


"""simple docstring"""
import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
			from tokenizers import Tokenizer
			from tokenizers.models import WordPiece



class      _UpperCAmelCase					(				unittest.TestCase							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		str         =							list(tokenizer_class.max_model_input_sizes.keys()   )
			for model_name in sa_models[:1]:
				__snake_case      :		Union[str, Any]         =							tokenizer_class.from_pretrained(a_   )
				self.assertIsNotNone(a_   )
				self.assertIsInstance(a_							,			a_   )
				self.assertIsInstance(a_							,			a_   )

				for special_tok in tokenizer.all_special_tokens:
					self.assertIsInstance(a_							,			a_   )
					__snake_case      :		Optional[Any]         =							tokenizer.convert_tokens_to_ids(a_   )
					self.assertIsInstance(a_							,			a_   )



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = None   ):





			'''simple docstring'''

			__snake_case      :		Any         =							pickle.dumps(a_   )
			self.assertIsNotNone(a_   )

			__snake_case      :		int         =							pickle.loads(a_   )

			# Ensure is_fast is correctly restored
			self.assertEqual(be_restored.is_fast							,			be_original.is_fast   )

			# Ensure encodings are potentially correctly restored
			if be_original.is_fast:
				self.assertIsNotNone(be_restored.encodings   )
			else:
				self.assertIsNone(be_restored.encodings   )

			# Ensure the keys are the same
			for original_v, restored_v in zip(be_original.values()							,			be_restored.values()   ):
				if equal_op:
					self.assertTrue(equal_op(a_							,			a_   )   )
				else:
					self.assertEqual(a_							,			a_   )



		@slow
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			self.check_tokenizer_from_pretrained(a_   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			self.assertEqual(TensorType('''tf'''   )							,			TensorType.TENSORFLOW   )
			self.assertEqual(TensorType('''pt'''   )							,			TensorType.PYTORCH   )
			self.assertEqual(TensorType('''np'''   )							,			TensorType.NUMPY   )



		@require_tokenizers
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			import numpy as np

			__snake_case      :		Optional[Any]         =							BertTokenizer.from_pretrained('''bert-base-cased'''   )
			__snake_case      :		Tuple         =							BertTokenizerFast.from_pretrained('''bert-base-cased'''   )

			# Python no tensor
			with self.subTest('''BatchEncoding (Python, return_tensors=None)'''   ):
				self.assert_dump_and_restore(tokenizer_p('''Small example to encode'''   )   )

			with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)'''   ):
				self.assert_dump_and_restore(
				    tokenizer_p('''Small example to encode'''							,			return_tensors=TensorType.NUMPY   )							,			np.array_equal   )

			with self.subTest('''BatchEncoding (Rust, return_tensors=None)'''   ):
				self.assert_dump_and_restore(tokenizer_r('''Small example to encode'''   )   )

			with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)'''   ):
				self.assert_dump_and_restore(
				    tokenizer_r('''Small example to encode'''							,			return_tensors=TensorType.NUMPY   )							,			np.array_equal   )



		@require_tf
		@require_tokenizers
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			import tensorflow as tf

			def tf_array_equals(a_							,			a_   ):
				return tf.reduce_all(tf.equal(a_							,			a_   )   )

			__snake_case      :		int         =							BertTokenizer.from_pretrained('''bert-base-cased'''   )
			__snake_case      :		Any         =							BertTokenizerFast.from_pretrained('''bert-base-cased'''   )

			with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)'''   ):
				self.assert_dump_and_restore(
				    tokenizer_p('''Small example to encode'''							,			return_tensors=TensorType.TENSORFLOW   )							,			a_   )

			with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''   ):
				self.assert_dump_and_restore(
				    tokenizer_r('''Small example to encode'''							,			return_tensors=TensorType.TENSORFLOW   )							,			a_   )



		@require_torch
		@require_tokenizers
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			import torch

			__snake_case      :		List[str]         =							BertTokenizer.from_pretrained('''bert-base-cased'''   )
			__snake_case      :		List[str]         =							BertTokenizerFast.from_pretrained('''bert-base-cased'''   )

			with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)'''   ):
				self.assert_dump_and_restore(
				    tokenizer_p('''Small example to encode'''							,			return_tensors=TensorType.PYTORCH   )							,			torch.equal   )

			with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)'''   ):
				self.assert_dump_and_restore(
				    tokenizer_r('''Small example to encode'''							,			return_tensors=TensorType.PYTORCH   )							,			torch.equal   )



		@require_tokenizers
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							BertTokenizer.from_pretrained('''bert-base-cased'''   )
			__snake_case      :		Tuple         =							BertTokenizerFast.from_pretrained('''bert-base-cased'''   )

			with self.subTest('''Python Tokenizer'''   ):
				self.assertFalse(tokenizer_p('''Small example to_encode'''   ).is_fast   )

			with self.subTest('''Rust Tokenizer'''   ):
				self.assertTrue(tokenizer_r('''Small example to_encode'''   ).is_fast   )



		@require_tokenizers
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							BertTokenizerFast.from_pretrained('''bert-base-cased'''   )
			__snake_case      :		Tuple         =							tokenizer_r(['''Test''', '''\xad''', '''test''']							,			is_split_into_words=a_   )

			self.assertEqual(encoded.word_to_tokens(0   )							,			TokenSpan(start=1							,			end=2   )   )
			self.assertEqual(encoded.word_to_tokens(1   )							,			a_   )
			self.assertEqual(encoded.word_to_tokens(2   )							,			TokenSpan(start=2							,			end=3   )   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}   )
			__snake_case      :		Union[str, Any]         =							batch.convert_to_tensors(tensor_type='''np'''   )
			self.assertEqual(tensor_batch['''inputs'''].shape							,			(2, 3)   )
			self.assertEqual(tensor_batch['''labels'''].shape							,			(2,)   )
			# test converting the converted
			with CaptureStderr() as cs:
				__snake_case      :		Union[str, Any]         =							batch.convert_to_tensors(tensor_type='''np'''   )
			self.assertFalse(len(cs.err   )							,			msg=f"""should have no warning, but got {cs.err}"""   )

			__snake_case      :		Optional[int]         =							BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}   )
			__snake_case      :		Dict         =							batch.convert_to_tensors(tensor_type='''np'''							,			prepend_batch_axis=a_   )
			self.assertEqual(tensor_batch['''inputs'''].shape							,			(1, 3)   )
			self.assertEqual(tensor_batch['''labels'''].shape							,			(1,)   )



		@require_torch
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}   )
			__snake_case      :		Union[str, Any]         =							batch.convert_to_tensors(tensor_type='''pt'''   )
			self.assertEqual(tensor_batch['''inputs'''].shape							,			(2, 3)   )
			self.assertEqual(tensor_batch['''labels'''].shape							,			(2,)   )
			# test converting the converted
			with CaptureStderr() as cs:
				__snake_case      :		Any         =							batch.convert_to_tensors(tensor_type='''pt'''   )
			self.assertFalse(len(cs.err   )							,			msg=f"""should have no warning, but got {cs.err}"""   )

			__snake_case      :		List[Any]         =							BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}   )
			__snake_case      :		List[Any]         =							batch.convert_to_tensors(tensor_type='''pt'''							,			prepend_batch_axis=a_   )
			self.assertEqual(tensor_batch['''inputs'''].shape							,			(1, 3)   )
			self.assertEqual(tensor_batch['''labels'''].shape							,			(1,)   )



		@require_tf
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}   )
			__snake_case      :		Dict         =							batch.convert_to_tensors(tensor_type='''tf'''   )
			self.assertEqual(tensor_batch['''inputs'''].shape							,			(2, 3)   )
			self.assertEqual(tensor_batch['''labels'''].shape							,			(2,)   )
			# test converting the converted
			with CaptureStderr() as cs:
				__snake_case      :		Optional[Any]         =							batch.convert_to_tensors(tensor_type='''tf'''   )
			self.assertFalse(len(cs.err   )							,			msg=f"""should have no warning, but got {cs.err}"""   )

			__snake_case      :		Union[str, Any]         =							BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}   )
			__snake_case      :		Union[str, Any]         =							batch.convert_to_tensors(tensor_type='''tf'''							,			prepend_batch_axis=a_   )
			self.assertEqual(tensor_batch['''inputs'''].shape							,			(1, 3)   )
			self.assertEqual(tensor_batch['''labels'''].shape							,			(1,)   )



		@require_flax
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}   )
			__snake_case      :		int         =							batch.convert_to_tensors(tensor_type='''jax'''   )
			self.assertEqual(tensor_batch['''inputs'''].shape							,			(2, 3)   )
			self.assertEqual(tensor_batch['''labels'''].shape							,			(2,)   )
			# test converting the converted
			with CaptureStderr() as cs:
				__snake_case      :		Optional[Any]         =							batch.convert_to_tensors(tensor_type='''jax'''   )
			self.assertFalse(len(cs.err   )							,			msg=f"""should have no warning, but got {cs.err}"""   )

			__snake_case      :		List[str]         =							BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}   )
			__snake_case      :		Union[str, Any]         =							batch.convert_to_tensors(tensor_type='''jax'''							,			prepend_batch_axis=a_   )
			self.assertEqual(tensor_batch['''inputs'''].shape							,			(1, 3)   )
			self.assertEqual(tensor_batch['''labels'''].shape							,			(1,)   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							[{'''input_ids''': np.array([0, 1, 2]   )}, {'''input_ids''': np.array([0, 1, 2, 3]   )}]
			__snake_case      :		Union[str, Any]         =							BertTokenizer.from_pretrained('''bert-base-cased'''   )

			__snake_case      :		Dict         =							tokenizer.pad(a_							,			padding=a_   )
			self.assertTrue(isinstance(batch['''input_ids''']							,			np.ndarray   )   )
			self.assertEqual(batch['''input_ids'''].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
			__snake_case      :		Union[str, Any]         =							tokenizer.pad(a_							,			padding=a_							,			return_tensors='''np'''   )
			self.assertTrue(isinstance(batch['''input_ids''']							,			np.ndarray   )   )
			self.assertEqual(batch['''input_ids'''].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )



		@require_torch
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			import torch

			__snake_case      :		Tuple         =							[{'''input_ids''': torch.tensor([0, 1, 2]   )}, {'''input_ids''': torch.tensor([0, 1, 2, 3]   )}]
			__snake_case      :		Dict         =							BertTokenizer.from_pretrained('''bert-base-cased'''   )

			__snake_case      :		Any         =							tokenizer.pad(a_							,			padding=a_   )
			self.assertTrue(isinstance(batch['''input_ids''']							,			torch.Tensor   )   )
			self.assertEqual(batch['''input_ids'''].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
			__snake_case      :		int         =							tokenizer.pad(a_							,			padding=a_							,			return_tensors='''pt'''   )
			self.assertTrue(isinstance(batch['''input_ids''']							,			torch.Tensor   )   )
			self.assertEqual(batch['''input_ids'''].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )



		@require_tf
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			import tensorflow as tf

			__snake_case      :		Optional[Any]         =							[{'''input_ids''': tf.constant([0, 1, 2]   )}, {'''input_ids''': tf.constant([0, 1, 2, 3]   )}]
			__snake_case      :		Any         =							BertTokenizer.from_pretrained('''bert-base-cased'''   )

			__snake_case      :		Optional[Any]         =							tokenizer.pad(a_							,			padding=a_   )
			self.assertTrue(isinstance(batch['''input_ids''']							,			tf.Tensor   )   )
			self.assertEqual(batch['''input_ids'''].numpy().tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
			__snake_case      :		List[str]         =							tokenizer.pad(a_							,			padding=a_							,			return_tensors='''tf'''   )
			self.assertTrue(isinstance(batch['''input_ids''']							,			tf.Tensor   )   )
			self.assertEqual(batch['''input_ids'''].numpy().tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )



		@require_tokenizers
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Tuple         =							Tokenizer(WordPiece(unk_token='''[UNK]'''   )   )
			PreTrainedTokenizerFast(tokenizer_object=a_   )



		@require_tokenizers
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							Tokenizer(WordPiece(unk_token='''[UNK]'''   )   )
			with tempfile.TemporaryDirectory() as tmpdirname:
				bert_tokenizer.save(os.path.join(a_							,			'''tokenizer.json'''   )   )
				PreTrainedTokenizerFast(tokenizer_file=os.path.join(a_							,			'''tokenizer.json'''   )   )










"""simple docstring"""


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_								=			{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

snake_case_								=			{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

snake_case_								=			{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask""", """token_type_ids"""]
						__UpperCamelCase													=				DebertaTokenizer






						def __init__(   self					:Optional[Any]  ,    lowercase_					:List[str]=None  ,    lowercase_					:List[str]=None  ,    lowercase_					:List[Any]=None  ,    lowercase_					:Optional[Any]="replace"  ,    lowercase_					:int="[CLS]"  ,    lowercase_					:Union[str, Any]="[SEP]"  ,    lowercase_					:int="[SEP]"  ,    lowercase_					:int="[CLS]"  ,    lowercase_					:Optional[int]="[UNK]"  ,    lowercase_					:int="[PAD]"  ,    lowercase_					:List[Any]="[MASK]"  ,    lowercase_					:Union[str, Any]=False  ,    **lowercase_					:int  ,    )							->     Tuple:
												super().__init__(
												    lowercase_  ,    lowercase_  ,    tokenizer_file=lowercase_  ,    errors=lowercase_  ,    bos_token=lowercase_  ,    eos_token=lowercase_  ,    unk_token=lowercase_  ,    sep_token=lowercase_  ,    cls_token=lowercase_  ,    pad_token=lowercase_  ,    mask_token=lowercase_  ,    add_prefix_space=lowercase_  ,    **lowercase_  ,    )
												UpperCAmelCase			 =					kwargs.pop('add_bos_token'  ,    lowercase_   )

												UpperCAmelCase			 =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
												if pre_tok_state.get('add_prefix_space'  ,    lowercase_   ) != add_prefix_space:
																		UpperCAmelCase			 =					getattr(lowercase_  ,    pre_tok_state.pop('type'   )   )
																		UpperCAmelCase			 =					add_prefix_space
																		UpperCAmelCase			 =					pre_tok_class(**lowercase_   )

												UpperCAmelCase			 =					add_prefix_space






						@property
						def       UpperCAmelCase__	(   self					:str   )							->     str:
												if self._mask_token is None:
																		if self.verbose:
																								logger.error('Using mask_token, but it is not set yet.'   )
																		return None
												return str(self._mask_token   )






						@mask_token.setter
						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Any   )							->     Any:
												UpperCAmelCase			 =					AddedToken(lowercase_  ,    lstrip=lowercase_  ,    rstrip=lowercase_   ) if isinstance(lowercase_  ,    lowercase_   ) else value
												UpperCAmelCase			 =					value






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												UpperCAmelCase			 =					[self.cls_token_id]
												UpperCAmelCase			 =					[self.sep_token_id]
												return cls + token_ids_a + sep + token_ids_a + sep






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.sep_token_id]
												UpperCAmelCase			 =					[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   ) * [0] + len(token_ids_a + sep   ) * [1]






						def       UpperCAmelCase__	(   self					:int  ,    *lowercase_					:str  ,    **lowercase_					:Optional[Any]   )							->     BatchEncoding:
												UpperCAmelCase			 =					kwargs.get('is_split_into_words'  ,    lowercase_   )
												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    *lowercase_					:Dict  ,    **lowercase_					:Any   )							->     BatchEncoding:
												UpperCAmelCase			 =					kwargs.get('is_split_into_words'  ,    lowercase_   )

												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:"Conversation"   )							->     List[int]:
												UpperCAmelCase			 =					[]
												for is_user, text in conversation.iter_texts():
																		input_ids.extend(self.encode(lowercase_  ,    add_special_tokens=lowercase_   ) + [self.eos_token_id]   )

												if len(lowercase_   ) > self.model_max_length:
																		UpperCAmelCase			 =					input_ids[-self.model_max_length :]
												return input_ids





