


'''simple docstring'''

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 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Tuple=2			,						_UpperCamelCase					: Tuple=True			,						_UpperCamelCase					: Tuple=False      )						->    List[Any]:





   '''simple docstring'''







   UpperCamelCase__         =  Accelerator(step_scheduler_with_optimizer=__UpperCamelCase			,						split_batches=__UpperCamelCase      )
   UpperCamelCase__         =  torch.nn.Linear(2			,						4      )
   UpperCamelCase__         =  torch.optim.AdamW(model.parameters()			,						lr=1.0      )
   UpperCamelCase__         =  torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase			,						max_lr=0.0_1			,						steps_per_epoch=2			,						epochs=1      )
   UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__         =  accelerator.prepare(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase      )

   # 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 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Dict=2			,						_UpperCamelCase					: Tuple=True			,						_UpperCamelCase					: Dict=False      )						->    List[str]:





   '''simple docstring'''







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

   # Optimizer has stepped
   UpperCamelCase__         =  False
   scheduler.step()
   UpperCamelCase__         =  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
   UpperCamelCase__         =  True
   scheduler.step()
   if not step_scheduler_with_optimizer:
      UpperCamelCase__         =  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 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Optional[Any] = 2      )						->    Any:





   '''simple docstring'''







   from transformers import get_linear_schedule_with_warmup

   UpperCamelCase__         =  [1, 2, 4]
   for num_steps in steps:
      UpperCamelCase__         =  GradientAccumulationPlugin(num_steps=__UpperCamelCase			,						adjust_scheduler=num_steps > 1      )
      UpperCamelCase__         =  Accelerator(gradient_accumulation_plugin=__UpperCamelCase      )
      UpperCamelCase__         =  torch.nn.Linear(2			,						4      )
      UpperCamelCase__         =  torch.optim.AdamW(model.parameters()			,						lr=1_0.0      )
      UpperCamelCase__         =  get_linear_schedule_with_warmup(optimizer=__UpperCamelCase			,						num_warmup_steps=0			,						num_training_steps=20      )

      UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__         =  accelerator.prepare(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase      )

      for i in range(10 * num_steps      ):
         with accelerator.accumulate(__UpperCamelCase      ):
            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        UpperCAmelCase       (				unittest.TestCase):

 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    debug_launcher(partial(a_,    num_processes=1    ),    num_processes=1    )
    debug_launcher(partial(a_,    num_processes=1,    split_batches=a_    ),    num_processes=1    )

 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    debug_launcher(partial(a_,    num_processes=1    ),    num_processes=1    )
    debug_launcher(partial(a_,    num_processes=1,    split_batches=a_    ),    num_processes=1    )

 def 						lowercase_  (       self       :		Tuple    ):





    """simple docstring"""



    debug_launcher(partial(a_,    num_processes=1,    step_scheduler_with_optimizer=a_    ),    num_processes=1    )

 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    debug_launcher(partial(a_,    num_processes=1,    step_scheduler_with_optimizer=a_    ),    num_processes=1    )

 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    AcceleratorState._reset_state(a_    )
    debug_launcher(a_    )
    debug_launcher(partial(a_,    num_processes=1,    split_batches=a_    ),    num_processes=1    )

 def 						lowercase_  (       self       :		Dict    ):





    """simple docstring"""



    AcceleratorState._reset_state(a_    )
    debug_launcher(a_    )
    debug_launcher(partial(a_,    num_processes=1,    split_batches=a_    ),    num_processes=1    )

 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    AcceleratorState._reset_state(a_    )
    debug_launcher(partial(a_,    step_scheduler_with_optimizer=a_    )    )

 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    AcceleratorState._reset_state(a_    )
    debug_launcher(partial(a_,    step_scheduler_with_optimizer=a_    )    )

 @require_huggingface_suite
 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    AcceleratorState._reset_state(a_    )
    debug_launcher(partial(a_,    num_processes=1    )    )
    debug_launcher(a_    )





import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Dict							=	MPNetTokenizer
     a		:			Dict							=	MPNetTokenizerFast
     a		:			Optional[int]							=	True
     a		:			Any							=	True







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          super().setUp()

          a      =      [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          a      =      os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8" ) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          a      =      "UNwant\u00E9d,running"
          a      =      "unwanted, running"
          return input_text, output_text







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



          '''simple docstring'''



          a      =      self.tokenizer_class(self.vocab_file )

          a      =      tokenizer.tokenize("UNwant\u00E9d,running" )
          self.assertListEqual(A  ,		["un", "##want", "##ed", ",", "runn", "##ing"] )
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A )  ,		[9, 6, 7, 12, 10, 11] )







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      self.tokenizer_class.from_pretrained("microsoft/mpnet-base" )

          a      =      tokenizer.encode("sequence builders"  ,		add_special_tokens=A )
          a      =      tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A )

          a      =      tokenizer.build_inputs_with_special_tokens(A )
          a      =      tokenizer.build_inputs_with_special_tokens(A  ,		A )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]

