




import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RwkvForCausalLM,
						    RwkvModel,
						)
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
						__a  :		Optional[int]		     =   False







class 				UpperCAmelCase:



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase	,				lowerCamelCase=14	,				lowerCamelCase=7	,				lowerCamelCase=True	,				lowerCamelCase=False	,				lowerCamelCase=True	,				lowerCamelCase=True	,				lowerCamelCase=True	,				lowerCamelCase=99	,				lowerCamelCase=32	,				lowerCamelCase=5	,				lowerCamelCase=37	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=16	,				lowerCamelCase=2	,				lowerCamelCase=3	,				lowerCamelCase=4	,				lowerCamelCase=None	,				) ->      Any:





								"""simple docstring"""


								lowercase__  :	str        =							parent
								lowercase__  :	int        =							batch_size
								lowercase__  :	Tuple        =							seq_length
								lowercase__  :	Tuple        =							is_training
								lowercase__  :	str        =							use_token_type_ids
								lowercase__  :	Tuple        =							use_input_mask
								lowercase__  :	str        =							use_labels
								lowercase__  :	List[Any]        =							use_mc_token_ids
								lowercase__  :	Tuple        =							vocab_size
								lowercase__  :	List[str]        =							hidden_size
								lowercase__  :	Optional[Any]        =							num_hidden_layers
								lowercase__  :	Any        =							intermediate_size
								lowercase__  :	Dict        =							hidden_act
								lowercase__  :	Dict        =							hidden_dropout_prob
								lowercase__  :	Union[str, Any]        =							attention_probs_dropout_prob
								lowercase__  :	Optional[Any]        =							max_position_embeddings
								lowercase__  :	Optional[Any]        =							type_vocab_size
								lowercase__  :	Optional[int]        =							type_sequence_label_size
								lowercase__  :	List[str]        =							num_labels
								lowercase__  :	Optional[int]        =							num_choices
								lowercase__  :	List[str]        =							scope
								lowercase__  :	Tuple        =							vocab_size - 1
								lowercase__  :	Union[str, Any]        =							vocab_size - 1
								lowercase__  :	Optional[Any]        =							vocab_size - 1

	def    __a     (					self    ) ->      Optional[Any]:





								"""simple docstring"""


								return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"    )

	def    __a     (					self	,				lowerCamelCase=False	,				lowerCamelCase=False	,				lowerCamelCase=False    ) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size    )

								lowercase__  :	int        =							None
								if self.use_input_mask:
															lowercase__  :	Tuple        =							random_attention_mask([self.batch_size, self.seq_length]    )

								lowercase__  :	Any        =							None
								if self.use_token_type_ids:
															lowercase__  :	Tuple        =							ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size    )

								lowercase__  :	Optional[int]        =							None
								if self.use_mc_token_ids:
															lowercase__  :	Tuple        =							ids_tensor([self.batch_size, self.num_choices]	,				self.seq_length    )

								lowercase__  :	List[str]        =							None
								lowercase__  :	str        =							None
								lowercase__  :	Union[str, Any]        =							None
								if self.use_labels:
															lowercase__  :	Any        =							ids_tensor([self.batch_size]	,				self.type_sequence_label_size    )
															lowercase__  :	Any        =							ids_tensor([self.batch_size, self.seq_length]	,				self.num_labels    )
															lowercase__  :	Union[str, Any]        =							ids_tensor([self.batch_size]	,				self.num_choices    )

								lowercase__  :	Dict        =							self.get_config(
								    gradient_checkpointing=lowerCamelCase	,				scale_attn_by_inverse_layer_idx=lowerCamelCase	,				reorder_and_upcast_attn=lowerCamelCase	,				)

								return (
								    config,
								    input_ids,
								    input_mask,
								    None,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)

	def    __a     (					self	,				lowerCamelCase=False	,				lowerCamelCase=False	,				lowerCamelCase=False    ) ->      Union[str, Any]:





								"""simple docstring"""


								return RwkvConfig(
								    vocab_size=self.vocab_size	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				intermediate_size=self.intermediate_size	,				activation_function=self.hidden_act	,				resid_pdrop=self.hidden_dropout_prob	,				attn_pdrop=self.attention_probs_dropout_prob	,				n_positions=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				use_cache=lowerCamelCase	,				bos_token_id=self.bos_token_id	,				eos_token_id=self.eos_token_id	,				pad_token_id=self.pad_token_id	,				gradient_checkpointing=lowerCamelCase	,				scale_attn_by_inverse_layer_idx=lowerCamelCase	,				reorder_and_upcast_attn=lowerCamelCase	,				)

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							self.get_config()
								lowercase__  :	Union[str, Any]        =							300
								return config

	def    __a     (					self    ) ->      Union[str, Any]:





								"""simple docstring"""


								(
								    (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       
								)  :	Tuple        =							self.prepare_config_and_inputs()

								lowercase__  :	Any        =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
								lowercase__  :	List[str]        =							ids_tensor([self.batch_size, self.seq_length]	,				vocab_size=2    )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				*lowerCamelCase    ) ->      Any:





								"""simple docstring"""


								lowercase__  :	str        =							True
								lowercase__  :	str        =							RwkvModel(config=lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()

								lowercase__  :	int        =							model(lowerCamelCase    )

								self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)    )
								self.parent.assertEqual(len(result.hidden_states    )	,				config.num_hidden_layers + 1    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				*lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__  :	str        =							RwkvForCausalLM(lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()

								lowercase__  :	Optional[int]        =							model(lowerCamelCase	,				labels=lowerCamelCase    )
								self.parent.assertEqual(result.loss.shape	,				()    )
								self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				*lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__  :	int        =							RwkvModel(config=lowerCamelCase    )
								model.to(lowerCamelCase    )
								model.eval()

								lowercase__  :	Any        =							model(lowerCamelCase    )
								lowercase__  :	Any        =							outputs.last_hidden_state

								lowercase__  :	int        =							model(input_ids[:, :2]    )
								lowercase__  :	List[Any]        =							outputs.last_hidden_state

								# Using the state computed on the first inputs, we will get the same output
								lowercase__  :	Tuple        =							model(input_ids[:, 2:]	,				state=outputs.state    )
								lowercase__  :	Dict        =							outputs.last_hidden_state

								self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]	,				dim=1    )	,				lowerCamelCase	,				atol=1E-5    )    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				*lowerCamelCase	,				lowerCamelCase=False    ) ->      Union[str, Any]:





								"""simple docstring"""


								lowercase__  :	List[str]        =							RwkvForCausalLM(lowerCamelCase    )
								model.to(lowerCamelCase    )
								if gradient_checkpointing:
															model.gradient_checkpointing_enable()

								lowercase__  :	Dict        =							model(lowerCamelCase	,				labels=lowerCamelCase    )
								self.parent.assertEqual(result.loss.shape	,				()    )
								self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)    )
								result.loss.backward()







	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Any        =							self.prepare_config_and_inputs()

								(
								    (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       (
								    lowercase__
								) ,       
								)  :	Tuple        =							config_and_inputs

								lowercase__  :	Any        =							{"input_ids": input_ids}

								return config, inputs_dict







@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0   ,     reason="""See https://github.com/huggingface/transformers/pull/24204"""					)
@require_torch
class 				UpperCAmelCase(					snake_case_   ,     snake_case_   ,     snake_case_   ,     unittest.TestCase					):



	"""simple docstring"""






	a     : List[str]	  =					(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
	a     : Dict	  =					(
	    {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
	)
	# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
	a     : Dict	  =					False
	a     : Dict	  =					False
	a     : Optional[int]	  =					False
	a     : Union[str, Any]	  =					False
	a     : Union[str, Any]	  =					False  # Rwkv does not support head masking

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	Tuple        =							RwkvModelTester(self    )
								lowercase__  :	int        =							ConfigTester(
								    self	,				config_class=lowerCamelCase	,				n_embd=37	,				common_properties=["hidden_size", "num_hidden_layers"]    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase=None    ) ->      Any:





								"""simple docstring"""


								if isinstance(lowerCamelCase	,				torch.Tensor    ):
															lowercase__ ,       lowercase__  :	Optional[int]        =							member.max().item(), member.min().item()
								elif isinstance(lowerCamelCase	,				lowerCamelCase    ) or isinstance(lowerCamelCase	,				lowerCamelCase    ):
															lowercase__ ,       lowercase__  :	Optional[int]        =							max(lowerCamelCase    ), min(lowerCamelCase    )

								if not isinstance(lowerCamelCase	,				lowerCamelCase    ):
															raise TypeError("container should be a list or tuple"    )
								elif len(lowerCamelCase    ) != 2:
															raise ValueError("container should have 2 elements"    )

								lowercase__ ,       lowercase__  :	Union[str, Any]        =							container

								lowercase__  :	int        =							(min_value >= expected_min) and (max_value <= expected_max)

								if not is_inside_interval:
															lowercase__  :	Union[str, Any]        =							"%s not found in %s" % (safe_repr(lowerCamelCase    ), safe_repr(lowerCamelCase    ))
															self.fail(self._formatMessage(lowerCamelCase	,				lowerCamelCase    )    )

	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								self.config_tester.run_common_tests()

	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								lowercase__  :	str        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_rwkv_model(*lowerCamelCase    )

	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								lowercase__  :	List[Any]        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_causl_lm(*lowerCamelCase    )

	def    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


								lowercase__  :	List[Any]        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_state_equivalency(*lowerCamelCase    )

	def    __a     (					self    ) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__ ,       lowercase__  :	Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowercase__  :	List[str]        =							model_class(config=lowerCamelCase    )
															for name, param in model.named_parameters():
																						if "time_decay" in name:
																													if param.requires_grad:
																																				self.assertTrue(param.data.max().item() == 3.0    )
																																				self.assertTrue(param.data.min().item() == -5.0    )
																						elif "time_first" in name:
																													if param.requires_grad:
																																				# check if it's a ones like
																																				self.assertTrue(torch.allclose(param.data	,				torch.ones_like(param.data    )	,				atol=1E-5	,				rtol=1E-5    )    )
																						elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]    ):
																													if param.requires_grad:
																																				self.assertInterval(
																																				    param.data	,				[0.0, 1.0]	,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""	,				)
																						elif "time_mix_value" in name:
																													if param.requires_grad:
																																				self.assertInterval(
																																				    param.data	,				[0.0, 1.3]	,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""	,				)

	def    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


								lowercase__ ,       lowercase__  :	Union[str, Any]        =							self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__  :	Dict        =							True

								lowercase__  :	Union[str, Any]        =							getattr(self.model_tester	,				"seq_length"	,				lowerCamelCase    )

								for model_class in self.all_model_classes:
															lowercase__  :	Any        =							True
															lowercase__  :	Optional[Any]        =							False
															lowercase__  :	Any        =							True
															lowercase__  :	List[Any]        =							model_class(lowerCamelCase    )
															model.to(lowerCamelCase    )
															model.eval()

															lowercase__  :	int        =							self._prepare_for_class(lowerCamelCase	,				lowerCamelCase    )
															lowercase__  :	str        =							inputs["input_ids"].shape[0]
															with torch.no_grad():
																						lowercase__  :	Union[str, Any]        =							model(**lowerCamelCase    )
															lowercase__  :	Dict        =							outputs.attentions
															self.assertEqual(len(lowerCamelCase    )	,				self.model_tester.num_hidden_layers    )

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															lowercase__  :	str        =							True
															lowercase__  :	Any        =							model_class(lowerCamelCase    )
															model.to(lowerCamelCase    )
															model.eval()

															lowercase__  :	Optional[Any]        =							self._prepare_for_class(lowerCamelCase	,				lowerCamelCase    )
															lowercase__  :	Union[str, Any]        =							inputs["input_ids"].shape[0]
															with torch.no_grad():
																						lowercase__  :	Union[str, Any]        =							model(**lowerCamelCase    )
															lowercase__  :	str        =							outputs.attentions
															self.assertEqual(len(lowerCamelCase    )	,				self.model_tester.num_hidden_layers    )

															self.assertListEqual(
															    list(attentions[0].shape[-3:]    )	,				[batch_size, seq_len, config.hidden_size]	,				)
															lowercase__  :	str        =							len(lowerCamelCase    )

															# Check attention is always last and order is fine
															lowercase__  :	List[Any]        =							True
															lowercase__  :	Union[str, Any]        =							True
															lowercase__  :	Optional[Any]        =							model_class(lowerCamelCase    )
															model.to(lowerCamelCase    )
															model.eval()

															lowercase__  :	Tuple        =							self._prepare_for_class(lowerCamelCase	,				lowerCamelCase    )
															lowercase__  :	List[str]        =							inputs["input_ids"].shape[0]
															with torch.no_grad():
																						lowercase__  :	Dict        =							model(**lowerCamelCase    )

															lowercase__  :	List[Any]        =							1
															self.assertEqual(out_len + added_hidden_states	,				len(lowerCamelCase    )    )

															lowercase__  :	Optional[int]        =							outputs.attentions

															self.assertEqual(len(lowerCamelCase    )	,				self.model_tester.num_hidden_layers    )
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]    )	,				[batch_size, seq_len, config.hidden_size]	,				)







	@slow
	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowercase__  :	Optional[Any]        =							RwkvModel.from_pretrained(lowerCamelCase    )
															self.assertIsNotNone(lowerCamelCase    )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0   ,     reason="""See https://github.com/huggingface/transformers/pull/24204"""					)
@slow
class 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	List[str]        =							"RWKV/rwkv-4-169m-pile"
								lowercase__  :	Any        =							AutoTokenizer.from_pretrained(self.model_id    )

	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								lowercase__  :	str        =							"Hello my name is Jasmine and I am a newbie to the"
								lowercase__  :	str        =							RwkvForCausalLM.from_pretrained(self.model_id    ).to(lowerCamelCase    )

								lowercase__  :	Tuple        =							self.tokenizer("Hello my name is"	,				return_tensors="pt"    ).input_ids.to(lowerCamelCase    )
								lowercase__  :	int        =							model.generate(lowerCamelCase	,				max_new_tokens=10    )
								lowercase__  :	Any        =							self.tokenizer.decode(output[0].tolist()    )

								self.assertEqual(lowerCamelCase	,				lowerCamelCase    )







	def    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


								lowercase__  :	Dict        =							"Hello my name is Jasmine and I am a newbie to the"

								lowercase__  :	Any        =							self.tokenizer("Hello my name is"	,				return_tensors="pt"    ).input_ids.to(lowerCamelCase    )
								lowercase__  :	Tuple        =							RwkvForCausalLM.from_pretrained(self.model_id	,				torch_dtype=torch.bfloataa    ).to(lowerCamelCase    )

								lowercase__  :	Optional[int]        =							model.generate(lowerCamelCase	,				max_new_tokens=10    )
								lowercase__  :	str        =							self.tokenizer.decode(output[0].tolist()    )

								self.assertEqual(lowerCamelCase	,				lowerCamelCase    )





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

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


if is_sentencepiece_available():
						from .tokenization_deberta_va import DebertaVaTokenizer
else:
						__a  :		Optional[Any]		     =   None

__a  :		List[str]		     =   logging.get_logger(__name__)

__a  :		Dict		     =   {'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

__a  :		int		     =   {
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

__a  :		Any		     =   {
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

__a  :		Union[str, Any]		     =   {
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : List[Any]	  =					VOCAB_FILES_NAMES
	a     : Optional[int]	  =					PRETRAINED_VOCAB_FILES_MAP
	a     : int	  =					PRETRAINED_INIT_CONFIGURATION
	a     : List[str]	  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     : int	  =					DebertaVaTokenizer

	def __init__(					self	,				lowerCamelCase=None	,				lowerCamelCase=None	,				lowerCamelCase=False	,				lowerCamelCase=False	,				lowerCamelCase="[CLS]"	,				lowerCamelCase="[SEP]"	,				lowerCamelCase="[UNK]"	,				lowerCamelCase="[SEP]"	,				lowerCamelCase="[PAD]"	,				lowerCamelCase="[CLS]"	,				lowerCamelCase="[MASK]"	,				**lowerCamelCase	,				) ->      None:





								"""simple docstring"""


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

								lowercase__  :	Optional[int]        =							do_lower_case
								lowercase__  :	Optional[Any]        =							split_by_punct
								lowercase__  :	Tuple        =							vocab_file
								lowercase__  :	Optional[int]        =							False if not self.vocab_file else True

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase=None    ) ->      List[Any]:





								"""simple docstring"""


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

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase=None	,				lowerCamelCase=False    ) ->      Union[str, Any]:





								"""simple docstring"""


								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCamelCase	,				token_ids_a=lowerCamelCase	,				already_has_special_tokens=lowerCamelCase    )

								if token_ids_a is not None:
															return [1] + ([0] * len(lowerCamelCase    )) + [1] + ([0] * len(lowerCamelCase    )) + [1]
								return [1] + ([0] * len(lowerCamelCase    )) + [1]

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase=None    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Any        =							[self.sep_token_id]
								lowercase__  :	List[str]        =							[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    __a     (					self	,				lowerCamelCase	,				lowerCamelCase = 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__  :	int        =							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,)