import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_="attention"   )		->						Tuple:




       '''simple docstring'''






       _A=	params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
       _A=	params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
       _A=	params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
       _A=	params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
       return k, o, q, v

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=False   )		->						Tuple:




       '''simple docstring'''






       if split_mlp_wi:
              _A=	params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
              _A=	params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
              _A=	(wi_a, wi_a)
       else:
              _A=	params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

       _A=	params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
       return wi, wo

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						str:




       '''simple docstring'''






       return params[F"{prefix}/layers_{i}/{layer_name}/scale"]

def 							UpperCamelCase				(    lowerCAmelCase_       ,					*, lowerCAmelCase_       ,					lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	traverse_util.flatten_dict(variables['target']   )
       _A=	{'/'.join(lowerCAmelCase_   ): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       _A=	'encoder/layers_0/mlp/wi_0/kernel' in old
       print('Split MLP:'       ,					lowerCAmelCase_   )

       _A=	collections.OrderedDict()

       # Shared embeddings.
       _A=	old['token_embedder/embedding']

       # Encoder.
       for i in range(lowerCAmelCase_   ):
              # Block i, layer 0 (Self Attention).
              _A=	tax_layer_norm_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'encoder'       ,					'pre_attention_layer_norm'   )
              _A,			_A,			_A,			_A=	tax_attention_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'encoder'       ,					'attention'   )
              _A=	layer_norm
              _A=	k.T
              _A=	o.T
              _A=	q.T
              _A=	v.T

              # Block i, layer 1 (MLP).
              _A=	tax_layer_norm_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'encoder'       ,					'pre_mlp_layer_norm'   )
              _A,			_A=	tax_mlp_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'encoder'       ,					lowerCAmelCase_   )
              _A=	layer_norm
              if split_mlp_wi:
                     _A=	wi[0].T
                     _A=	wi[1].T
              else:
                     _A=	wi.T
              _A=	wo.T

       _A=	old[
           'encoder/relpos_bias/rel_embedding'
       ].T
       _A=	old['encoder/encoder_norm/scale']

       if not is_encoder_only:
              # Decoder.
              for i in range(lowerCAmelCase_   ):
                     # Block i, layer 0 (Self Attention).
                     _A=	tax_layer_norm_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'decoder'       ,					'pre_self_attention_layer_norm'   )
                     _A,			_A,			_A,			_A=	tax_attention_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'decoder'       ,					'self_attention'   )
                     _A=	layer_norm
                     _A=	k.T
                     _A=	o.T
                     _A=	q.T
                     _A=	v.T

                     # Block i, layer 1 (Cross Attention).
                     _A=	tax_layer_norm_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'decoder'       ,					'pre_cross_attention_layer_norm'   )
                     _A,			_A,			_A,			_A=	tax_attention_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'decoder'       ,					'encoder_decoder_attention'   )
                     _A=	layer_norm
                     _A=	k.T
                     _A=	o.T
                     _A=	q.T
                     _A=	v.T

                     # Block i, layer 2 (MLP).
                     _A=	tax_layer_norm_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'decoder'       ,					'pre_mlp_layer_norm'   )
                     _A,			_A=	tax_mlp_lookup(lowerCAmelCase_       ,					lowerCAmelCase_       ,					'decoder'       ,					lowerCAmelCase_   )
                     _A=	layer_norm
                     if split_mlp_wi:
                            _A=	wi[0].T
                            _A=	wi[1].T
                     else:
                            _A=	wi.T
                     _A=	wo.T

              _A=	old['decoder/decoder_norm/scale']
              _A=	old[
                  'decoder/relpos_bias/rel_embedding'
              ].T

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     _A=	old['decoder/logits_dense/kernel'].T

       return new

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       _A=	collections.OrderedDict([(k, torch.from_numpy(v.copy()   )) for (k, v) in converted_params.items()]   )

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              _A=	state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     _A=	state_dict['shared.weight']

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print('Using shared word embeddings as lm_head.'   )
                     _A=	state_dict['shared.weight']

       return state_dict

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






       _A=	checkpoints.load_tax_checkpoint(lowerCAmelCase_   )
       _A=	convert_tax_to_pytorch(lowerCAmelCase_       ,					num_layers=config.num_layers       ,					is_encoder_only=lowerCAmelCase_   )
       _A=	make_state_dict(lowerCAmelCase_       ,					lowerCAmelCase_   )
       model.load_state_dict(lowerCAmelCase_       ,					strict=lowerCAmelCase_   )

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_ = False   )		->						List[str]:




       '''simple docstring'''






       _A=	TaConfig.from_json_file(lowerCAmelCase_   )
       print(F"Building PyTorch model from configuration: {config}"   )
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              _A=	TaEncoderModel(lowerCAmelCase_   )
       else:
              _A=	TaForConditionalGeneration(lowerCAmelCase_   )

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )

       # Save pytorch-model
       print(F"Save PyTorch model to {pytorch_dump_path}"   )
       model.save_pretrained(lowerCAmelCase_   )

       # Verify that we can load the checkpoint.
       model.from_pretrained(lowerCAmelCase_   )
       print('Done'   )


if __name__ == "__main__":
      UpperCAmelCase_								=							argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
      # Required parameters
      parser.add_argument(
          '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
      )
      UpperCAmelCase_								=							parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )
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_								=							'''pt'''
elif is_tf_available():
      UpperCAmelCase_								=							'''tf'''
else:
      UpperCAmelCase_								=							'''jax'''





class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=ByTaTokenizer
     _SCREAMING_SNAKE_CASE     :     Dict		=False



     def 			a__  (					self       ):
            super().setUp()
            _A=	ByTaTokenizer()
            tokenizer.save_pretrained(self.tmpdirname       )



     @cached_property
     def 			a__  (					self       ):
            return ByTaTokenizer.from_pretrained('google/byt5-small'       )



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            return self.tokenizer_class.from_pretrained(self.tmpdirname				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=False				,      lowerCAmelCase__=20				,      lowerCAmelCase__=5       ):
            # XXX The default common tokenizer tests assume that every ID is decodable on its own.
            # This assumption is invalid for ByT5 because single bytes might not be
            # valid utf-8 (byte 128 for instance).
            # Here we're overriding the smallest possible method to provide
            # a clean sequence without making the same assumption.

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

            _A=	list(filter(lambda lowerCAmelCase__       : re.match(r'^[ a-zA-Z]+$'				,      t[1]       )				,      lowerCAmelCase__       )       )
            _A=	list(filter(lambda lowerCAmelCase__       : [t[0]] == tokenizer.encode(t[1]				,      add_special_tokens=lowerCAmelCase__       )				,      lowerCAmelCase__       )       )
            if max_length is not None and len(lowerCAmelCase__       ) > max_length:
                   _A=	toks[:max_length]
            if min_length is not None and len(lowerCAmelCase__       ) < min_length and len(lowerCAmelCase__       ) > 0:
                   while len(lowerCAmelCase__       ) < 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(lowerCAmelCase__				,      clean_up_tokenization_spaces=lowerCAmelCase__       )
            if " " not in output_txt and len(lowerCAmelCase__       ) > 1:
                   _A=	(
                       tokenizer.decode([toks_ids[0]]				,      clean_up_tokenization_spaces=lowerCAmelCase__       )
                       + ' '
                       + tokenizer.decode(toks_ids[1:]				,      clean_up_tokenization_spaces=lowerCAmelCase__       )
                   )
            if with_prefix_space:
                   _A=	' ' + output_txt
            _A=	tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
            return output_txt, output_ids



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']       )
            _A=	tokenizer(['hi', 'I went to the gym', '']       )
            self.assertListEqual(batch_with_eos_added['input_ids']				,      batch_without_eos_added['input_ids']       )



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	'Unicode €.'
            _A=	tokenizer(lowerCAmelCase__       )
            _A=	[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
            self.assertEqual(encoded['input_ids']				,      lowerCAmelCase__       )

            # decoding
            _A=	tokenizer.decode(lowerCAmelCase__       )
            self.assertEqual(lowerCAmelCase__				,      'Unicode €.</s>'       )

            _A=	tokenizer('e è é ê ë'       )
            _A=	[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
            self.assertEqual(encoded['input_ids']				,      lowerCAmelCase__       )
            # decoding
            _A=	tokenizer.decode(lowerCAmelCase__       )
            self.assertEqual(lowerCAmelCase__				,      '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 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
            # fmt: off
            _A=	[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
            _A=	tokenizer(lowerCAmelCase__				,      padding=lowerCAmelCase__				,      return_tensors=lowerCAmelCase__       )
            self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

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

            self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

            self.assertEqual((2, 37)				,      batch.input_ids.shape       )
            self.assertEqual((2, 37)				,      batch.attention_mask.shape       )



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



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	[
                'Summary of the text.',
                'Another summary.',
            ]
            _A=	tokenizer(
                text_target=lowerCAmelCase__				,      max_length=32				,      padding='max_length'				,      truncation=lowerCAmelCase__				,      return_tensors=lowerCAmelCase__       )
            self.assertEqual(32				,      targets['input_ids'].shape[1]       )



     def 			a__  (					self       ):
            _A=	self.ta_base_tokenizer
            _A=	['A long paragraph for summarization. </s>']
            _A=	['Summary of the text. </s>']
            # fmt: off
            _A=	[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]
            _A=	[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
            # fmt: on

            _A=	tokenizer(lowerCAmelCase__				,      text_target=lowerCAmelCase__       )

            self.assertEqual(lowerCAmelCase__				,      batch['input_ids'][0]       )
            self.assertEqual(lowerCAmelCase__				,      batch['labels'][0]       )



     def 			a__  (					self       ):
            # safety check on max_len default value so we are sure the test works
            _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(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          tokenizer.save_pretrained(lowerCAmelCase__       )

                          _A=	tokenizer.__class__.from_pretrained(lowerCAmelCase__       )
                          _A=	after_tokenizer.encode(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          self.assertListEqual(lowerCAmelCase__				,      lowerCAmelCase__       )

                          shutil.rmtree(lowerCAmelCase__       )

            _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(lowerCAmelCase__				,      add_special_tokens=lowerCAmelCase__       )
                          tokenizer.save_pretrained(lowerCAmelCase__       )

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

                          _A=	tokenizer.__class__.from_pretrained(lowerCAmelCase__				,      model_max_length=43       )
                          self.assertEqual(tokenizer.model_max_length				,      43       )

                          shutil.rmtree(lowerCAmelCase__       )



     def 			a__  (					self       ):
            _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(lowerCAmelCase__       )

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

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

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

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

                          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       ):
            _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(lowerCAmelCase__       )

                          _A=	tokenizer_class.from_pretrained(lowerCAmelCase__       )

                          self.assertTrue(tokenizer.decode([255]       ) == ''       )



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
            # and special added tokens as tokens
            _A=	self.get_tokenizers(fast=lowerCAmelCase__				,      do_lower_case=lowerCAmelCase__       )
            for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                          _A=	['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
                          _A=	tokenizer.convert_tokens_to_string(lowerCAmelCase__       )

                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A=	self.get_tokenizers()
            for tokenizer in tokenizers:
                   with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                          _A=	[
                              'bos_token',
                              'eos_token',
                              'unk_token',
                              'sep_token',
                              'pad_token',
                              'cls_token',
                              'mask_token',
                          ]

                          _A=	0
                          _A=	tokenizer.convert_ids_to_tokens(
                              lowerCAmelCase__				,      skip_special_tokens=lowerCAmelCase__       )

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

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

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

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