




"""simple docstring"""





import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad



class   a__					(						unittest.TestCase ):


			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       10


			def    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       [1, 2, 3, 4]
						__UpperCAmelCase						:			str												=       [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
						self.assertEqual(truncate_or_pad(UpperCamelCase_   ,	self.block_size   ,	0)   ,	UpperCamelCase_)


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
						__UpperCAmelCase						:			Optional[int]												=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
						self.assertEqual(truncate_or_pad(UpperCamelCase_   ,	self.block_size   ,	0)   ,	UpperCamelCase_)


			def    a_  (   self   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
						__UpperCAmelCase						:			int												=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
						self.assertEqual(truncate_or_pad(UpperCamelCase_   ,	self.block_size   ,	0)   ,	UpperCamelCase_)


			def    a_  (   self   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       "It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this."
						__UpperCAmelCase						,  __UpperCAmelCase						:			str												=       process_story(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_   ,	[])


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       ""
						__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       process_story(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_   ,	[])
						self.assertEqual(UpperCamelCase_   ,	[])


			def    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       (
						    "It was the year of Our Lord one thousand seven hundred and "
						    "seventy-five\n\nSpiritual revelations were conceded to England "
						    "at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
						)
						__UpperCAmelCase						,  __UpperCAmelCase						:			Any												=       process_story(UpperCamelCase_)

						__UpperCAmelCase						:			Any												=       [
						    "It was the year of Our Lord one thousand seven hundred and seventy-five.",
						    "Spiritual revelations were conceded to England at that favoured period, as at this.",
						]
						self.assertEqual(UpperCamelCase_   ,	UpperCamelCase_)

						__UpperCAmelCase						:			str												=       ["It was the best of times."]
						self.assertEqual(UpperCamelCase_   ,	UpperCamelCase_)


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       torch.tensor([1, 2, 3, 4])
						__UpperCAmelCase						:			List[Any]												=       torch.tensor([1, 1, 1, 1])
						np.testing.assert_array_equal(build_mask(UpperCamelCase_   ,	0).numpy()   ,	expected.numpy())


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       torch.tensor([1, 2, 3, 4, 23, 23, 23])
						__UpperCAmelCase						:			Dict												=       torch.tensor([1, 1, 1, 1, 0, 0, 0])
						np.testing.assert_array_equal(build_mask(UpperCamelCase_   ,	23).numpy()   ,	expected.numpy())


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       torch.tensor([8, 2, 3, 4, 1, 1, 1])
						__UpperCAmelCase						:			Optional[int]												=       torch.tensor([1, 1, 1, 1, 0, 0, 0])
						np.testing.assert_array_equal(build_mask(UpperCamelCase_   ,	1).numpy()   ,	expected.numpy())


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       101
						__UpperCAmelCase						:			Optional[int]												=       torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]])
						__UpperCAmelCase						:			Tuple												=       torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]])

						__UpperCAmelCase						:			str												=       compute_token_type_ids(UpperCamelCase_   ,	UpperCamelCase_)
						np.testing.assert_array_equal(UpperCamelCase_   ,	UpperCamelCase_)





# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


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





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = True				,  lowerCAmelCase = False   )    ->					str:
        '''simple docstring'''






        _lowercase      			=scheduler
        _lowercase      			=optimizers if isinstance(lowerCAmelCase				,  (list, tuple)   ) else [optimizers]
        _lowercase      			=split_batches
        _lowercase      			=step_with_optimizer
        _lowercase      			=GradientState()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        if not self.step_with_optimizer:
              # No link between scheduler and optimizer -> just step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
              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(*lowerCAmelCase				,  **lowerCAmelCase   )
        else:
              # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
              # num_processes steps per training step
              _lowercase      			=AcceleratorState().num_processes
              for _ in range(lowerCAmelCase   ):
                    # 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(*lowerCAmelCase				,  **lowerCAmelCase   )
                    else:
                          self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        return self.scheduler.get_last_lr()




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        return self.scheduler.state_dict()




  def     A__	(							self				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        self.scheduler.load_state_dict(lowerCAmelCase   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self.scheduler.get_lr()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






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


