


import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = True      ,      __lowercase = False						):

  """simple docstring"""







  __A  :		Optional[int]       =			scheduler
  __A  :		Dict       =			optimizers if isinstance(__lowercase      ,      (list, tuple)						) else [optimizers]
  __A  :		int       =			split_batches
  __A  :		int       =			step_with_optimizer
  __A  :		str       =			GradientState()


 def  snake_case__   (      self      ,      *__lowercase      ,      **__lowercase						):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase      ,      **__lowercase						)
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase      ,      **__lowercase						)
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A  :		Optional[int]       =			AcceleratorState().num_processes
   for _ in range(__lowercase						):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler      ,      'total_steps'						):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase      ,      **__lowercase						)
    else:
     self.scheduler.step(*__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.scheduler.state_dict()


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.scheduler.get_lr()







 def  snake_case__   (      self      ,      *__lowercase      ,      **__lowercase						):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase      ,      **__lowercase						)










'''simple docstring'''
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						XLMTokenizer
 __lowercase				:		Any    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  __A  :		Optional[int]       =			[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  __A  :		int       =			dict(zip(__lowercase      ,      range(len(__lowercase						)						)						)						)
  __A  :		str       =			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  __A  :		Dict       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['vocab_file']						)
  __A  :		List[Any]       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['merges_file']						)
  with open(self.vocab_file      ,      'w'						) as fp:
   fp.write(json.dumps(__lowercase						)						)
  with open(self.merges_file      ,      'w'						) as fp:
   fp.write('\n'.join(__lowercase						)						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			'lower newer'
  __A  :		int       =			'lower newer'
  return input_text, output_text


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			XLMTokenizer(self.vocab_file      ,      self.merges_file						)

  __A  :		Optional[Any]       =			'lower'
  __A  :		Union[str, Any]       =			['low', 'er</w>']
  __A  :		Any       =			tokenizer.tokenize(__lowercase						)
  self.assertListEqual(__lowercase      ,      __lowercase						)

  __A  :		Optional[int]       =			tokens + ['<unk>']
  __A  :		Any       =			[14, 15, 20]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)      ,      __lowercase						)







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			XLMTokenizer.from_pretrained('xlm-mlm-en-2048'						)

  __A  :		Tuple       =			tokenizer.encode('sequence builders'      ,      add_special_tokens=__lowercase						)
  __A  :		Union[str, Any]       =			tokenizer.encode('multi-sequence build'      ,      add_special_tokens=__lowercase						)

  __A  :		int       =			tokenizer.build_inputs_with_special_tokens(__lowercase						)
  __A  :		Tuple       =			tokenizer.build_inputs_with_special_tokens(__lowercase      ,      __lowercase						)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]






