,       batched=lowercase_				,       load_from_cache_file=not data_args.overwrite_cache				,       desc='''Running tokenizer on prediction dataset'''				,       )

    # Get the metric function
       A__   				=					evaluate.load('''xnli'''		)

       # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
       # predictions and label_ids field) and has to return a dictionary string to float.
       def compute_metrics(lowercase_		):
              A__   				=					p.predictions[0] if isinstance(p.predictions				,       lowercase_		) else p.predictions
              A__   				=					np.argmax(lowercase_				,       axis=1		)
              return metric.compute(predictions=lowercase_				,       references=p.label_ids		)

       # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
       if data_args.pad_to_max_length:
              A__   				=					default_data_collator
       elif training_args.fpaa:
              A__   				=					DataCollatorWithPadding(lowercase_				,       pad_to_multiple_of=8		)
       else:
              A__   				=					None

       # Initialize our Trainer
       A__   				=					Trainer(
           model=lowercase_				,       args=lowercase_				,       train_dataset=train_dataset if training_args.do_train else None				,       eval_dataset=eval_dataset if training_args.do_eval else None				,       compute_metrics=lowercase_				,       tokenizer=lowercase_				,       data_collator=lowercase_				,       )

       # Training
       if training_args.do_train:
              A__   				=					None
              if training_args.resume_from_checkpoint is not None:
                     A__   				=					training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     A__   				=					last_checkpoint
              A__   				=					trainer.train(resume_from_checkpoint=lowercase_		)
              A__   				=					train_result.metrics
              A__   				=					(
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_		)
              )
              A__   				=					min(lowercase_				,       len(lowercase_		)		)

              trainer.save_model()  # Saves the tokenizer too for easy upload

              trainer.log_metrics('''train'''				,       lowercase_		)
              trainer.save_metrics('''train'''				,       lowercase_		)
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              logger.info('''*** Evaluate ***'''		)
              A__   				=					trainer.evaluate(eval_dataset=lowercase_		)

              A__   				=					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase_		)
              A__   				=					min(lowercase_				,       len(lowercase_		)		)

              trainer.log_metrics('''eval'''				,       lowercase_		)
              trainer.save_metrics('''eval'''				,       lowercase_		)

       # Prediction
       if training_args.do_predict:
              logger.info('''*** Predict ***'''		)
              A__				,							A__				,							A__   				=					trainer.predict(lowercase_				,       metric_key_prefix='''predict'''		)

              A__   				=					(
                  data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowercase_		)
              )
              A__   				=					min(lowercase_				,       len(lowercase_		)		)

              trainer.log_metrics('''predict'''				,       lowercase_		)
              trainer.save_metrics('''predict'''				,       lowercase_		)

              A__   				=					np.argmax(lowercase_				,       axis=1		)
              A__   				=					os.path.join(training_args.output_dir				,       '''predictions.txt'''		)
              if trainer.is_world_process_zero():
                     with open(lowercase_				,       '''w'''		) as writer:
                            writer.write('''index\tprediction\n'''		)
                            for index, item in enumerate(lowercase_		):
                                   A__   				=					label_list[item]
                                   writer.write(f"""{index}\t{item}\n"""		)


if __name__ == "__main__":
  main()








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():
  _lowerCamelCase			:	List[Any]							 =     """pt"""
elif is_tf_available():
  _lowerCamelCase			:	int							 =     """tf"""
else:
  _lowerCamelCase			:	Dict							 =     """jax"""


class        UpperCamelCase_					(				UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   PerceiverTokenizer
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->int:





           '''simple docstring'''

           super().setUp()
           A__   				=					PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname)


    @cached_property
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Any:





           '''simple docstring'''

           return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''')


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				**UpperCAmelCase__   :			List[str])		->PerceiverTokenizer:





           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[int]=False      ,				UpperCAmelCase__   :			Union[str, Any]=20      ,				UpperCAmelCase__   :			Dict=5)		->Tuple[str, list]:





           '''simple docstring'''

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

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

           # Ensure consistency
           A__   				=					tokenizer.decode(UpperCAmelCase__      ,				clean_up_tokenization_spaces=UpperCAmelCase__)
           if " " not in output_txt and len(UpperCAmelCase__) > 1:
                  A__   				=					(
                      tokenizer.decode([toks_ids[0]]      ,				clean_up_tokenization_spaces=UpperCAmelCase__)
                      + ''' '''
                      + tokenizer.decode(toks_ids[1:]      ,				clean_up_tokenization_spaces=UpperCAmelCase__)
                  )
           if with_prefix_space:
                  A__   				=					''' ''' + output_txt
           A__   				=					tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
           return output_txt, output_ids


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->str:





           '''simple docstring'''

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

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

           A__   				=					tokenizer('''e è é ê ë''')
           A__   				=					[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded['''input_ids''']      ,				UpperCAmelCase__)
           # decoding
           A__   				=					tokenizer.decode(UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__      ,				'''[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  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Dict:





           '''simple docstring'''

           A__   				=					self.perceiver_tokenizer
           A__   				=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           # fmt: off
           A__   				=					[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
           A__   				=					tokenizer(UpperCAmelCase__      ,				padding=UpperCAmelCase__      ,				return_tensors=UpperCAmelCase__)
           self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)

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

           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

           self.assertEqual((2, 38)      ,				batch.input_ids.shape)
           self.assertEqual((2, 38)      ,				batch.attention_mask.shape)


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Any:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Tuple:





           '''simple docstring'''

           A__   				=					self.perceiver_tokenizer
           A__   				=					[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           A__   				=					tokenizer(
               text_target=UpperCAmelCase__      ,				max_length=32      ,				padding='''max_length'''      ,				truncation=UpperCAmelCase__      ,				return_tensors=UpperCAmelCase__)
           self.assertEqual(32      ,				targets['''input_ids'''].shape[1])


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Tuple:





           '''simple docstring'''

           A__   				=					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
           A__   				=					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
                         A__   				=					tempfile.mkdtemp()

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

                         A__   				=					tokenizer.__class__.from_pretrained(UpperCAmelCase__)
                         A__   				=					after_tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
                         self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

                         shutil.rmtree(UpperCAmelCase__)

           A__   				=					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
                         A__   				=					tempfile.mkdtemp()

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

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

                         A__   				=					tokenizer.__class__.from_pretrained(UpperCAmelCase__      ,				model_max_length=43)
                         self.assertEqual(tokenizer.model_max_length      ,				43)

                         shutil.rmtree(UpperCAmelCase__)


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





           '''simple docstring'''

           A__   				=					[]
           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(UpperCAmelCase__)

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

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

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

                         A__   				=					added_tokens_extra_ids + [
                             '''an_additional_special_token'''
                         ]
                         A__   				=					added_tokens_extra_ids + [
                             '''an_additional_special_token'''
                         ]

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

                         # 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
                         A__   				=					tokenizer_class.from_pretrained(
                             UpperCAmelCase__      ,				)
                         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
                         A__   				=					added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''      ,				lstrip=UpperCAmelCase__)]
                         A__   				=					tokenizer_class.from_pretrained(
                             UpperCAmelCase__      ,				additional_special_tokens=UpperCAmelCase__      ,				)

                         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  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178])      ,				'''�''')


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''simple docstring'''

           pass


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





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[Any]:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->str:





           '''simple docstring'''

           pass





    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Tuple:





           '''simple docstring'''

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

                         self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)


