




import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase=True					,		_lowercase="pt" ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     {'''add_prefix_space''': True} if isinstance(_lowercase					,		_lowercase ) and not line.startswith(''' ''' ) else {}
     UpperCAmelCase_     :				List[str]  =     padding_side
     return tokenizer(
         [line]					,		max_length=_lowercase					,		padding='''max_length''' if pad_to_max_length else None					,		truncation=_lowercase					,		return_tensors=_lowercase					,		add_special_tokens=_lowercase					,		**_lowercase					,		)







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=None					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     input_ids.ne(_lowercase ).any(dim=0 )
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE="train"  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=""  ,)					->						Tuple:
         super().__init__()
         UpperCAmelCase_     :				Optional[Any]  =     Path(_SCREAMING_SNAKE_CASE    ).joinpath(type_path + '''.source'''    )
         UpperCAmelCase_     :				List[Any]  =     Path(_SCREAMING_SNAKE_CASE    ).joinpath(type_path + '''.target'''    )
         UpperCAmelCase_     :				Tuple  =     self.get_char_lens(self.src_file    )
         UpperCAmelCase_     :				Any  =     max_source_length
         UpperCAmelCase_     :				str  =     max_target_length
         assert min(self.src_lens    ) > 0, f'''found empty line in {self.src_file}'''
         UpperCAmelCase_     :				str  =     tokenizer
         UpperCAmelCase_     :				Union[str, Any]  =     prefix
         if n_obs is not None:
              UpperCAmelCase_     :				Dict  =     self.src_lens[:n_obs]
         UpperCAmelCase_     :				Union[str, Any]  =     src_lang
         UpperCAmelCase_     :				Any  =     tgt_lang





    def __len__( self    )					->						Any:
         return len(self.src_lens    )





    def __getitem__( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict[str, torch.Tensor]:
         UpperCAmelCase_     :				Any  =     index + 1  # linecache starts at 1
         UpperCAmelCase_     :				int  =     self.prefix + linecache.getline(str(self.src_file    )  ,_SCREAMING_SNAKE_CASE    ).rstrip('''\n'''    )
         UpperCAmelCase_     :				int  =     linecache.getline(str(self.tgt_file    )  ,_SCREAMING_SNAKE_CASE    ).rstrip('''\n'''    )
         assert source_line, f'''empty source line for index {index}'''
         assert tgt_line, f'''empty tgt line for index {index}'''

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    ):
              source_line += self.tokenizer.eos_token
              tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         UpperCAmelCase_     :				Tuple  =     (
             self.tokenizer.question_encoder if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    ) else self.tokenizer
         )
         UpperCAmelCase_     :				Dict  =     self.tokenizer.generator if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    ) else self.tokenizer

         UpperCAmelCase_     :				Tuple  =     encode_line(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,self.max_source_length  ,'''right'''    )
         UpperCAmelCase_     :				Tuple  =     encode_line(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,self.max_target_length  ,'''right'''    )

         UpperCAmelCase_     :				Optional[Any]  =     source_inputs['''input_ids'''].squeeze()
         UpperCAmelCase_     :				Optional[Any]  =     target_inputs['''input_ids'''].squeeze()
         UpperCAmelCase_     :				Tuple  =     source_inputs['''attention_mask'''].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }





    @staticmethod
    def 				a__ ( _SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         return [len(_SCREAMING_SNAKE_CASE    ) for x in Path(_SCREAMING_SNAKE_CASE    ).open().readlines()]





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict[str, torch.Tensor]:
         UpperCAmelCase_     :				int  =     torch.stack([x['''input_ids'''] for x in batch]    )
         UpperCAmelCase_     :				Dict  =     torch.stack([x['''attention_mask'''] for x in batch]    )
         UpperCAmelCase_     :				Tuple  =     torch.stack([x['''decoder_input_ids'''] for x in batch]    )
         UpperCAmelCase_     :				int  =     (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    )
             else self.tokenizer.pad_token_id
         )
         UpperCAmelCase_     :				Tuple  =     (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer  ,_SCREAMING_SNAKE_CASE    )
             else self.tokenizer.pad_token_id
         )
         UpperCAmelCase_     :				str  =     trim_batch(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     trim_batch(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     {
             '''input_ids''': source_ids,
             '''attention_mask''': source_mask,
             '''decoder_input_ids''': y,
         }
         return batch


__a							     =					getLogger(__name__)







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return list(itertools.chain.from_iterable(_lowercase ) )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     get_git_info()
     save_json(_lowercase					,		os.path.join(_lowercase					,		'''git_log.json''' ) )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=4					,		**_lowercase ):

     '''simple docstring'''


     with open(_lowercase					,		'''w''' ) as f:
          json.dump(_lowercase					,		_lowercase					,		indent=_lowercase					,		**_lowercase )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     with open(_lowercase ) as f:
          return json.load(_lowercase )







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     git.Repo(search_parent_directories=_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     {
         '''repo_id''': str(_lowercase ),
         '''repo_sha''': str(repo.head.object.hexsha ),
         '''repo_branch''': str(repo.active_branch ),
         '''hostname''': str(socket.gethostname() ),
     }
     return repo_infos







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     return list(map(_lowercase					,		_lowercase ) )







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     with open(_lowercase					,		'''wb''' ) as f:
          return pickle.dump(_lowercase					,		_lowercase )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     def remove_articles(_lowercase ):
          return re.sub(r'''\b(a|an|the)\b'''					,		''' '''					,		_lowercase )

     def white_space_fix(_lowercase ):
          return " ".join(text.split() )

     def remove_punc(_lowercase ):
          UpperCAmelCase_     :				Any  =     set(string.punctuation )
          return "".join(ch for ch in text if ch not in exclude )

     def lower(_lowercase ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(_lowercase ) ) ) )







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Any  =     normalize_answer(_lowercase ).split()
     UpperCAmelCase_     :				List[Any]  =     normalize_answer(_lowercase ).split()
     UpperCAmelCase_     :				Any  =     Counter(_lowercase ) & Counter(_lowercase )
     UpperCAmelCase_     :				Tuple  =     sum(common.values() )
     if num_same == 0:
          return 0
     UpperCAmelCase_     :				str  =     1.0 * num_same / len(_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     1.0 * num_same / len(_lowercase )
     UpperCAmelCase_     :				Any  =     (2 * precision * recall) / (precision + recall)
     return fa







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     return normalize_answer(_lowercase ) == normalize_answer(_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     assert len(_lowercase ) == len(_lowercase )
     UpperCAmelCase_     :				Optional[int]  =     0
     for hypo, pred in zip(_lowercase					,		_lowercase ):
          em += exact_match_score(_lowercase					,		_lowercase )
     if len(_lowercase ) > 0:
          em /= len(_lowercase )
     return {"em": em}







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return model_prefix.startswith('''rag''' )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[int]  =     {p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     UpperCAmelCase_     :				Dict  =     '''dropout_rate'''
     for p in extra_params:
          if getattr(_lowercase					,		_lowercase					,		_lowercase ):
               if not hasattr(_lowercase					,		_lowercase ) and not hasattr(_lowercase					,		equivalent_param[p] ):
                    logger.info('''config doesn\'t have a `{}` attribute'''.format(_lowercase ) )
                    delattr(_lowercase					,		_lowercase )
                    continue
               UpperCAmelCase_     :				Union[str, Any]  =     p if hasattr(_lowercase					,		_lowercase ) else equivalent_param[p]
               setattr(_lowercase					,		_lowercase					,		getattr(_lowercase					,		_lowercase ) )
               delattr(_lowercase					,		_lowercase )
     return hparams, config





import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
     from test_module.custom_tokenization_fast import CustomTokenizerFast

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Tuple  =     0





    @slow
    def 				a__ ( self    )					->						Any:
         for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
              UpperCAmelCase_     :				Optional[Any]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,(BertTokenizer, BertTokenizerFast)    )
              self.assertGreater(len(_SCREAMING_SNAKE_CASE    )  ,0    )

         for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
              UpperCAmelCase_     :				Union[str, Any]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,(GPTaTokenizer, GPTaTokenizerFast)    )
              self.assertGreater(len(_SCREAMING_SNAKE_CASE    )  ,0    )





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				List[str]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
         self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,(BertTokenizer, BertTokenizerFast)    )
         self.assertEqual(tokenizer.vocab_size  ,12    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Any  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
         self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,(RobertaTokenizer, RobertaTokenizerFast)    )
         self.assertEqual(tokenizer.vocab_size  ,20    )





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				int  =     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
         self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         # Check that tokenizer_type ≠ model_type
         UpperCAmelCase_     :				Optional[int]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,config=_SCREAMING_SNAKE_CASE    )
         self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,(BertTokenizer, BertTokenizerFast)    )
         self.assertEqual(tokenizer.vocab_size  ,12    )





    def 				a__ ( self    )					->						Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
              shutil.copy('''./tests/fixtures/vocab.txt'''  ,os.path.join(_SCREAMING_SNAKE_CASE  ,'''vocab.txt'''    )    )

              UpperCAmelCase_     :				Optional[int]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,tokenizer_type='''bert'''  ,use_fast=_SCREAMING_SNAKE_CASE    )
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         with tempfile.TemporaryDirectory() as tmp_dir:
              shutil.copy('''./tests/fixtures/vocab.json'''  ,os.path.join(_SCREAMING_SNAKE_CASE  ,'''vocab.json'''    )    )
              shutil.copy('''./tests/fixtures/merges.txt'''  ,os.path.join(_SCREAMING_SNAKE_CASE  ,'''merges.txt'''    )    )

              UpperCAmelCase_     :				Optional[Any]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,tokenizer_type='''gpt2'''  ,use_fast=_SCREAMING_SNAKE_CASE    )
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    @require_tokenizers
    def 				a__ ( self    )					->						Optional[int]:
         with tempfile.TemporaryDirectory() as tmp_dir:
              shutil.copy('''./tests/fixtures/vocab.txt'''  ,os.path.join(_SCREAMING_SNAKE_CASE  ,'''vocab.txt'''    )    )

              UpperCAmelCase_     :				Tuple  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,tokenizer_type='''bert'''    )
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         with tempfile.TemporaryDirectory() as tmp_dir:
              shutil.copy('''./tests/fixtures/vocab.json'''  ,os.path.join(_SCREAMING_SNAKE_CASE  ,'''vocab.json'''    )    )
              shutil.copy('''./tests/fixtures/merges.txt'''  ,os.path.join(_SCREAMING_SNAKE_CASE  ,'''merges.txt'''    )    )

              UpperCAmelCase_     :				Union[str, Any]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,tokenizer_type='''gpt2'''    )
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						int:
         with pytest.raises(_SCREAMING_SNAKE_CASE    ):
              AutoTokenizer.from_pretrained('''./'''  ,tokenizer_type='''xxx'''    )





    @require_tokenizers
    def 				a__ ( self    )					->						Optional[Any]:
         for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
              UpperCAmelCase_     :				Any  =     tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased'''    )
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,(BertTokenizer, BertTokenizerFast)    )

              if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                   self.assertEqual(tokenizer.basic_tokenizer.do_lower_case  ,_SCREAMING_SNAKE_CASE    )
              else:
                   self.assertEqual(tokenizer.do_lower_case  ,_SCREAMING_SNAKE_CASE    )

              self.assertEqual(tokenizer.model_max_length  ,512    )





    @require_tokenizers
    def 				a__ ( self    )					->						List[Any]:
         for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
              with self.assertRaisesRegex(
                  _SCREAMING_SNAKE_CASE  ,'''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'''  ,):
                   UpperCAmelCase_     :				int  =     tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists'''    )





    def 				a__ ( self    )					->						Optional[Any]:
         # tests: https://github.com/huggingface/transformers/pull/13251
         # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
         # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
         UpperCAmelCase_     :				int  =     TOKENIZER_MAPPING.values()
         UpperCAmelCase_     :				List[Any]  =     []

         for slow_tok, fast_tok in tokenizers:
              if slow_tok is not None:
                   tokenizer_names.append(slow_tok.__name__    )

              if fast_tok is not None:
                   tokenizer_names.append(fast_tok.__name__    )

         for tokenizer_name in tokenizer_names:
              # must find the right class
              tokenizer_class_from_name(_SCREAMING_SNAKE_CASE    )





    @require_tokenizers
    def 				a__ ( self    )					->						Tuple:
         self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''  ,use_fast=_SCREAMING_SNAKE_CASE    )  ,_SCREAMING_SNAKE_CASE    )
         self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''    )  ,_SCREAMING_SNAKE_CASE    )





    @require_tokenizers
    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				str  =     AutoTokenizer.from_pretrained('''distilbert-base-uncased'''  ,do_lower_case=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     '''Hello, world. How are you?'''
         UpperCAmelCase_     :				List[Any]  =     tokenizer.tokenize(_SCREAMING_SNAKE_CASE    )
         self.assertEqual('''[UNK]'''  ,tokens[0]    )

         UpperCAmelCase_     :				Any  =     AutoTokenizer.from_pretrained('''microsoft/mpnet-base'''  ,do_lower_case=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     tokenizer.tokenize(_SCREAMING_SNAKE_CASE    )
         self.assertEqual('''[UNK]'''  ,tokens[0]    )





    @require_tokenizers
    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				List[Any]  =     AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config'''    )
         self.assertEqual(type(_SCREAMING_SNAKE_CASE    )  ,_SCREAMING_SNAKE_CASE    )
         self.assertEqual(tokenizer.model_max_length  ,512    )
         self.assertEqual(tokenizer.vocab_size  ,30_000    )
         self.assertEqual(tokenizer.unk_token  ,'''[UNK]'''    )
         self.assertEqual(tokenizer.padding_side  ,'''right'''    )
         self.assertEqual(tokenizer.truncation_side  ,'''right'''    )





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Any  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
         self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,(BertTokenizer, BertTokenizerFast)    )
         with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Dict  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )

         self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,tokenizer.__class__    )
         self.assertEqual(tokenizera.vocab_size  ,12    )





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				Tuple  =     AutoTokenizer.from_pretrained('''ctrl'''    )
         # There is no fast CTRL so this always gives us a slow tokenizer.
         self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						str:
         # Check we can load the tokenizer config of an online model.
         UpperCAmelCase_     :				int  =     get_tokenizer_config('''bert-base-cased'''    )
         UpperCAmelCase_     :				Optional[int]  =     config.pop('''_commit_hash'''  ,_SCREAMING_SNAKE_CASE    )
         # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
         self.assertEqual(_SCREAMING_SNAKE_CASE  ,{'''do_lower_case''': False}    )

         # This model does not have a tokenizer_config so we get back an empty dict.
         UpperCAmelCase_     :				Any  =     get_tokenizer_config(_SCREAMING_SNAKE_CASE    )
         self.assertDictEqual(_SCREAMING_SNAKE_CASE  ,{}    )

         # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
         UpperCAmelCase_     :				Dict  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
         with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Dict  =     get_tokenizer_config(_SCREAMING_SNAKE_CASE    )

         # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
         self.assertEqual(config['''tokenizer_class''']  ,'''BertTokenizer'''    )





    def 				a__ ( self    )					->						str:
         try:
              AutoConfig.register('''custom'''  ,_SCREAMING_SNAKE_CASE    )

              AutoTokenizer.register(_SCREAMING_SNAKE_CASE  ,slow_tokenizer_class=_SCREAMING_SNAKE_CASE    )
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
                   AutoTokenizer.register(_SCREAMING_SNAKE_CASE  ,slow_tokenizer_class=_SCREAMING_SNAKE_CASE    )

              UpperCAmelCase_     :				str  =     CustomTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )

                   UpperCAmelCase_     :				Dict  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
                   self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]





    @require_tokenizers
    def 				a__ ( self    )					->						int:
         try:
              AutoConfig.register('''custom'''  ,_SCREAMING_SNAKE_CASE    )

              # Can register in two steps
              AutoTokenizer.register(_SCREAMING_SNAKE_CASE  ,slow_tokenizer_class=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(TOKENIZER_MAPPING[CustomConfig]  ,(CustomTokenizer, None)    )
              AutoTokenizer.register(_SCREAMING_SNAKE_CASE  ,fast_tokenizer_class=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(TOKENIZER_MAPPING[CustomConfig]  ,(CustomTokenizer, CustomTokenizerFast)    )

              del TOKENIZER_MAPPING._extra_content[CustomConfig]
              # Can register in one step
              AutoTokenizer.register(
                  _SCREAMING_SNAKE_CASE  ,slow_tokenizer_class=_SCREAMING_SNAKE_CASE  ,fast_tokenizer_class=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(TOKENIZER_MAPPING[CustomConfig]  ,(CustomTokenizer, CustomTokenizerFast)    )

              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
                   AutoTokenizer.register(_SCREAMING_SNAKE_CASE  ,fast_tokenizer_class=_SCREAMING_SNAKE_CASE    )

              # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
              # and that model does not have a tokenizer.json
              with tempfile.TemporaryDirectory() as tmp_dir:
                   UpperCAmelCase_     :				List[str]  =     BertTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE    )
                   bert_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				str  =     CustomTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE    )

              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )

                   UpperCAmelCase_     :				Optional[int]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
                   self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

                   UpperCAmelCase_     :				Any  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,use_fast=_SCREAMING_SNAKE_CASE    )
                   self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]





    def 				a__ ( self    )					->						Optional[int]:
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				Tuple  =     AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''    )
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				Union[str, Any]  =     AutoTokenizer.from_pretrained(
                  '''hf-internal-testing/test_dynamic_tokenizer'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Dict  =     AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE    )
         self.assertTrue(tokenizer.special_attribute_present    )
         # Test tokenizer can be reloaded.
         with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				int  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,trust_remote_code=_SCREAMING_SNAKE_CASE    )
         self.assertTrue(reloaded_tokenizer.special_attribute_present    )

         if is_tokenizers_available():
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizerFast'''    )
              self.assertEqual(reloaded_tokenizer.__class__.__name__  ,'''NewTokenizerFast'''    )

              # Test we can also load the slow version
              UpperCAmelCase_     :				List[str]  =     AutoTokenizer.from_pretrained(
                  '''hf-internal-testing/test_dynamic_tokenizer'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE  ,use_fast=_SCREAMING_SNAKE_CASE    )
              self.assertTrue(tokenizer.special_attribute_present    )
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizer'''    )
              # Test tokenizer can be reloaded.
              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Optional[int]  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,trust_remote_code=_SCREAMING_SNAKE_CASE  ,use_fast=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(reloaded_tokenizer.__class__.__name__  ,'''NewTokenizer'''    )
              self.assertTrue(reloaded_tokenizer.special_attribute_present    )
         else:
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizer'''    )
              self.assertEqual(reloaded_tokenizer.__class__.__name__  ,'''NewTokenizer'''    )





    @require_tokenizers
    def 				a__ ( self    )					->						Optional[int]:

         class     __a(	_a  ):






             """simple docstring"""





             lowerCAmelCase         = False


         class     __a(	_a  ):






             """simple docstring"""





             lowerCAmelCase         = NewTokenizer
             lowerCAmelCase         = False

         try:
              AutoConfig.register('''custom'''  ,_SCREAMING_SNAKE_CASE    )
              AutoTokenizer.register(_SCREAMING_SNAKE_CASE  ,slow_tokenizer_class=_SCREAMING_SNAKE_CASE    )
              AutoTokenizer.register(_SCREAMING_SNAKE_CASE  ,fast_tokenizer_class=_SCREAMING_SNAKE_CASE    )
              # If remote code is not set, the default is to use local
              UpperCAmelCase_     :				int  =     AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''    )
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizerFast'''    )
              self.assertFalse(tokenizer.special_attribute_present    )
              UpperCAmelCase_     :				Any  =     AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''  ,use_fast=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizer'''    )
              self.assertFalse(tokenizer.special_attribute_present    )

              # If remote code is disabled, we load the local one.
              UpperCAmelCase_     :				Tuple  =     AutoTokenizer.from_pretrained(
                  '''hf-internal-testing/test_dynamic_tokenizer'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizerFast'''    )
              self.assertFalse(tokenizer.special_attribute_present    )
              UpperCAmelCase_     :				List[str]  =     AutoTokenizer.from_pretrained(
                  '''hf-internal-testing/test_dynamic_tokenizer'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE  ,use_fast=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizer'''    )
              self.assertFalse(tokenizer.special_attribute_present    )

              # If remote is enabled, we load from the Hub
              UpperCAmelCase_     :				Optional[int]  =     AutoTokenizer.from_pretrained(
                  '''hf-internal-testing/test_dynamic_tokenizer'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizerFast'''    )
              self.assertTrue(tokenizer.special_attribute_present    )
              UpperCAmelCase_     :				Any  =     AutoTokenizer.from_pretrained(
                  '''hf-internal-testing/test_dynamic_tokenizer'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE  ,use_fast=_SCREAMING_SNAKE_CASE    )
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizer'''    )
              self.assertTrue(tokenizer.special_attribute_present    )

         finally:
              if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
              if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				Optional[int]  =     AutoTokenizer.from_pretrained(
             '''hf-internal-testing/test_dynamic_tokenizer_legacy'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE    )
         self.assertTrue(tokenizer.special_attribute_present    )
         if is_tokenizers_available():
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizerFast'''    )

              # Test we can also load the slow version
              UpperCAmelCase_     :				Tuple  =     AutoTokenizer.from_pretrained(
                  '''hf-internal-testing/test_dynamic_tokenizer_legacy'''  ,trust_remote_code=_SCREAMING_SNAKE_CASE  ,use_fast=_SCREAMING_SNAKE_CASE    )
              self.assertTrue(tokenizer.special_attribute_present    )
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizer'''    )
         else:
              self.assertEqual(tokenizer.__class__.__name__  ,'''NewTokenizer'''    )





    def 				a__ ( self    )					->						Optional[Any]:
         with self.assertRaisesRegex(
             _SCREAMING_SNAKE_CASE  ,'''bert-base is not a local folder and is not a valid model identifier'''    ):
              UpperCAmelCase_     :				Tuple  =     AutoTokenizer.from_pretrained('''bert-base'''    )





    def 				a__ ( self    )					->						List[Any]:
         with self.assertRaisesRegex(
             _SCREAMING_SNAKE_CASE  ,R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''    ):
              UpperCAmelCase_     :				str  =     AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ,revision='''aaaaaa'''    )





    def 				a__ ( self    )					->						Any:
         # Make sure we have cached the tokenizer.
         UpperCAmelCase_     :				Optional[int]  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )
         with RequestCounter() as counter:
              UpperCAmelCase_     :				List[str]  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )
              self.assertEqual(counter.get_request_count  ,0    )
              self.assertEqual(counter.head_request_count  ,1    )
              self.assertEqual(counter.other_request_count  ,0    )