


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


UpperCAmelCase__     			=							logging.get_logger(__name__)

UpperCAmelCase__     			=							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__     			=							{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase__     			=							{
    '''microsoft/mpnet-base''': 512,
}

UpperCAmelCase__     			=							{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}







class    lowerCamelCase__     ( lowerCAmelCase):
					SCREAMING_SNAKE_CASE__       =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE__       =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE__       =    PRETRAINED_INIT_CONFIGURATION
					SCREAMING_SNAKE_CASE__       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE__       =    MPNetTokenizer
					SCREAMING_SNAKE_CASE__       =    ['''input_ids''', '''attention_mask''']





					def __init__(self	,  UpperCAmelCase=None	,  UpperCAmelCase=None	,  UpperCAmelCase=True	,  UpperCAmelCase="<s>"	,  UpperCAmelCase="</s>"	,  UpperCAmelCase="</s>"	,  UpperCAmelCase="<s>"	,  UpperCAmelCase="[UNK]"	,  UpperCAmelCase="<pad>"	,  UpperCAmelCase="<mask>"	,  UpperCAmelCase=True	,  UpperCAmelCase=None	,  **UpperCAmelCase	,  )  ->     int:
						super().__init__(
						    UpperCAmelCase	,  tokenizer_file=UpperCAmelCase	,  do_lower_case=UpperCAmelCase	,  bos_token=UpperCAmelCase	,  eos_token=UpperCAmelCase	,  sep_token=UpperCAmelCase	,  cls_token=UpperCAmelCase	,  unk_token=UpperCAmelCase	,  pad_token=UpperCAmelCase	,  mask_token=UpperCAmelCase	,  tokenize_chinese_chars=UpperCAmelCase	,  strip_accents=UpperCAmelCase	,  **UpperCAmelCase	,  )

						_lowercase							   =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
						if (
						    pre_tok_state.get('''lowercase'''	,  UpperCAmelCase ) != do_lower_case
						    or pre_tok_state.get('''strip_accents'''	,  UpperCAmelCase ) != strip_accents
						):
							_lowercase							   =getattr(UpperCAmelCase	,  pre_tok_state.pop('''type''' ) )
							_lowercase							   =do_lower_case
							_lowercase							   =strip_accents
							_lowercase							   =pre_tok_class(**UpperCAmelCase )

						_lowercase							   =do_lower_case





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





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





					def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase=None )  ->     Optional[int]:
						_lowercase							   =[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   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase = None )  ->     List[int]:
						_lowercase							   =[self.sep_token_id]
						_lowercase							   =[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   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase = None )  ->     Tuple[str]:
						_lowercase							   =self._tokenizer.model.save(UpperCAmelCase	,  name=UpperCAmelCase )
						return tuple(UpperCAmelCase )





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Dict:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.OneCycleLR(__snake_case  ,		max_lr=0.01  ,		steps_per_epoch=2  ,		epochs=1		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 scheduler.step()
 if step_scheduler_with_optimizer or (num_processes == 1):
  assert (
      scheduler.scheduler.last_epoch == num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
 else:
  assert (
      scheduler.scheduler.last_epoch != num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.LambdaLR(__snake_case  ,		lr_lambda=lambda __snake_case		: 1 - n / 10		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 _lowercase							   =False
 scheduler.step()
 _lowercase							   =1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

 # Optimizer has not stepped
 _lowercase							   =True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  _lowercase							   =1 - 2 / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"
def        UpperCAmelCase_      (							__snake_case = 2		)  ->    Optional[int]:







 """simple docstring"""


 from transformers import get_linear_schedule_with_warmup

 _lowercase							   =[1, 2, 4]
 for num_steps in steps:
  _lowercase							   =GradientAccumulationPlugin(num_steps=__snake_case  ,		adjust_scheduler=num_steps > 1		)
  _lowercase							   =Accelerator(gradient_accumulation_plugin=__snake_case		)
  _lowercase							   =torch.nn.Linear(2  ,		4		)
  _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=10.0		)
  _lowercase							   =get_linear_schedule_with_warmup(optimizer=__snake_case  ,		num_warmup_steps=0  ,		num_training_steps=20		)

  _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

  for i in range(10 * num_steps		):
   with accelerator.accumulate(__snake_case		):
    optimizer.step()
    scheduler.step()

   if i == (10 * num_steps - 2):
    assert (
        scheduler.get_last_lr()[0] != 0
    ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
  assert (
      scheduler.get_last_lr()[0] == 0
  ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
  GradientState._reset_state()







@require_cpu
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Any:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Union[str, Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Dict:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Optional[Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     List[str]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     def   __A      (self )  ->     Union[str, Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     @require_huggingface_suite
     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 ) )
      debug_launcher(UpperCAmelCase )

