code: 
  
  
  
  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 			__lowercase     (				a__=2							,     a__=True							,     a__=False     )       ->				int:
  							__SCREAMING_SNAKE_CASE								=      Accelerator(step_scheduler_with_optimizer=a__							,     split_batches=a__     )
  							__SCREAMING_SNAKE_CASE								=      torch.nn.Linear(2							,     4     )
  							__SCREAMING_SNAKE_CASE								=      torch.optim.AdamW(model.parameters()							,     lr=1.0     )
  							__SCREAMING_SNAKE_CASE								=      torch.optim.lr_scheduler.OneCycleLR(a__							,     max_lr=0.01							,     steps_per_epoch=2							,     epochs=1     )
  							__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      accelerator.prepare(a__							,     a__							,     a__     )
  
  							# 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 			__lowercase     (				a__=2							,     a__=True							,     a__=False     )       ->				Optional[Any]:
  							__SCREAMING_SNAKE_CASE								=      Accelerator(step_scheduler_with_optimizer=a__							,     split_batches=a__     )
  							__SCREAMING_SNAKE_CASE								=      torch.nn.Linear(2							,     4     )
  							__SCREAMING_SNAKE_CASE								=      torch.optim.AdamW(model.parameters()							,     lr=1.0     )
  							__SCREAMING_SNAKE_CASE								=      torch.optim.lr_scheduler.LambdaLR(a__							,     lr_lambda=lambda a__     : 1 - n / 10     )
  							__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      accelerator.prepare(a__							,     a__							,     a__     )
  
  							# Optimizer has stepped
  							__SCREAMING_SNAKE_CASE								=      False
  							scheduler.step()
  							__SCREAMING_SNAKE_CASE								=      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
  							__SCREAMING_SNAKE_CASE								=      True
  							scheduler.step()
  							if not step_scheduler_with_optimizer:
  														__SCREAMING_SNAKE_CASE								=      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 			__lowercase     (				a__ = 2     )       ->				Optional[Any]:
  							from transformers import get_linear_schedule_with_warmup
  
  							__SCREAMING_SNAKE_CASE								=      [1, 2, 4]
  							for num_steps in steps:
  														__SCREAMING_SNAKE_CASE								=      GradientAccumulationPlugin(num_steps=a__							,     adjust_scheduler=num_steps > 1     )
  														__SCREAMING_SNAKE_CASE								=      Accelerator(gradient_accumulation_plugin=a__     )
  														__SCREAMING_SNAKE_CASE								=      torch.nn.Linear(2							,     4     )
  														__SCREAMING_SNAKE_CASE								=      torch.optim.AdamW(model.parameters()							,     lr=10.0     )
  														__SCREAMING_SNAKE_CASE								=      get_linear_schedule_with_warmup(optimizer=a__							,     num_warmup_steps=0							,     num_training_steps=20     )
  
  														__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      accelerator.prepare(a__							,     a__							,     a__     )
  
  														for i in range(10 * num_steps     ):
  																					with accelerator.accumulate(a__     ):
  																												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 ):
  
  						'''simple docstring'''
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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  _A			(					self	):
  
  
  
  
  
  
  
  													'''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  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													debug_launcher(partial(_A   ,							num_processes=1   ,							step_scheduler_with_optimizer=_A	)   ,							num_processes=1	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													debug_launcher(partial(_A   ,							num_processes=1   ,							step_scheduler_with_optimizer=_A	)   ,							num_processes=1	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													AcceleratorState._reset_state(_A	)
  													debug_launcher(_A	)
  													debug_launcher(partial(_A   ,							num_processes=1   ,							split_batches=_A	)   ,							num_processes=1	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													AcceleratorState._reset_state(_A	)
  													debug_launcher(_A	)
  													debug_launcher(partial(_A   ,							num_processes=1   ,							split_batches=_A	)   ,							num_processes=1	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													AcceleratorState._reset_state(_A	)
  													debug_launcher(partial(_A   ,							step_scheduler_with_optimizer=_A	)	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													AcceleratorState._reset_state(_A	)
  													debug_launcher(partial(_A   ,							step_scheduler_with_optimizer=_A	)	)
  
  
  
  
  
  
  						@require_huggingface_suite
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													AcceleratorState._reset_state(_A	)
  													debug_launcher(partial(_A   ,							num_processes=1	)	)
  													debug_launcher(_A	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  import unittest
  
  from cer import CER
  
  
  lowerCAmelCase__	:  Dict 		=CER()
  
  class      UpperCAmelCase_						(     unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['White House']
  													__SCREAMING_SNAKE_CASE								=      ['white house']
  													# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['were wolf']
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1	) < 1e-6	)
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      ['weae     wolf']
  													# S = 1, D = 1, I = 0, N = 8, CER = 0.25
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.2_5	) < 1e-6	)
  
  													# consecutive whitespaces case 1
  													__SCREAMING_SNAKE_CASE								=      ['were wolf']
  													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
  													# S = 0, D = 0, I = 0, N = 9, CER = 0
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)
  
  													# consecutive whitespaces case 2
  													__SCREAMING_SNAKE_CASE								=      ['were   wolf']
  													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
  													# S = 0, D = 0, I = 0, N = 9, CER = 0
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      ['weaewolf']
  													# S = 1, D = 0, I = 0, N = 8, CER = 0.125
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      ['wereawolf']
  													# S = 0, D = 1, I = 0, N = 8, CER = 0.125
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      ['wereolf']
  													# S = 0, D = 0, I = 1, N = 8, CER = 0.125
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf']
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertEqual(_A   ,							0.0	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father']
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertEqual(_A   ,							0.0	)
  
  													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father', 'doge']
  													__SCREAMING_SNAKE_CASE								=      ['werxwolf', 'I       am your father', 'doge']
  													# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['My hovercraft', 'is full of eels']
  													__SCREAMING_SNAKE_CASE								=      ['My hovercraft is full', ' of eels']
  													# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
  													# whitespace at the front of " of eels" will be strip during preporcessing
  													# so need to insert 2 whitespaces
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A   ,							concatenate_texts=_A	)
  													self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8	) < 1e-6	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
  													__SCREAMING_SNAKE_CASE								=      [' 能吞虾玻璃而 不霜身体啦']
  													# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5	) < 1e-6	)
  
  													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃', '而不伤身体']
  													__SCREAMING_SNAKE_CASE								=      ['我    能 吞 下 玻 璃', '而不伤身体']
  													# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5	) < 1e-6	)
  
  													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
  													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
  													self.assertFalse(_A   ,							0.0	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ['']
  													__SCREAMING_SNAKE_CASE								=      ['Hypothesis']
  													with self.assertRaises(_A	):
  																				cer.compute(predictions=_A   ,							references=_A	)
  
  
  if __name__ == "__main__":
  			unittest.main()
  
  
  
  
  
  
  