






import os
import sys
import unittest


__UpperCAmelCase     							=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase     							=						os.path.join(git_repo_path, 'src', 'transformers')

__UpperCAmelCase     							=						'\n{0} = None\n'

__UpperCAmelCase     							=						'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


__UpperCAmelCase     							=						'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'



class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				Tuple						=						find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'       )
							self.assertIsNone(_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[Any]						=						find_backend('    if not is_tokenizers_available():'       )
							self.assertEqual(_UpperCamelCase    ,		'tokenizers'       )

							UpperCAmelCase_   :				Dict						=						find_backend('    if not is_tensorflow_text_available():'       )
							self.assertEqual(_UpperCamelCase    ,		'tensorflow_text'       )

							UpperCAmelCase_   :				Dict						=						find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'       )
							self.assertEqual(_UpperCamelCase    ,		'sentencepiece_and_tokenizers'       )

							UpperCAmelCase_   :				str						=						find_backend(
							    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'       )
							self.assertEqual(_UpperCamelCase    ,		'sentencepiece_and_tensorflow_text'       )

							UpperCAmelCase_   :				List[str]						=						find_backend(
							    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'       )
							self.assertEqual(_UpperCamelCase    ,		'sentencepiece_and_tokenizers_and_vision'       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				Optional[Any]						=						read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn('torch'    ,		_UpperCamelCase       )
							self.assertIn('tensorflow_text'    ,		_UpperCamelCase       )
							self.assertIn('sentencepiece_and_tokenizers'    ,		_UpperCamelCase       )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn('BertModel'    ,		objects['torch']       )
							self.assertIn('TFBertModel'    ,		objects['tf']       )
							self.assertIn('FlaxBertModel'    ,		objects['flax']       )
							self.assertIn('BertModel'    ,		objects['torch']       )
							self.assertIn('TFBertTokenizer'    ,		objects['tensorflow_text']       )
							self.assertIn('convert_slow_tokenizer'    ,		objects['sentencepiece_and_tokenizers']       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Optional[Any]						=						create_dummy_object('CONSTANT'    ,		'\'torch\''       )
							self.assertEqual(_UpperCamelCase    ,		'\nCONSTANT = None\n'       )

							UpperCAmelCase_   :				Any						=						create_dummy_object('function'    ,		'\'torch\''       )
							self.assertEqual(
							    _UpperCamelCase    ,		'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'       )

							UpperCAmelCase_   :				Tuple						=						'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
							UpperCAmelCase_   :				Union[str, Any]						=						create_dummy_object('FakeClass'    ,		'\'torch\''       )
							self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Optional[int]						=						'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
							UpperCAmelCase_   :				str						=						create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}       )
							self.assertEqual(dummy_files['torch']    ,		_UpperCamelCase       )














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

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


if is_sentencepiece_available():
		from .tokenization_barthez import BarthezTokenizer
else:
		__UpperCAmelCase     							=						None

__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase     							=						{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCAmelCase     							=						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__UpperCAmelCase     							=						'▁'



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   List[Any]							=  VOCAB_FILES_NAMES
		_snake_case   :   Union[str, Any]							=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case   :   Dict							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case   :   Dict							=  ['''input_ids''', '''attention_mask''']
		_snake_case   :   Union[str, Any]							=  BarthezTokenizer



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="</s>"    ,		_UpperCamelCase="<s>"    ,		_UpperCamelCase="<unk>"    ,		_UpperCamelCase="<pad>"    ,		_UpperCamelCase="<mask>"    ,		**_UpperCamelCase    ,		)     ->							str:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_   :				List[Any]						=						AddedToken(_UpperCamelCase    ,		lstrip=_UpperCamelCase    ,		rstrip=_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else mask_token

							super().__init__(
							    _UpperCamelCase    ,		tokenizer_file=_UpperCamelCase    ,		bos_token=_UpperCamelCase    ,		eos_token=_UpperCamelCase    ,		unk_token=_UpperCamelCase    ,		sep_token=_UpperCamelCase    ,		cls_token=_UpperCamelCase    ,		pad_token=_UpperCamelCase    ,		mask_token=_UpperCamelCase    ,		**_UpperCamelCase    ,		)

							UpperCAmelCase_   :				Tuple						=						vocab_file
							UpperCAmelCase_   :				Tuple						=						False if not self.vocab_file else True



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:

							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_   :				Union[str, Any]						=						[self.cls_token_id]
							UpperCAmelCase_   :				Optional[Any]						=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							List[int]:
							UpperCAmelCase_   :				Any						=						[self.sep_token_id]
							UpperCAmelCase_   :				List[Any]						=						[self.cls_token_id]

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[str]:
							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(_UpperCamelCase       ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
												return
							UpperCAmelCase_   :				List[Any]						=						os.path.join(
							    _UpperCamelCase    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(_UpperCamelCase       ):
												copyfile(self.vocab_file    ,		_UpperCamelCase       )

							return (out_vocab_file,)






