




from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
			from transformers import (
			    TFAutoModel,
			    TFAutoModelForCausalLM,
			    TFAutoModelForMaskedLM,
			    TFAutoModelForPreTraining,
			    TFAutoModelForQuestionAnswering,
			    TFAutoModelForSeqaSeqLM,
			    TFAutoModelForSequenceClassification,
			    TFAutoModelForTableQuestionAnswering,
			    TFAutoModelForTokenClassification,
			    TFAutoModelWithLMHead,
			    TFBertForMaskedLM,
			    TFBertForPreTraining,
			    TFBertForQuestionAnswering,
			    TFBertForSequenceClassification,
			    TFBertModel,
			    TFFunnelBaseModel,
			    TFFunnelModel,
			    TFGPTaLMHeadModel,
			    TFRobertaForMaskedLM,
			    TFTaForConditionalGeneration,
			    TFTapasForQuestionAnswering,
			)
			from transformers.models.auto.modeling_tf_auto import (
			    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_MASKED_LM_MAPPING,
			    TF_MODEL_FOR_PRETRAINING_MAPPING,
			    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    TF_MODEL_MAPPING,
			)
			from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
			from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Dict       	=		'new-model'


if is_tf_available():







			class a (     __SCREAMING_SNAKE_CASE  ):
									"""simple docstring"""

									UpperCamelCase_	:       Union[str, Any]       	=		NewModelConfig







@require_tf
class a (     unittest.TestCase  ):
						"""simple docstring"""

						@slow
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    '''bert-base-cased'''
													__lowercase      =    AutoConfig.from_pretrained(__A				)
													self.assertIsNotNone(__A				)
													self.assertIsInstance(__A				,							__A				)

													__lowercase      =    TFAutoModel.from_pretrained(__A				)
													self.assertIsNotNone(__A				)
													self.assertIsInstance(__A				,							__A				)






						@slow
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    '''bert-base-cased'''
													__lowercase      =    AutoConfig.from_pretrained(__A				)
													self.assertIsNotNone(__A				)
													self.assertIsInstance(__A				,							__A				)

													__lowercase      =    TFAutoModelForPreTraining.from_pretrained(__A				)
													self.assertIsNotNone(__A				)
													self.assertIsInstance(__A				,							__A				)






						@slow
						def     UpperCAmelCase_      ( self :     Dict				)    ->						List[str]:
													"""simple docstring"""


													for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowercase      =    AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)

																				__lowercase      =    TFAutoModelForCausalLM.from_pretrained(__A				)
																				__lowercase     ,     __lowercase      =    TFAutoModelForCausalLM.from_pretrained(__A				,							output_loading_info=__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)






						@slow
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						str:
													"""simple docstring"""


													for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowercase      =    AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)

																				__lowercase      =    TFAutoModelWithLMHead.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)






						@slow
						def     UpperCAmelCase_      ( self :     int				)    ->						int:
													"""simple docstring"""


													for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowercase      =    AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)

																				__lowercase      =    TFAutoModelForMaskedLM.from_pretrained(__A				)
																				__lowercase     ,     __lowercase      =    TFAutoModelForMaskedLM.from_pretrained(__A				,							output_loading_info=__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)






						@slow
						def     UpperCAmelCase_      ( self :     str				)    ->						Optional[Any]:
													"""simple docstring"""


													for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowercase      =    AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)

																				__lowercase      =    TFAutoModelForSeqaSeqLM.from_pretrained(__A				)
																				__lowercase     ,     __lowercase      =    TFAutoModelForSeqaSeqLM.from_pretrained(__A				,							output_loading_info=__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)






						@slow
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[Any]:
													"""simple docstring"""


													for model_name in ["bert-base-uncased"]:
																				__lowercase      =    AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)

																				__lowercase      =    TFAutoModelForSequenceClassification.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)






						@slow
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[Any]:
													"""simple docstring"""


													for model_name in ["bert-base-uncased"]:
																				__lowercase      =    AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)

																				__lowercase      =    TFAutoModelForQuestionAnswering.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)






						@slow
						@require_tensorflow_probability
						def     UpperCAmelCase_      ( self :     Any				)    ->						str:
													"""simple docstring"""


													for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
																				__lowercase      =    AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)

																				__lowercase      =    TFAutoModelForTableQuestionAnswering.from_pretrained(__A				)
																				__lowercase     ,     __lowercase      =    TFAutoModelForTableQuestionAnswering.from_pretrained(
																				    __A				,							output_loading_info=__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A				,							__A				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    TFAutoModelWithLMHead.from_pretrained(__A				)
													self.assertIsInstance(__A				,							__A				)
													self.assertEqual(model.num_parameters()				,							14_410				)
													self.assertEqual(model.num_parameters(only_trainable=__A				)				,							14_410				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    TFAutoModelWithLMHead.from_pretrained(__A				)
													self.assertIsInstance(__A				,							__A				)
													self.assertEqual(model.num_parameters()				,							14_410				)
													self.assertEqual(model.num_parameters(only_trainable=__A				)				,							14_410				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''				)
													self.assertIsInstance(__A				,							__A				)

													__lowercase      =    copy.deepcopy(model.config				)
													__lowercase      =    ['''FunnelBaseModel''']
													__lowercase      =    TFAutoModel.from_config(__A				)
													self.assertIsInstance(__A				,							__A				)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				model.save_pretrained(__A				)
																				__lowercase      =    TFAutoModel.from_pretrained(__A				)
																				self.assertIsInstance(__A				,							__A				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						int:
													"""simple docstring"""


													try:
																				AutoConfig.register('''new-model'''				,							__A				)

																				__lowercase      =    [
																				    TFAutoModel,
																				    TFAutoModelForCausalLM,
																				    TFAutoModelForMaskedLM,
																				    TFAutoModelForPreTraining,
																				    TFAutoModelForQuestionAnswering,
																				    TFAutoModelForSequenceClassification,
																				    TFAutoModelForTokenClassification,
																				]

																				for auto_class in auto_classes:
																											with self.subTest(auto_class.__name__				):
																																		# Wrong config class will raise an error
																																		with self.assertRaises(__A				):
																																									auto_class.register(__A				,							__A				)
																																		auto_class.register(__A				,							__A				)
																																		# Trying to register something existing in the Transformers library will raise an error
																																		with self.assertRaises(__A				):
																																									auto_class.register(__A				,							__A				)

																																		# Now that the config is registered, it can be used as any other config with the auto-API
																																		__lowercase      =    BertModelTester(self				).get_config()
																																		__lowercase      =    NewModelConfig(**tiny_config.to_dict()				)
																																		__lowercase      =    auto_class.from_config(__A				)
																																		self.assertIsInstance(__A				,							__A				)

																																		with tempfile.TemporaryDirectory() as tmp_dir:
																																									model.save_pretrained(__A				)
																																									__lowercase      =    auto_class.from_pretrained(__A				)
																																									self.assertIsInstance(__A				,							__A				)

													finally:
																				if "new-model" in CONFIG_MAPPING._extra_content:
																											del CONFIG_MAPPING._extra_content["new-model"]
																				for mapping in (
																				    TF_MODEL_MAPPING,
																				    TF_MODEL_FOR_PRETRAINING_MAPPING,
																				    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
																				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
																				    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
																				    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
																				    TF_MODEL_FOR_MASKED_LM_MAPPING,
																				):
																											if NewModelConfig in mapping._extra_content:
																																		del mapping._extra_content[NewModelConfig]






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						int:
													"""simple docstring"""


													with self.assertRaisesRegex(
													    __A				,							'''bert-base is not a local folder and is not a valid model identifier'''				):
																				__lowercase      =    TFAutoModel.from_pretrained('''bert-base'''				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						List[Any]:
													"""simple docstring"""


													with self.assertRaisesRegex(
													    __A				,							R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''				):
																				__lowercase      =    TFAutoModel.from_pretrained(__A				,							revision='''aaaaaa'''				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[int]:
													"""simple docstring"""


													with self.assertRaisesRegex(
													    __A				,							'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''				,							):
																				__lowercase      =    TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''				)






						def     UpperCAmelCase_      ( self :     int				)    ->						List[Any]:
													"""simple docstring"""


													with self.assertRaisesRegex(__A				,							'''Use `from_pt=True` to load this model'''				):
																				__lowercase      =    TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
													with RequestCounter() as counter:
																				__lowercase      =    TFAutoModel.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				)

													# With a sharded checkpoint
													__lowercase      =    TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''				)
													with RequestCounter() as counter:
																				__lowercase      =    TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''				)
																				self.assertEqual(counter.get_request_count				,							0				)
																				self.assertEqual(counter.head_request_count				,							1				)
																				self.assertEqual(counter.other_request_count				,							0				)








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

from tokenizers import processors

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


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			UpperCAmelCase__				     = None


UpperCAmelCase__				     = logging.get_logger(__name__)


UpperCAmelCase__				     = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__				     = {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase__				     = {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
UpperCAmelCase__				     = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		VOCAB_FILES_NAMES
						UpperCamelCase_	:       List[str]       	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	:       int       	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	:       Optional[int]       	=		['input_ids', 'attention_mask']
						UpperCamelCase_	:       Optional[int]       	=		MBartTokenizer

						UpperCamelCase_	:       List[int]       	=		[]
						UpperCamelCase_	:       List[int]       	=		[]






						def __init__( self :     Tuple				,							lowerCamelCase__ :     str=None				,							lowerCamelCase__ :     Dict=None				,							lowerCamelCase__ :     Optional[Any]="<s>"				,							lowerCamelCase__ :     int="</s>"				,							lowerCamelCase__ :     int="</s>"				,							lowerCamelCase__ :     List[Any]="<s>"				,							lowerCamelCase__ :     Union[str, Any]="<unk>"				,							lowerCamelCase__ :     Union[str, Any]="<pad>"				,							lowerCamelCase__ :     List[Any]="<mask>"				,							lowerCamelCase__ :     Optional[Any]=None				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     Dict=None				,							**lowerCamelCase__ :     List[str]				,							)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    AddedToken(lowerCamelCase__				,							lstrip=lowerCamelCase__				,							rstrip=lowerCamelCase__				) if isinstance(lowerCamelCase__				,							lowerCamelCase__				) else mask_token

													super().__init__(
													    vocab_file=lowerCamelCase__				,							tokenizer_file=lowerCamelCase__				,							bos_token=lowerCamelCase__				,							eos_token=lowerCamelCase__				,							sep_token=lowerCamelCase__				,							cls_token=lowerCamelCase__				,							unk_token=lowerCamelCase__				,							pad_token=lowerCamelCase__				,							mask_token=lowerCamelCase__				,							src_lang=lowerCamelCase__				,							tgt_lang=lowerCamelCase__				,							additional_special_tokens=lowerCamelCase__				,							**lowerCamelCase__				,							)

													__lowercase      =    vocab_file
													__lowercase      =    False if not self.vocab_file else True

													__lowercase      =    FAIRSEQ_LANGUAGE_CODES.copy()

													if additional_special_tokens is not None:
																				# Only add those special tokens if they are not already there.
																				_additional_special_tokens.extend(
																				    [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

													self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}				)
													__lowercase      =    {
													    lang_code: self.convert_tokens_to_ids(lowerCamelCase__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
													}

													__lowercase      =    src_lang if src_lang is not None else '''en_XX'''
													__lowercase      =    self.convert_tokens_to_ids(self._src_lang				)
													__lowercase      =    tgt_lang
													self.set_src_lang_special_tokens(self._src_lang				)






						@property
						def     UpperCAmelCase_      ( self :     Any				)    ->						str:
													"""simple docstring"""


													return self._src_lang






						@src_lang.setter
						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     str				)    ->						None:
													"""simple docstring"""


													__lowercase      =    new_src_lang
													self.set_src_lang_special_tokens(self._src_lang				)






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				)    ->						List[int]:
													"""simple docstring"""


													if token_ids_a is None:
																				return self.prefix_tokens + token_ids_a + self.suffix_tokens
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				)    ->						List[int]:
													"""simple docstring"""


													__lowercase      =    [self.sep_token_id]
													__lowercase      =    [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 :     List[str]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[str]				,							lowerCamelCase__ :     Optional[str]				,							**lowerCamelCase__ :     List[str]				)    ->						Optional[int]:
													"""simple docstring"""


													if src_lang is None or tgt_lang is None:
																				raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''				)
													__lowercase      =    src_lang
													__lowercase      =    self(lowerCamelCase__				,							add_special_tokens=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)
													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    tgt_lang_id
													return inputs






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     str = "en_XX"				,							lowerCamelCase__ :     Optional[List[str]] = None				,							lowerCamelCase__ :     str = "ro_RO"				,							**lowerCamelCase__ :     Union[str, Any]				,							)    ->						BatchEncoding:
													"""simple docstring"""


													__lowercase      =    src_lang
													__lowercase      =    tgt_lang
													return super().prepare_seqaseq_batch(lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													return self.set_src_lang_special_tokens(self.src_lang				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													return self.set_tgt_lang_special_tokens(self.tgt_lang				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Optional[Any]				)    ->						None:
													"""simple docstring"""


													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    []
													__lowercase      =    [self.eos_token_id, self.cur_lang_code]

													__lowercase      =    self.convert_ids_to_tokens(self.prefix_tokens				)
													__lowercase      =    self.convert_ids_to_tokens(self.suffix_tokens				)

													__lowercase      =    processors.TemplateProcessing(
													    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,							pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,							self.prefix_tokens + self.suffix_tokens				)				)				,							)






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     str				)    ->						None:
													"""simple docstring"""


													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    []
													__lowercase      =    [self.eos_token_id, self.cur_lang_code]

													__lowercase      =    self.convert_ids_to_tokens(self.prefix_tokens				)
													__lowercase      =    self.convert_ids_to_tokens(self.suffix_tokens				)

													__lowercase      =    processors.TemplateProcessing(
													    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str				,							pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str				,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,							self.prefix_tokens + self.suffix_tokens				)				)				,							)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[str] = None				)    ->						Tuple[str]:
													"""simple docstring"""


													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
													__lowercase      =    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__				)

													return (out_vocab_file,)






