'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


a=  logging.getLogger(__name__)







def  _UpperCamelCase (     _a	:	int=2				,     _a	:	List[str]=3				,     _a	:	Tuple=1_6				,     _a	:	int = 1_0				,     _a	:	int = 2			):




    """simple docstring"""



    def get_dataset(_a	:	Tuple			):
        __UpperCamelCase	:	int		    =  torch.randn(batch_size * n_batches				,     1			)
        return TensorDataset(_a				,     a * x + b + 0.1 * torch.randn(batch_size * n_batches				,     1			)			)

    __UpperCamelCase	:	Optional[int]		    =  get_dataset(_a			)
    __UpperCamelCase	:	Tuple		    =  get_dataset(_a			)
    __UpperCamelCase	:	int		    =  DataLoader(_a				,     shuffle=_a				,     batch_size=_a				,     num_workers=4			)
    __UpperCamelCase	:	Tuple		    =  DataLoader(_a				,     shuffle=_a				,     batch_size=_a				,     num_workers=4			)
    return (train_dataloader, valid_dataloader)







def  _UpperCamelCase (     _a	:	Optional[Any]				,     _a	:	Tuple				,     _a	:	List[Any]				,     _a	:	Tuple				,     _a	:	List[Any]				,     _a	:	int=None			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    for epoch in range(_a			):
        # Train quickly
        model.train()
        for batch in dataloader:
            __UpperCamelCase    ,					__UpperCamelCase	:	List[str]		    =  batch
            __UpperCamelCase	:	Any		    =  model(_a			)
            __UpperCamelCase	:	List[Any]		    =  torch.nn.functional.mse_loss(_a				,     _a			)
            accelerator.backward(_a			)
            optimizer.step()
            optimizer.zero_grad()
        rands.append(random.random()			)  # Introduce some randomness
        if scheduler is not None:
            scheduler.step()
    return rands






class 			__lowercase		(						nn.Module						):







     """simple docstring"""






     def __init__(		self    ):
         super().__init__()
         __UpperCamelCase	:	Any		    =  nn.Parameter(torch.randn(1    )    )
         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.randn(1    )    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return x * self.a + self.b






class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2    )
             __UpperCamelCase	:	List[Any]		    =  DummyModel()
             __UpperCamelCase	:	Dict		    =  torch.optim.Adam(params=model.parameters()     ,			lr=1E-3    )
             __UpperCamelCase    ,					__UpperCamelCase	:	List[str]		    =  dummy_dataloaders()
             __UpperCamelCase	:	int		    =  ProjectConfiguration(total_limit=1     ,			project_dir=_lowerCamelCase     ,			automatic_checkpoint_naming=_lowerCamelCase    )
             # Train baseline
             __UpperCamelCase	:	Optional[Any]		    =  Accelerator(project_config=_lowerCamelCase    )
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Optional[Any]		    =  accelerator.prepare(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             # Save initial
             accelerator.save_state()

             # Save second state
             accelerator.save_state()
             self.assertEqual(len(os.listdir(accelerator.project_dir    )    )     ,			1    )







     def lowerCAmelCase     (		self    ):
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2    )
             __UpperCamelCase	:	Union[str, Any]		    =  DummyModel()
             __UpperCamelCase	:	int		    =  torch.optim.Adam(params=model.parameters()     ,			lr=1E-3    )
             __UpperCamelCase    ,					__UpperCamelCase	:	Dict		    =  dummy_dataloaders()
             # Train baseline
             __UpperCamelCase	:	Dict		    =  Accelerator()
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[str]		    =  accelerator.prepare(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             # Save initial
             __UpperCamelCase	:	List[str]		    =  os.path.join(_lowerCamelCase     ,			'initial'    )
             accelerator.save_state(_lowerCamelCase    )
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	str		    =  model.a.item(), model.b.item()
             __UpperCamelCase	:	List[Any]		    =  optimizer.state_dict()
             __UpperCamelCase	:	List[str]		    =  train(3     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	Optional[Any]		    =  model.a.item(), model.b.item()
             __UpperCamelCase	:	List[str]		    =  optimizer.state_dict()

             # Train partially
             set_seed(4_2    )
             __UpperCamelCase	:	Optional[int]		    =  DummyModel()
             __UpperCamelCase	:	Dict		    =  torch.optim.Adam(params=model.parameters()     ,			lr=1E-3    )
             __UpperCamelCase    ,					__UpperCamelCase	:	List[str]		    =  dummy_dataloaders()
             __UpperCamelCase	:	Optional[Any]		    =  Accelerator()
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  accelerator.prepare(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             accelerator.load_state(_lowerCamelCase    )
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	Optional[Any]		    =  model.a.item(), model.b.item()
             __UpperCamelCase	:	Any		    =  optimizer.state_dict()
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )

             __UpperCamelCase	:	Tuple		    =  train(2     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             # Save everything
             __UpperCamelCase	:	Optional[Any]		    =  os.path.join(_lowerCamelCase     ,			'checkpoint'    )
             accelerator.save_state(_lowerCamelCase    )

             # Load everything back in and make sure all states work
             accelerator.load_state(_lowerCamelCase    )
             test_rands += train(1     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	List[str]		    =  model.a.item(), model.b.item()
             __UpperCamelCase	:	Optional[int]		    =  optimizer.state_dict()
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2    )
             __UpperCamelCase	:	List[str]		    =  DummyModel()
             __UpperCamelCase	:	Any		    =  torch.optim.Adam(params=model.parameters()     ,			lr=1E-3    )
             __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  dummy_dataloaders()
             __UpperCamelCase	:	Optional[int]		    =  ProjectConfiguration(automatic_checkpoint_naming=_lowerCamelCase    )

             # Train baseline
             __UpperCamelCase	:	Union[str, Any]		    =  Accelerator(project_dir=_lowerCamelCase     ,			project_config=_lowerCamelCase    )
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  accelerator.prepare(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             # Save initial
             accelerator.save_state()
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	List[str]		    =  model.a.item(), model.b.item()
             __UpperCamelCase	:	Union[str, Any]		    =  optimizer.state_dict()
             __UpperCamelCase	:	Optional[int]		    =  train(3     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	Optional[Any]		    =  model.a.item(), model.b.item()
             __UpperCamelCase	:	Tuple		    =  optimizer.state_dict()

             # Train partially
             set_seed(4_2    )
             __UpperCamelCase	:	Union[str, Any]		    =  DummyModel()
             __UpperCamelCase	:	Optional[int]		    =  torch.optim.Adam(params=model.parameters()     ,			lr=1E-3    )
             __UpperCamelCase    ,					__UpperCamelCase	:	Tuple		    =  dummy_dataloaders()
             __UpperCamelCase	:	Optional[Any]		    =  ProjectConfiguration(iteration=1     ,			automatic_checkpoint_naming=_lowerCamelCase    )
             __UpperCamelCase	:	Tuple		    =  Accelerator(project_dir=_lowerCamelCase     ,			project_config=_lowerCamelCase    )
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  accelerator.prepare(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             accelerator.load_state(os.path.join(_lowerCamelCase     ,			'checkpoints'     ,			'checkpoint_0'    )    )
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	List[str]		    =  model.a.item(), model.b.item()
             __UpperCamelCase	:	str		    =  optimizer.state_dict()
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )

             __UpperCamelCase	:	int		    =  train(2     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_lowerCamelCase     ,			'checkpoints'     ,			'checkpoint_1'    )    )
             test_rands += train(1     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             ((__UpperCamelCase)    ,					(__UpperCamelCase))	:	List[str]		    =  model.a.item(), model.b.item()
             __UpperCamelCase	:	Any		    =  optimizer.state_dict()
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertEqual(_lowerCamelCase     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  torch.tensor([1, 2, 3]    )
         __UpperCamelCase	:	Optional[Any]		    =  torch.tensor([2, 3, 4]    )
         __UpperCamelCase	:	List[Any]		    =  DummyModel()
         __UpperCamelCase	:	Optional[int]		    =  torch.optim.Adam(net.parameters()    )
         __UpperCamelCase	:	Optional[Any]		    =  Accelerator()
         with self.assertRaises(_lowerCamelCase    ) as ve:
             accelerator.register_for_checkpointing(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  str(ve.exception    )
         self.assertTrue('Item at index 0' in message    )
         self.assertTrue('Item at index 1' in message    )
         self.assertFalse('Item at index 2' in message    )
         self.assertFalse('Item at index 3' in message    )







     def lowerCAmelCase     (		self    ):
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2    )
             __UpperCamelCase	:	List[Any]		    =  DummyModel()
             __UpperCamelCase	:	int		    =  torch.optim.Adam(params=model.parameters()     ,			lr=1E-3    )
             __UpperCamelCase	:	List[Any]		    =  torch.optim.lr_scheduler.StepLR(_lowerCamelCase     ,			step_size=1     ,			gamma=0.9_9    )
             __UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  dummy_dataloaders()
             __UpperCamelCase	:	Union[str, Any]		    =  ProjectConfiguration(automatic_checkpoint_naming=_lowerCamelCase    )
             # Train baseline
             __UpperCamelCase	:	List[Any]		    =  Accelerator(project_dir=_lowerCamelCase     ,			project_config=_lowerCamelCase    )
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  accelerator.prepare(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             # Save initial
             accelerator.save_state()
             __UpperCamelCase	:	List[str]		    =  scheduler.state_dict()
             train(3     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )
             self.assertNotEqual(_lowerCamelCase     ,			scheduler.state_dict()    )

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_lowerCamelCase     ,			'checkpoints'     ,			'checkpoint_0'    )    )
             self.assertEqual(_lowerCamelCase     ,			scheduler.state_dict()    )







     def lowerCAmelCase     (		self    ):
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2    )
             __UpperCamelCase	:	Tuple		    =  DummyModel()
             __UpperCamelCase	:	int		    =  ProjectConfiguration(automatic_checkpoint_naming=_lowerCamelCase     ,			total_limit=2    )
             # Train baseline
             __UpperCamelCase	:	str		    =  Accelerator(project_dir=_lowerCamelCase     ,			project_config=_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  accelerator.prepare(_lowerCamelCase    )
             # Save 3 states:
             for _ in range(1_1    ):
                 accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(_lowerCamelCase     ,			'checkpoints'     ,			'checkpoint_0'    )    )    )
             self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase     ,			'checkpoints'     ,			'checkpoint_9'    )    )    )
             self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase     ,			'checkpoints'     ,			'checkpoint_10'    )    )    )







     @require_cuda
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
         execute_subprocess_async(_lowerCamelCase     ,			env=os.environ.copy()    )


if __name__ == "__main__":
  a=  '''/tmp/accelerate/state_checkpointing'''
  a=  DummyModel()
  a=  torch.optim.Adam(params=model.parameters(), lr=1E-3)
  a=  torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
  a,			a=  dummy_dataloaders()
  a=  ProjectConfiguration(automatic_checkpoint_naming=True)
  # Train baseline
  a=  Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
  if accelerator.process_index == 0:
    if os.path.exists(savedir):
      shutil.rmtree(savedir)
    os.makedirs(savedir)
  a,			a,			a,			a,			a=  accelerator.prepare(
      model, optimizer, train_dataloader, valid_dataloader, scheduler
  )
  a,			a=  accelerator.prepare(model, optimizer)
  train(3, model, train_dataloader, optimizer, accelerator, scheduler)
  # Check that the intial optimizer is loaded on the GPU
  for group in optimizer.param_groups:
    a=  group['''params'''][0].device
    break
  assert param_device.type == accelerator.device.type
  a=  model.cpu()
  accelerator.wait_for_everyone()
  accelerator.save_state()
  accelerator.wait_for_everyone()

  # Check CPU state
  accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
  for group in optimizer.param_groups:
    a=  group['''params'''][0].device
    break
  assert (
      param_device.type == torch.device('''cpu''').type
  ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

  # Check device state
  model.to(accelerator.device)
  accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
  for group in optimizer.param_groups:
    a=  group['''params'''][0].device
    break
  assert (
      param_device.type == accelerator.device.type
  ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

  # Check error
  with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
    accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
  accelerator.wait_for_everyone()
  if accelerator.process_index == 0:
    shutil.rmtree(savedir)
  accelerator.wait_for_everyone()




'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

a=  logging.get_logger(__name__)


a=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a=  {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

a=  {'''facebook/blenderbot-3B''': 1_2_8}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE__		   =						BlenderbotTokenizer







     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	:	str		    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
             __UpperCamelCase	:	Any		    =  getattr(_lowerCamelCase     ,			pre_tok_state.pop('type'    )    )
             __UpperCamelCase	:	Dict		    =  add_prefix_space
             __UpperCamelCase	:	Optional[Any]		    =  pre_tok_class(**_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  add_prefix_space

         __UpperCamelCase	:	Optional[int]		    =  'post_processor'
         __UpperCamelCase	:	Tuple		    =  getattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         if tokenizer_component_instance:
             __UpperCamelCase	:	str		    =  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	:	List[Any]		    =  tuple(state['sep']    )
             if "cls" in state:
                 __UpperCamelCase	:	str		    =  tuple(state['cls']    )

             __UpperCamelCase	:	Tuple		    =  False

             if state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
                 __UpperCamelCase	:	Dict		    =  add_prefix_space
                 __UpperCamelCase	:	str		    =  True

             if state.get('trim_offsets'     ,			_lowerCamelCase    ) != trim_offsets:
                 __UpperCamelCase	:	int		    =  trim_offsets
                 __UpperCamelCase	:	Any		    =  True

             if changes_to_apply:
                 __UpperCamelCase	:	Dict		    =  getattr(_lowerCamelCase     ,			state.pop('type'    )    )
                 __UpperCamelCase	:	Any		    =  component_class(**_lowerCamelCase    )
                 setattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     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	:	int		    =  AddedToken(_lowerCamelCase     ,			lstrip=_lowerCamelCase     ,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) else value
         __UpperCamelCase	:	Optional[Any]		    =  value







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  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	:	Dict		    =  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	:	List[str]		    =  self._tokenizer.model.save(_lowerCamelCase     ,			name=_lowerCamelCase    )
         return tuple(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	Union[str, Any]		    =  [self.sep_token_id]
         __UpperCamelCase	:	List[Any]		    =  [self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep    ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         return token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  []
         for is_user, text in conversation.iter_texts():
             if is_user:
                 # We need to space prefix as it's being done within blenderbot
                 inputs.append(' ' + text    )
             else:
                 # Generated responses should contain them already.
                 inputs.append(_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  '  '.join(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.encode(_lowerCamelCase    )
         if len(_lowerCamelCase    ) > self.model_max_length:
             __UpperCamelCase	:	Tuple		    =  input_ids[-self.model_max_length :]
             logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
         return input_ids



