


import os
import sys
import unittest


__lowerCamelCase							:							Optional[Any]					 =      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
__lowerCamelCase							:							Tuple					 =      os.path.join(git_repo_path, "src", "transformers")

__lowerCamelCase							:							List[Any]					 =      '''
{0} = None
'''

__lowerCamelCase							:							str					 =      '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__lowerCamelCase							:							List[Any]					 =      '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''



class       a   (   unittest.TestCase ):






		def    lowerCAmelCase_	(	self				)->      Union[str, Any]:
						'''simple docstring'''


						A__  :				int 					=find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''				)
						self.assertIsNone(snake_case__				)

						A__  :				Union[str, Any] 					=find_backend('''    if not is_tokenizers_available():'''				)
						self.assertEqual(snake_case__						,  '''tokenizers'''				)

						A__  :				Optional[Any] 					=find_backend('''    if not is_tensorflow_text_available():'''				)
						self.assertEqual(snake_case__						,  '''tensorflow_text'''				)

						A__  :				int 					=find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''				)
						self.assertEqual(snake_case__						,  '''sentencepiece_and_tokenizers'''				)

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

						A__  :				Tuple 					=find_backend(
						    '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''				)
						self.assertEqual(snake_case__						,  '''sentencepiece_and_tokenizers_and_vision'''				)






		def    lowerCAmelCase_	(	self				)->      Dict:
						'''simple docstring'''


						A__  :				str 					=read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn('''torch'''						,  snake_case__				)
						self.assertIn('''tensorflow_text'''						,  snake_case__				)
						self.assertIn('''sentencepiece_and_tokenizers'''						,  snake_case__				)

						# 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    lowerCAmelCase_	(	self				)->      str:
						'''simple docstring'''


						A__  :				str 					=create_dummy_object('''CONSTANT'''						,  '''\'torch\''''				)
						self.assertEqual(snake_case__						,  '''\nCONSTANT = None\n'''				)

						A__  :				Dict 					=create_dummy_object('''function'''						,  '''\'torch\''''				)
						self.assertEqual(
						    snake_case__						,  '''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''				)

						A__  :				List[str] 					='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
						A__  :				Dict 					=create_dummy_object('''FakeClass'''						,  '''\'torch\''''				)
						self.assertEqual(snake_case__						,  snake_case__				)






		def    lowerCAmelCase_	(	self				)->      Union[str, Any]:
						'''simple docstring'''


						A__  :				Optional[Any] 					='# 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'
						A__  :				Dict 					=create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}				)
						self.assertEqual(dummy_files['''torch''']						,  snake_case__				)









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__ :   Dict						=							None

UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)

UpperCamelCase__ :   Union[str, Any]						=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   Dict						=							{
    '''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__ :   Dict						=							{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

UpperCamelCase__ :   Any						=							'''▁'''







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[str]						=     VOCAB_FILES_NAMES
				__a     :       int						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Optional[int]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       List[Any]						=     ["input_ids", "attention_mask"]
				__a     :       Dict						=     BarthezTokenizer






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

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

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_file
							SCREAMING_SNAKE_CASE_   : int					  =      False if not self.vocab_file else True






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):

							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : List[str]					  =      [self.cls_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : 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 		snake_case	(			self      ,snake_case__      ,snake_case__ = 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(snake_case__ ):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
										return
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      os.path.join(
							    snake_case__      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

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

							return (out_vocab_file,)



