





import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    GPTNeoXForCausalLM,
					    GPTNeoXForQuestionAnswering,
					    GPTNeoXForSequenceClassification,
					    GPTNeoXForTokenClassification,
					    GPTNeoXModel,
					)

class 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=13	,			__lowerCAmelCase=7	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=99	,			__lowerCAmelCase=64	,			__lowerCAmelCase=5	,			__lowerCAmelCase=4	,			__lowerCAmelCase=37	,			__lowerCAmelCase="gelu"	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=512	,			__lowerCAmelCase=16	,			__lowerCAmelCase=2	,			__lowerCAmelCase=0.02	,			__lowerCAmelCase=3	,			__lowerCAmelCase=4	,			__lowerCAmelCase=None	,			):
														UpperCamelCase__					  =      parent
														UpperCamelCase__					  =      batch_size
														UpperCamelCase__					  =      seq_length
														UpperCamelCase__					  =      is_training
														UpperCamelCase__					  =      use_input_mask
														UpperCamelCase__					  =      use_token_type_ids
														UpperCamelCase__					  =      use_labels
														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      hidden_size
														UpperCamelCase__					  =      num_hidden_layers
														UpperCamelCase__					  =      num_attention_heads
														UpperCamelCase__					  =      intermediate_size
														UpperCamelCase__					  =      hidden_act
														UpperCamelCase__					  =      hidden_dropout_prob
														UpperCamelCase__					  =      attention_probs_dropout_prob
														UpperCamelCase__					  =      max_position_embeddings
														UpperCamelCase__					  =      type_vocab_size
														UpperCamelCase__					  =      type_sequence_label_size
														UpperCamelCase__					  =      initializer_range
														UpperCamelCase__					  =      num_labels
														UpperCamelCase__					  =      num_choices
														UpperCamelCase__					  =      scope
														UpperCamelCase__					  =      vocab_size - 1






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size	)

														UpperCamelCase__					  =      None
														if self.use_input_mask:
																					UpperCamelCase__					  =      random_attention_mask([self.batch_size, self.seq_length]	)

														UpperCamelCase__					  =      None
														if self.use_labels:
																					UpperCamelCase__					  =      ids_tensor([self.batch_size, self.seq_length]	,			self.num_labels	)

														UpperCamelCase__					  =      self.get_config()

														return config, input_ids, input_mask, token_labels






							def 					_lowerCamelCase		(    self	):
														return GPTNeoXConfig(
														    vocab_size=self.vocab_size	,			hidden_size=self.hidden_size	,			num_hidden_layers=self.num_hidden_layers	,			num_attention_heads=self.num_attention_heads	,			intermediate_size=self.intermediate_size	,			hidden_act=self.hidden_act	,			hidden_dropout_prob=self.hidden_dropout_prob	,			attention_probs_dropout_prob=self.attention_probs_dropout_prob	,			max_position_embeddings=self.max_position_embeddings	,			type_vocab_size=self.type_vocab_size	,			is_decoder=__lowerCAmelCase	,			initializer_range=self.initializer_range	,			pad_token_id=self.pad_token_id	,			)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      self.prepare_config_and_inputs()

														UpperCamelCase__					  =      True

														return config, input_ids, input_mask, token_labels






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      GPTNeoXModel(config=__lowerCAmelCase	)
														model.to(__lowerCAmelCase	)
														model.eval()
														UpperCamelCase__					  =      model(__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	)
														UpperCamelCase__					  =      model(__lowerCAmelCase	)
														self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      GPTNeoXModel(__lowerCAmelCase	)
														model.to(__lowerCAmelCase	)
														model.eval()
														UpperCamelCase__					  =      model(__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	)
														self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      GPTNeoXForCausalLM(config=__lowerCAmelCase	)
														model.to(__lowerCAmelCase	)
														model.eval()
														UpperCamelCase__					  =      model(__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			labels=__lowerCAmelCase	)
														self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.vocab_size)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      self.num_labels
														UpperCamelCase__					  =      GPTNeoXForQuestionAnswering(__lowerCAmelCase	)
														model.to(__lowerCAmelCase	)
														model.eval()
														UpperCamelCase__					  =      model(__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	)
														self.parent.assertEqual(result.start_logits.shape	,			(self.batch_size, self.seq_length)	)
														self.parent.assertEqual(result.end_logits.shape	,			(self.batch_size, self.seq_length)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      self.num_labels
														UpperCamelCase__					  =      GPTNeoXForSequenceClassification(__lowerCAmelCase	)
														model.to(__lowerCAmelCase	)
														model.eval()
														UpperCamelCase__					  =      ids_tensor([self.batch_size]	,			self.type_sequence_label_size	)
														UpperCamelCase__					  =      model(__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			labels=__lowerCAmelCase	)
														self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      self.num_labels
														UpperCamelCase__					  =      GPTNeoXForTokenClassification(__lowerCAmelCase	)
														model.to(__lowerCAmelCase	)
														model.eval()
														UpperCamelCase__					  =      model(__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			labels=__lowerCAmelCase	)
														self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.num_labels)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      GPTNeoXForCausalLM(config=__lowerCAmelCase	)
														model.to(__lowerCAmelCase	)
														model.eval()

														# first forward pass
														UpperCamelCase__					  =      model(__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			use_cache=__lowerCAmelCase	)
														UpperCamelCase__					  =      outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														UpperCamelCase__					  =      ids_tensor((self.batch_size, 3)	,			config.vocab_size	)
														UpperCamelCase__					  =      ids_tensor((self.batch_size, 3)	,			vocab_size=2	)

														# append to next input_ids and
														UpperCamelCase__					  =      torch.cat([input_ids, next_tokens]	,			dim=-1	)
														UpperCamelCase__					  =      torch.cat([input_mask, next_mask]	,			dim=-1	)

														UpperCamelCase__					  =      model(__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			output_hidden_states=__lowerCAmelCase	)
														UpperCamelCase__					  =      output_from_no_past["""hidden_states"""][0]
														UpperCamelCase__					  =      model(
														    __lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			past_key_values=__lowerCAmelCase	,			output_hidden_states=__lowerCAmelCase	,			)["""hidden_states"""][0]

														# select random slice
														UpperCamelCase__					  =      ids_tensor((1,)	,			output_from_past.shape[-1]	).item()
														UpperCamelCase__					  =      output_from_no_past[:, -3:, random_slice_idx].detach()
														UpperCamelCase__					  =      output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-3	)	)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.prepare_config_and_inputs()
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      config_and_inputs
														UpperCamelCase__					  =      {"""input_ids""": input_ids, """attention_mask""": input_mask}
														return config, inputs_dict






@require_torch
class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		_a ,		unittest.TestCase							):
							snake_case  :   Union[str, Any]      =							(
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							snake_case  :   int      =							(GPTNeoXForCausalLM,) if is_torch_available() else ()
							snake_case  :   Any      =							(
							    {
							        """feature-extraction""": GPTNeoXModel,
							        """question-answering""": GPTNeoXForQuestionAnswering,
							        """text-classification""": GPTNeoXForSequenceClassification,
							        """text-generation""": GPTNeoXForCausalLM,
							        """token-classification""": GPTNeoXForTokenClassification,
							        """zero-shot""": GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							snake_case  :   List[Any]      =							False
							snake_case  :   str      =							False
							snake_case  :   Any      =							False
							snake_case  :   Union[str, Any]      =							False






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      GPTNeoXModelTester(self	)
														UpperCamelCase__					  =      ConfigTester(self	,			config_class=__lowerCAmelCase	,			hidden_size=64	,			num_attention_heads=8	)






							def 					_lowerCamelCase		(    self	):
														self.config_tester.run_common_tests()






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														# This regression test was failing with PyTorch < 1.3
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_decoder()

														UpperCamelCase__					  =      None

														self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_causal_lm(*__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase	)






							@unittest.skip(reason="""Feed forward chunking is not implemented"""	)
							def 					_lowerCamelCase		(    self	):
														pass







							@parameterized.expand([("""linear""",), ("""dynamic""",)]	)
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase__					  =      ids_tensor([1, 10]	,			config.vocab_size	)
														UpperCamelCase__					  =      ids_tensor([1, int(config.max_position_embeddings * 1.5	)]	,			config.vocab_size	)

														set_seed(42	)  # Fixed seed at init time so the two models get the same random weights
														UpperCamelCase__					  =      GPTNeoXModel(__lowerCAmelCase	)
														original_model.to(__lowerCAmelCase	)
														original_model.eval()
														UpperCamelCase__					  =      original_model(__lowerCAmelCase	).last_hidden_state
														UpperCamelCase__					  =      original_model(__lowerCAmelCase	).last_hidden_state

														set_seed(42	)  # Fixed seed at init time so the two models get the same random weights
														UpperCamelCase__					  =      {"""type""": scaling_type, """factor""": 10.0}
														UpperCamelCase__					  =      GPTNeoXModel(__lowerCAmelCase	)
														scaled_model.to(__lowerCAmelCase	)
														scaled_model.eval()
														UpperCamelCase__					  =      scaled_model(__lowerCAmelCase	).last_hidden_state
														UpperCamelCase__					  =      scaled_model(__lowerCAmelCase	).last_hidden_state

														# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
														# maximum sequence length, so the outputs for the short input should match.
														if scaling_type == "dynamic":
																					self.assertTrue(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-5	)	)
														else:
																					self.assertFalse(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-5	)	)

														# The output should be different for long inputs
														self.assertFalse(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-5	)	)






@require_torch
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""	)
														for checkpointing in [True, False]:
																					UpperCamelCase__					  =      GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""	)

																					if checkpointing:
																												model.gradient_checkpointing_enable()
																					else:
																												model.gradient_checkpointing_disable()
																					model.to(__lowerCAmelCase	)

																					UpperCamelCase__					  =      tokenizer("""My favorite food is"""	,			return_tensors="""pt"""	).to(__lowerCAmelCase	)
																					# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
																					# See: https://github.com/huggingface/transformers/pull/24193
																					UpperCamelCase__					  =      """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

																					UpperCamelCase__					  =      model.generate(**__lowerCAmelCase	,			do_sample=__lowerCAmelCase	,			max_new_tokens=20	)
																					UpperCamelCase__					  =      tokenizer.batch_decode(__lowerCAmelCase	)[0]

																					self.assertEqual(__lowerCAmelCase	,			__lowerCAmelCase	)











import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


UpperCamelCase__           =			logging.get_logger(__name__)

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

UpperCamelCase__           =			{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__           =			{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							VOCAB_FILES_NAMES
							snake_case  :   Optional[Any]      =							PRETRAINED_VOCAB_FILES_MAP
							snake_case  :   Any      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case  :   Dict      =							["""input_ids""", """attention_mask"""]
							snake_case  :   List[Any]      =							RobertaTokenizer






							def __init__(    self	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase="replace"	,			__lowerCAmelCase="<s>"	,			__lowerCAmelCase="</s>"	,			__lowerCAmelCase="</s>"	,			__lowerCAmelCase="<s>"	,			__lowerCAmelCase="<unk>"	,			__lowerCAmelCase="<pad>"	,			__lowerCAmelCase="<mask>"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			**__lowerCAmelCase	,			):
														super().__init__(
														    __lowerCAmelCase	,			__lowerCAmelCase	,			tokenizer_file=__lowerCAmelCase	,			errors=__lowerCAmelCase	,			bos_token=__lowerCAmelCase	,			eos_token=__lowerCAmelCase	,			sep_token=__lowerCAmelCase	,			cls_token=__lowerCAmelCase	,			unk_token=__lowerCAmelCase	,			pad_token=__lowerCAmelCase	,			mask_token=__lowerCAmelCase	,			add_prefix_space=__lowerCAmelCase	,			trim_offsets=__lowerCAmelCase	,			**__lowerCAmelCase	,			)

														UpperCamelCase__					  =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
														if pre_tok_state.get("""add_prefix_space"""	,			__lowerCAmelCase	) != add_prefix_space:
																					UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			pre_tok_state.pop("""type"""	)	)
																					UpperCamelCase__					  =      add_prefix_space
																					UpperCamelCase__					  =      pre_tok_class(**__lowerCAmelCase	)

														UpperCamelCase__					  =      add_prefix_space

														UpperCamelCase__					  =      """post_processor"""
														UpperCamelCase__					  =      getattr(self.backend_tokenizer	,			__lowerCAmelCase	,			__lowerCAmelCase	)
														if tokenizer_component_instance:
																					UpperCamelCase__					  =      json.loads(tokenizer_component_instance.__getstate__()	)

																					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																					if "sep" in state:
																												UpperCamelCase__					  =      tuple(state["""sep"""]	)
																					if "cls" in state:
																												UpperCamelCase__					  =      tuple(state["""cls"""]	)

																					UpperCamelCase__					  =      False

																					if state.get("""add_prefix_space"""	,			__lowerCAmelCase	) != add_prefix_space:
																												UpperCamelCase__					  =      add_prefix_space
																												UpperCamelCase__					  =      True

																					if state.get("""trim_offsets"""	,			__lowerCAmelCase	) != trim_offsets:
																												UpperCamelCase__					  =      trim_offsets
																												UpperCamelCase__					  =      True

																					if changes_to_apply:
																												UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			state.pop("""type"""	)	)
																												UpperCamelCase__					  =      component_class(**__lowerCAmelCase	)
																												setattr(self.backend_tokenizer	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							@property
							def 					_lowerCamelCase		(    self	):
														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 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else value
														UpperCamelCase__					  =      value






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      kwargs.get("""is_split_into_words"""	,			__lowerCAmelCase	)
														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(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      kwargs.get("""is_split_into_words"""	,			__lowerCAmelCase	)

														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(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      self._tokenizer.model.save(__lowerCAmelCase	,			name=__lowerCAmelCase	)
														return tuple(__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	):
														UpperCamelCase__					  =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
														if token_ids_a is None:
																					return output

														return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														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 + sep + token_ids_a + sep	) * [0]




