






"""simple docstring"""






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase_     :				str         =				[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-classification""",
        """language-modeling""",
        """summarization""",
        """token-classification""",
        """question-answering""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
  import run_clm_flax
  import run_flax_glue
  import run_flax_ner
  import run_mlm_flax
  import run_qa
  import run_summarization_flax
  import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_     :				Any         =				logging.getLogger()



def       _A    ()				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	argparse.ArgumentParser()
    parser.add_argument('''-f'''							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	parser.parse_args()
    return args.f



def       _A    (__a					,       __a="eval"							)				-> List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	os.path.join(__a					,       f'{split}_results.json'							)
    if os.path.exists(__a							):
        with open(__a					,       '''r'''							) as f:
            return json.load(__a							)
    raise ValueError(f'can\'t find {path}'							)


UpperCAmelCase_     :				Optional[int]         =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	self.get_auto_remove_tmp_dir()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	F'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

          with patch.object(lowercase_		,			'''argv'''		,			lowercase_):
              run_flax_glue.main()
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	get_results(lowercase_)
              self.assertGreaterEqual(result['''eval_accuracy''']		,			0.75)
      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.get_auto_remove_tmp_dir()
          SCREAMING_SNAKE_CASE_   :						int							=	F'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

          with patch.object(lowercase_		,			'''argv'''		,			lowercase_):
              run_clm_flax.main()
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	get_results(lowercase_)
              self.assertLess(result['''eval_perplexity''']		,			100)
      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.get_auto_remove_tmp_dir()
          SCREAMING_SNAKE_CASE_   :						Tuple							=	F'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

          with patch.object(lowercase_		,			'''argv'''		,			lowercase_):
              run_summarization_flax.main()
              SCREAMING_SNAKE_CASE_   :						str							=	get_results(lowercase_		,			split='''test''')
              self.assertGreaterEqual(result['''test_rouge1''']		,			10)
              self.assertGreaterEqual(result['''test_rouge2''']		,			2)
              self.assertGreaterEqual(result['''test_rougeL''']		,			7)
              self.assertGreaterEqual(result['''test_rougeLsum''']		,			7)
      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.get_auto_remove_tmp_dir()
          SCREAMING_SNAKE_CASE_   :						str							=	F'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

          with patch.object(lowercase_		,			'''argv'''		,			lowercase_):
              run_mlm_flax.main()
              SCREAMING_SNAKE_CASE_   :						Dict							=	get_results(lowercase_)
              self.assertLess(result['''eval_perplexity''']		,			42)
      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.get_auto_remove_tmp_dir()
          SCREAMING_SNAKE_CASE_   :						Any							=	F'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

          with patch.object(lowercase_		,			'''argv'''		,			lowercase_):
              run_ta_mlm_flax.main()
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	get_results(lowercase_)
              self.assertGreaterEqual(result['''eval_accuracy''']		,			0.42)
      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	7 if get_gpu_count() > 1 else 2

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.get_auto_remove_tmp_dir()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	F'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

          with patch.object(lowercase_		,			'''argv'''		,			lowercase_):
              run_flax_ner.main()
              SCREAMING_SNAKE_CASE_   :						str							=	get_results(lowercase_)
              self.assertGreaterEqual(result['''eval_accuracy''']		,			0.75)
              self.assertGreaterEqual(result['''eval_f1''']		,			0.3)



      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.get_auto_remove_tmp_dir()
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	F'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

          with patch.object(lowercase_		,			'''argv'''		,			lowercase_):
              run_qa.main()
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	get_results(lowercase_)
              self.assertGreaterEqual(result['''eval_f1''']		,			30)
              self.assertGreaterEqual(result['''eval_exact''']		,			30)










"""simple docstring"""






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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  UpperCAmelCase_     :				Tuple         =				"""pt"""
elif is_tf_available():
  UpperCAmelCase_     :				Optional[Any]         =				"""tf"""
else:
  UpperCAmelCase_     :				str         =				"""jax"""




class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						ByTaTokenizer
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          super().setUp()
          SCREAMING_SNAKE_CASE_   :						Dict							=	ByTaTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)
      @cached_property
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          return ByTaTokenizer.from_pretrained('''google/byt5-small''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			**lowercase_  :						Tuple):




          '''simple docstring'''


          return self.tokenizer_class.from_pretrained(self.tmpdirname		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[Any]=False		,			lowercase_  :						Tuple=20		,			lowercase_  :						List[str]=5):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
          for i in range(len(lowercase_)):
              try:
                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.decode([i]		,			clean_up_tokenization_spaces=lowercase_)
              except UnicodeDecodeError:
                  pass
              toks.append((i, tok))

          SCREAMING_SNAKE_CASE_   :						Dict							=	list(filter(lambda lowercase_: re.match(r'''^[ a-zA-Z]+$'''		,			t[1])		,			lowercase_))
          SCREAMING_SNAKE_CASE_   :						Any							=	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:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	toks[:max_length]
          if min_length is not None and len(lowercase_) < min_length and len(lowercase_) > 0:
              while len(lowercase_) < min_length:
                  SCREAMING_SNAKE_CASE_   :						int							=	toks + toks
        # toks_str = [t[1] for t in toks]
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[t[0] for t in toks]

          # Ensure consistency
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer.decode(lowercase_		,			clean_up_tokenization_spaces=lowercase_)
          if " " not in output_txt and len(lowercase_) > 1:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	(
                  tokenizer.decode([toks_ids[0]]		,			clean_up_tokenization_spaces=lowercase_)
                  + ''' '''
                  + tokenizer.decode(toks_ids[1:]		,			clean_up_tokenization_spaces=lowercase_)
              )
          if with_prefix_space:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	''' ''' + output_txt
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
          return output_txt, output_ids
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''])
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(['''hi''', '''I went to the gym''', ''''''])
          self.assertListEqual(batch_with_eos_added['''input_ids''']		,			batch_without_eos_added['''input_ids'''])
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Any							=	'''Unicode €.'''
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)

          # decoding
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''Unicode €.</s>''')

          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer('''e è é ê ë''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)
          # decoding
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''e è é ê ë</s>''')

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))		,			'''e è é ê ë</s>''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Dict							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
          # fmt: on
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          self.assertIsInstance(lowercase_		,			lowercase_)

          if FRAMEWORK != "jax":
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	list(batch.input_ids.numpy()[0])
          else:
              SCREAMING_SNAKE_CASE_   :						int							=	list(batch.input_ids.tolist()[0])

          self.assertListEqual(lowercase_		,			lowercase_)

          self.assertEqual((2, 37)		,			batch.input_ids.shape)
          self.assertEqual((2, 37)		,			batch.attention_mask.shape)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          SCREAMING_SNAKE_CASE_   :						Any							=	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 _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						List[str]							=	[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer(
              text_target=lowercase_		,			max_length=32		,			padding='''max_length'''		,			truncation=lowercase_		,			return_tensors=lowercase_)
          self.assertEqual(32		,			targets['''input_ids'''].shape[1])
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	['''A long paragraph for summarization. </s>''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''Summary of the text. </s>''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
          SCREAMING_SNAKE_CASE_   :						Tuple							=	[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
          # fmt: on

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(lowercase_		,			text_target=lowercase_)

          self.assertEqual(lowercase_		,			batch['''input_ids'''][0])
          self.assertEqual(lowercase_		,			batch['''labels'''][0])
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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
          SCREAMING_SNAKE_CASE_   :						str							=	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
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	''' He is very happy, UNwant\u00E9d,running'''
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)

                  shutil.rmtree(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	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
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tempfile.mkdtemp()

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

                  SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	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)

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.__class__.from_pretrained(lowercase_		,			model_max_length=43)
                  self.assertEqual(tokenizer.model_max_length		,			43)

                  shutil.rmtree(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	[]
          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:
                      SCREAMING_SNAKE_CASE_   :						List[Any]							=	json.load(lowercase_)

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

                  SCREAMING_SNAKE_CASE_   :						str							=	[F'<extra_id_{i}>' for i in range(125)]

                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]
                  SCREAMING_SNAKE_CASE_   :						int							=	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
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer_class.from_pretrained(
                      lowercase_		,			)
                  self.assertIn(
                      '''an_additional_special_token'''		,			tokenizer_without_change_in_init.additional_special_tokens)
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  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
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''		,			lstrip=lowercase_)]
                  SCREAMING_SNAKE_CASE_   :						int							=	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 _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	[]
          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_)

                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer_class.from_pretrained(lowercase_)

                  self.assertTrue(tokenizer.decode([255]) == '''''')
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.get_tokenizers(fast=lowercase_		,			do_lower_case=lowercase_)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						Any							=	['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.convert_tokens_to_string(lowercase_)

                  self.assertIsInstance(lowercase_		,			lowercase_)



      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						int							=	[
                      '''bos_token''',
                      '''eos_token''',
                      '''unk_token''',
                      '''sep_token''',
                      '''pad_token''',
                      '''cls_token''',
                      '''mask_token''',
                  ]

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	0
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.convert_ids_to_tokens(
                      lowercase_		,			skip_special_tokens=lowercase_)

                  for attr in attributes_list:
                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[])

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[token_id_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[token_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[token_id_to_test_setters])


