

'''simple docstring'''




import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_ta import TaTokenizer
else:
      _lowerCAmelCase         =						None


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase         =						{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCAmelCase         =						{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}






class   _SCREAMING_SNAKE_CASE   (   a__     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:int      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					["""input_ids""", """attention_mask"""]
 __SCREAMING_SNAKE_CASE					:Dict      	=					TaTokenizer

 __SCREAMING_SNAKE_CASE					:List[int]      	=					[]



 def __init__(   self		:   Optional[int]			,				a__		:   Tuple=None			,				a__		:   Optional[Any]=None			,				a__		:   Optional[int]="</s>"			,				a__		:   Optional[Any]="<unk>"			,				a__		:   List[str]="<pad>"			,				a__		:   Dict=100			,				a__		:   List[Any]=None			,				**a__		:   Tuple			,				):
      # Add extra_ids to the special token list
      if extra_ids > 0 and additional_special_tokens is None:
           __magic_name__						       =    [F'''<extra_id_{i}>''' for i in range(lowerCAmelCase__      )]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra special tokens
           __magic_name__						       =    len(set(filter(lambda a__      : bool('''extra_id_''' in str(lowerCAmelCase__      )      )			,				lowerCAmelCase__      )      )      )
           if extra_tokens != extra_ids:
                raise ValueError(
                    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                    ''' tokens'''      )

      super().__init__(
          lowerCAmelCase__			,				tokenizer_file=lowerCAmelCase__			,				eos_token=lowerCAmelCase__			,				unk_token=lowerCAmelCase__			,				pad_token=lowerCAmelCase__			,				extra_ids=lowerCAmelCase__			,				additional_special_tokens=lowerCAmelCase__			,				**lowerCAmelCase__			,				)

      __magic_name__						       =    vocab_file
      __magic_name__						       =    False if not self.vocab_file else True
      __magic_name__						       =    extra_ids



 @staticmethod
 def 			snake_case__  (   a__		:   List[Any]			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
           __magic_name__						       =    TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
           elif init_max_model_length is None:
                warnings.warn(
                    '''This tokenizer was incorrectly instantiated with a model max length of'''
                    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
                    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''			,				lowerCAmelCase__			,				)

      return max_model_length



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[Any]			,				a__		:   Optional[Any] = None      ):
      if not self.can_save_slow_tokenizer:
           raise ValueError(
               '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
               '''tokenizer.'''      )

      if not os.path.isdir(lowerCAmelCase__      ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
           return
      __magic_name__						       =    os.path.join(
          lowerCAmelCase__			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

      if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowerCAmelCase__      ):
           copyfile(self.vocab_file			,				lowerCAmelCase__      )
           logger.info(F'''Copy vocab file to {out_vocab_file}'''      )

      return (out_vocab_file,)



 def 			snake_case__  (   self		:   Any			,				a__		:   Tuple			,				a__		:   Tuple = None      ):
      __magic_name__						       =    token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
           return self.prefix_tokens + token_ids_a
      else:
           __magic_name__						       =    token_ids_a + [self.eos_token_id]
           return self.prefix_tokens + token_ids_a + token_ids_a



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   Any			,				a__		:   Any = None      ):
      __magic_name__						       =    [self.eos_token_id]

      if token_ids_a is None:
           return len(token_ids_a + eos      ) * [0]
      return len(token_ids_a + eos + token_ids_a + eos      ) * [0]



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return list(
          set(filter(lambda a__      : bool(re.search(r'''<extra_id_\d+>'''			,				lowerCAmelCase__      )      ) is not None			,				self.additional_special_tokens      )      )      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return [self.convert_tokens_to_ids(lowerCAmelCase__      ) for token in self.get_sentinel_tokens()]




'''simple docstring'''




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_lowerCAmelCase         =						get_tests_dir("fixtures")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase         =						get_tests_dir("fixtures/dummy-config.json")






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    0



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      with tempfile.TemporaryDirectory() as tmpdirname:
           __magic_name__						       =    WavaVecaConfig()

           # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      ).to_dict()

           config_dict.pop('''feature_extractor_type'''      )
           __magic_name__						       =    WavaVecaFeatureExtractor(**a__      )

           # save in new folder
           model_config.save_pretrained(a__      )
           config.save_pretrained(a__      )

           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )

           # make sure private variable is not incorrectly saved
           __magic_name__						       =    json.loads(config.to_json_string()      )
           self.assertTrue('''_processor_class''' not in dict_as_saved      )

      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
      self.assertIsInstance(a__			,				a__      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				'''bert-base is not a local folder and is not a valid model identifier'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''bert-base'''      )



 def 			snake_case__  (   self		:   str      ):
      with self.assertRaisesRegex(
          a__			,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				revision='''aaaaaa'''      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      with self.assertRaisesRegex(
          a__			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''      )



 def 			snake_case__  (   self		:   Dict      ):
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(a__      ):
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )

      __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
          '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
      self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )

      # Test feature extractor can be reloaded.
      with tempfile.TemporaryDirectory() as tmp_dir:
           feature_extractor.save_pretrained(a__      )
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__			,				trust_remote_code=a__      )
      self.assertEqual(reloaded_feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )



 def 			snake_case__  (   self		:   int      ):
      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(a__      ):
                AutoFeatureExtractor.register(a__			,				a__      )

           # Now that the config is registered, it can be used as any other config with the auto-API
           __magic_name__						       =    CustomFeatureExtractor.from_pretrained(a__      )
           with tempfile.TemporaryDirectory() as tmp_dir:
                feature_extractor.save_pretrained(a__      )
                __magic_name__						       =    AutoFeatureExtractor.from_pretrained(a__      )
                self.assertIsInstance(a__			,				a__      )

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



 def 			snake_case__  (   self		:   int      ):






      class   _SCREAMING_SNAKE_CASE   (   __a     ):
       __SCREAMING_SNAKE_CASE					:Optional[int]      	=					True

      try:
           AutoConfig.register('''custom'''			,				a__      )
           AutoFeatureExtractor.register(a__			,				a__      )
           # If remote code is not set, the default is to use local
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote code is disabled, we load the local one.
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(feature_extractor.is_local      )

           # If remote is enabled, we load from the Hub
           __magic_name__						       =    AutoFeatureExtractor.from_pretrained(
               '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=a__      )
           self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''      )
           self.assertTrue(not hasattr(a__			,				'''is_local'''      )      )

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






