



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_								=   False



class 	__lowerCAmelCase       :




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



    '''simple docstring'''
    __lowerCamelCase  							=      parent
    __lowerCamelCase  							=      batch_size
    __lowerCamelCase  							=      seq_length
    __lowerCamelCase  							=      is_training
    __lowerCamelCase  							=      use_token_type_ids
    __lowerCamelCase  							=      use_input_mask
    __lowerCamelCase  							=      use_labels
    __lowerCamelCase  							=      use_mc_token_ids
    __lowerCamelCase  							=      vocab_size
    __lowerCamelCase  							=      hidden_size
    __lowerCamelCase  							=      num_hidden_layers
    __lowerCamelCase  							=      intermediate_size
    __lowerCamelCase  							=      hidden_act
    __lowerCamelCase  							=      hidden_dropout_prob
    __lowerCamelCase  							=      attention_probs_dropout_prob
    __lowerCamelCase  							=      max_position_embeddings
    __lowerCamelCase  							=      type_vocab_size
    __lowerCamelCase  							=      type_sequence_label_size
    __lowerCamelCase  							=      num_labels
    __lowerCamelCase  							=      num_choices
    __lowerCamelCase  							=      scope
    __lowerCamelCase  							=      vocab_size - 1
    __lowerCamelCase  							=      vocab_size - 1
    __lowerCamelCase  							=      vocab_size - 1




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b'''			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase=False		,			__UpperCAmelCase=False		,			__UpperCAmelCase=False			):



    '''simple docstring'''
    __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

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

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

    __lowerCamelCase  							=      None
    if self.use_mc_token_ids:
       __lowerCamelCase  							=      ids_tensor([self.batch_size, self.num_choices]		,			self.seq_length			)

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

    __lowerCamelCase  							=      self.get_config(
        gradient_checkpointing=__UpperCAmelCase		,			scale_attn_by_inverse_layer_idx=__UpperCAmelCase		,			reorder_and_upcast_attn=__UpperCAmelCase		,			)

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




 def     lowerCamelCase (  self		,			__UpperCAmelCase=False		,			__UpperCAmelCase=False		,			__UpperCAmelCase=False			):



    '''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=__UpperCAmelCase		,			bos_token_id=self.bos_token_id		,			eos_token_id=self.eos_token_id		,			pad_token_id=self.pad_token_id		,			gradient_checkpointing=__UpperCAmelCase		,			scale_attn_by_inverse_layer_idx=__UpperCAmelCase		,			reorder_and_upcast_attn=__UpperCAmelCase		,			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.get_config()
    __lowerCamelCase  							=      300
    return config




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    (
        __lowerCamelCase			
    )  							=      self.prepare_config_and_inputs()

    __lowerCamelCase  							=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
    __lowerCamelCase  							=      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     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			*__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      True
    __lowerCamelCase  							=      RwkvModel(config=__UpperCAmelCase			)
    model.to(__UpperCAmelCase			)
    model.eval()

    __lowerCamelCase  							=      model(__UpperCAmelCase			)

    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     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			*__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      RwkvForCausalLM(__UpperCAmelCase			)
    model.to(__UpperCAmelCase			)
    model.eval()

    __lowerCamelCase  							=      model(__UpperCAmelCase		,			labels=__UpperCAmelCase			)
    self.parent.assertEqual(result.loss.shape		,			()			)
    self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			*__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      RwkvModel(config=__UpperCAmelCase			)
    model.to(__UpperCAmelCase			)
    model.eval()

    __lowerCamelCase  							=      model(__UpperCAmelCase			)
    __lowerCamelCase  							=      outputs.last_hidden_state

    __lowerCamelCase  							=      model(input_ids[:, :2]			)
    __lowerCamelCase  							=      outputs.last_hidden_state

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

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




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			*__UpperCAmelCase		,			__UpperCAmelCase=False			):



    '''simple docstring'''
    __lowerCamelCase  							=      RwkvForCausalLM(__UpperCAmelCase			)
    model.to(__UpperCAmelCase			)
    if gradient_checkpointing:
       model.gradient_checkpointing_enable()

    __lowerCamelCase  							=      model(__UpperCAmelCase		,			labels=__UpperCAmelCase			)
    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     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.prepare_config_and_inputs()

    (
        __lowerCamelCase			
    )  							=      config_and_inputs

    __lowerCamelCase  							=      {"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 	__lowerCAmelCase       (		lowerCamelCase_					,		lowerCamelCase_					,		lowerCamelCase_					,		unittest.TestCase				):
 lowerCAmelCase__       =					(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
 lowerCAmelCase__       =					(
     {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
 )
 # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
 lowerCAmelCase__       =					False
 lowerCAmelCase__       =					False
 lowerCAmelCase__       =					False
 lowerCAmelCase__       =					False
 lowerCAmelCase__       =					False  # Rwkv does not support head masking




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      RwkvModelTester(self			)
    __lowerCamelCase  							=      ConfigTester(
        self		,			config_class=__UpperCAmelCase		,			n_embd=37		,			common_properties=['''hidden_size''', '''num_hidden_layers''']			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase=None			):



    '''simple docstring'''







    if isinstance(__UpperCAmelCase		,			torch.Tensor			):
       __lowerCamelCase  							=      member.max().item(), member.min().item()
    elif isinstance(__UpperCAmelCase		,			__UpperCAmelCase			) or isinstance(__UpperCAmelCase		,			__UpperCAmelCase			):
       __lowerCamelCase  							=      max(__UpperCAmelCase			), min(__UpperCAmelCase			)

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

    __lowerCamelCase  							=      container

    __lowerCamelCase  							=      (min_value >= expected_min) and (max_value <= expected_max)

    if not is_inside_interval:
       __lowerCamelCase  							=      "%s not found in %s" % (safe_repr(__UpperCAmelCase			), safe_repr(__UpperCAmelCase			))
       self.fail(self._formatMessage(__UpperCAmelCase		,			__UpperCAmelCase			)			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_rwkv_model(*__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_causl_lm(*__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_state_equivalency(*__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowerCamelCase  							=      model_class(config=__UpperCAmelCase			)
       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     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase  							=      True

    __lowerCamelCase  							=      getattr(self.model_tester		,			'''seq_length'''		,			__UpperCAmelCase			)

    for model_class in self.all_model_classes:
       __lowerCamelCase  							=      True
       __lowerCamelCase  							=      False
       __lowerCamelCase  							=      True
       __lowerCamelCase  							=      model_class(__UpperCAmelCase			)
       model.to(__UpperCAmelCase			)
       model.eval()

       __lowerCamelCase  							=      self._prepare_for_class(__UpperCAmelCase		,			__UpperCAmelCase			)
       __lowerCamelCase  							=      inputs["input_ids"].shape[0]
       with torch.no_grad():
          __lowerCamelCase  							=      model(**__UpperCAmelCase			)
       __lowerCamelCase  							=      outputs.attentions
       self.assertEqual(len(__UpperCAmelCase			)		,			self.model_tester.num_hidden_layers			)

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __lowerCamelCase  							=      True
       __lowerCamelCase  							=      model_class(__UpperCAmelCase			)
       model.to(__UpperCAmelCase			)
       model.eval()

       __lowerCamelCase  							=      self._prepare_for_class(__UpperCAmelCase		,			__UpperCAmelCase			)
       __lowerCamelCase  							=      inputs["input_ids"].shape[0]
       with torch.no_grad():
          __lowerCamelCase  							=      model(**__UpperCAmelCase			)
       __lowerCamelCase  							=      outputs.attentions
       self.assertEqual(len(__UpperCAmelCase			)		,			self.model_tester.num_hidden_layers			)

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

       # Check attention is always last and order is fine
       __lowerCamelCase  							=      True
       __lowerCamelCase  							=      True
       __lowerCamelCase  							=      model_class(__UpperCAmelCase			)
       model.to(__UpperCAmelCase			)
       model.eval()

       __lowerCamelCase  							=      self._prepare_for_class(__UpperCAmelCase		,			__UpperCAmelCase			)
       __lowerCamelCase  							=      inputs["input_ids"].shape[0]
       with torch.no_grad():
          __lowerCamelCase  							=      model(**__UpperCAmelCase			)

       __lowerCamelCase  							=      1
       self.assertEqual(out_len + added_hidden_states		,			len(__UpperCAmelCase			)			)

       __lowerCamelCase  							=      outputs.attentions

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




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''







    for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCamelCase  							=      RwkvModel.from_pretrained(__UpperCAmelCase			)
       self.assertIsNotNone(__UpperCAmelCase			)



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




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      "RWKV/rwkv-4-169m-pile"
    __lowerCamelCase  							=      AutoTokenizer.from_pretrained(self.model_id			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      "Hello my name is Jasmine and I am a newbie to the"
    __lowerCamelCase  							=      RwkvForCausalLM.from_pretrained(self.model_id			).to(__UpperCAmelCase			)

    __lowerCamelCase  							=      self.tokenizer('''Hello my name is'''		,			return_tensors='''pt'''			).input_ids.to(__UpperCAmelCase			)
    __lowerCamelCase  							=      model.generate(__UpperCAmelCase		,			max_new_tokens=10			)
    __lowerCamelCase  							=      self.tokenizer.decode(output[0].tolist()			)

    self.assertEqual(__UpperCAmelCase		,			__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      "Hello my name is Jasmine and I am a newbie to the"

    __lowerCamelCase  							=      self.tokenizer('''Hello my name is'''		,			return_tensors='''pt'''			).input_ids.to(__UpperCAmelCase			)
    __lowerCamelCase  							=      RwkvForCausalLM.from_pretrained(self.model_id		,			torch_dtype=torch.bfloataa			).to(__UpperCAmelCase			)

    __lowerCamelCase  							=      model.generate(__UpperCAmelCase		,			max_new_tokens=10			)
    __lowerCamelCase  							=      self.tokenizer.decode(output[0].tolist()			)

    self.assertEqual(__UpperCAmelCase		,			__UpperCAmelCase			)











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_longformer import LongformerTokenizer


a_								=   logging.get_logger(__name__)

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

a_								=   {
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

a_								=   {
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}



class 	__lowerCAmelCase       (		lowerCAmelCase__				):
 lowerCAmelCase__       =					VOCAB_FILES_NAMES
 lowerCAmelCase__       =					PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__       =					["""input_ids""", """attention_mask"""]
 lowerCAmelCase__       =					LongformerTokenizer




 def __init__(  self		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase="replace"		,			__UpperCAmelCase="<s>"		,			__UpperCAmelCase="</s>"		,			__UpperCAmelCase="</s>"		,			__UpperCAmelCase="<s>"		,			__UpperCAmelCase="<unk>"		,			__UpperCAmelCase="<pad>"		,			__UpperCAmelCase="<mask>"		,			__UpperCAmelCase=False		,			__UpperCAmelCase=True		,			**__UpperCAmelCase		,			):



    '''simple docstring'''
    super().__init__(
        __UpperCAmelCase		,			__UpperCAmelCase		,			tokenizer_file=__UpperCAmelCase		,			errors=__UpperCAmelCase		,			bos_token=__UpperCAmelCase		,			eos_token=__UpperCAmelCase		,			sep_token=__UpperCAmelCase		,			cls_token=__UpperCAmelCase		,			unk_token=__UpperCAmelCase		,			pad_token=__UpperCAmelCase		,			mask_token=__UpperCAmelCase		,			add_prefix_space=__UpperCAmelCase		,			trim_offsets=__UpperCAmelCase		,			**__UpperCAmelCase		,			)

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

    __lowerCamelCase  							=      add_prefix_space

    __lowerCamelCase  							=      '''post_processor'''
    __lowerCamelCase  							=      getattr(self.backend_tokenizer		,			__UpperCAmelCase		,			__UpperCAmelCase			)
    if tokenizer_component_instance:
       __lowerCamelCase  							=      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:
          __lowerCamelCase  							=      tuple(state['''sep''']			)
       if "cls" in state:
          __lowerCamelCase  							=      tuple(state['''cls''']			)

       __lowerCamelCase  							=      False

       if state.get('''add_prefix_space'''		,			__UpperCAmelCase			) != add_prefix_space:
          __lowerCamelCase  							=      add_prefix_space
          __lowerCamelCase  							=      True

       if state.get('''trim_offsets'''		,			__UpperCAmelCase			) != trim_offsets:
          __lowerCamelCase  							=      trim_offsets
          __lowerCamelCase  							=      True

       if changes_to_apply:
          __lowerCamelCase  							=      getattr(__UpperCAmelCase		,			state.pop('''type'''			)			)
          __lowerCamelCase  							=      component_class(**__UpperCAmelCase			)
          setattr(self.backend_tokenizer		,			__UpperCAmelCase		,			__UpperCAmelCase			)




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    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		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      AddedToken(__UpperCAmelCase		,			lstrip=__UpperCAmelCase		,			rstrip=__UpperCAmelCase			) if isinstance(__UpperCAmelCase		,			__UpperCAmelCase			) else value
    __lowerCamelCase  							=      value




 def     lowerCamelCase (  self		,			*__UpperCAmelCase		,			**__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      kwargs.get('''is_split_into_words'''		,			__UpperCAmelCase			)
    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(*__UpperCAmelCase		,			**__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			*__UpperCAmelCase		,			**__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      kwargs.get('''is_split_into_words'''		,			__UpperCAmelCase			)

    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(*__UpperCAmelCase		,			**__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = None			):



    '''simple docstring'''
    __lowerCamelCase  							=      self._tokenizer.model.save(__UpperCAmelCase		,			name=__UpperCAmelCase			)
    return tuple(__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase=None			):



    '''simple docstring'''
    __lowerCamelCase  							=      [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		,			__UpperCAmelCase		,			__UpperCAmelCase = None			):



    '''simple docstring'''
    __lowerCamelCase  							=      [self.sep_token_id]
    __lowerCamelCase  							=      [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]






