code: 
  
  
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  __lowercase:		List[str]							=     "0.1"
  __lowercase:		Union[str, Any]							=     "Lucia Harcekova"
  
  
  
  
  
  
  
  @dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					int
   _lowerCamelCase :					int
   _lowerCamelCase :					str
  
   def __repr__( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						window_size
      UpperCamelCase__ 						=						lookahead_buffer_size
      UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      UpperCamelCase__ 						=						""
  
      # while there are still characters in text to compress
      while text:
         # find the next encoding phrase
         # - triplet with offset, length, indicator (the next encoding character)
         UpperCamelCase__ 						=						self._find_encoding_token(a_,					a_					)
  
         # update the search buffer:
         # - add new characters from text into it
         # - check if size exceed the max search buffer size, if so, drop the
         #   oldest elements
         search_buffer += text[: token.length + 1]
         if len(a_					) > self.search_buffer_size:
            UpperCamelCase__ 						=						search_buffer[-self.search_buffer_size :]
  
         # update the text
         UpperCamelCase__ 						=						text[token.length + 1 :]
  
         # append the token to output
         output.append(a_					)
  
      return output
  
   def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						""
  
      for token in tokens:
         for _ in range(token.length					):
            output += output[-token.offset]
         output += token.indicator
  
      return output
  
   def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text:
         raise ValueError("We need some text to work with."					)
  
      # Initialise result parameters to default values
      UpperCamelCase__						,		UpperCamelCase__ 						=						0, 0
  
      if not search_buffer:
         return Token(a_,					a_,					text[length]					)
  
      for i, character in enumerate(a_					):
         UpperCamelCase__ 						=						len(a_					) - i
         if character == text[0]:
            UpperCamelCase__ 						=						self._match_length_from_index(a_,					a_,					0,					a_					)
            # if the found length is bigger than the current or if it's equal,
            # which means it's offset is smaller: update offset and length
            if found_length >= length:
               UpperCamelCase__						,		UpperCamelCase__ 						=						found_offset, found_length
  
      return Token(a_,					a_,					text[length]					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          a_,					window + text[text_index],					text_index + 1,					window_index + 1					)
  
  
  if __name__ == "__main__":
         from doctest import testmod
  
         testmod()
         # Initialize compressor class
         __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
         # Example
         __lowercase:		Union[str, Any]							=     "cabracadabrarrarrad"
         __lowercase:		Optional[int]							=     lzaa_compressor.compress(TEXT)
         print(lzaa_compressor.compress("ababcbababaa"))
         __lowercase:		Optional[int]							=     lzaa_compressor.decompress(compressed_text)
         assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
style_context: 
  
  
  '''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      :				Any=2    ,		_UpperCamelCase      :				Tuple=True    ,		_UpperCamelCase      :				Union[str, Any]=False   )		->					int:
  
  
  
  
  
     '''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      :				Optional[int]=2    ,		_UpperCamelCase      :				List[str]=True    ,		_UpperCamelCase      :				Tuple=False   )		->					Optional[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.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      :				int = 2   )		->					Optional[int]:
  
  
  
  
  
     '''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 :	int					):
  
  
  
  
  
      """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 :	Union[str, 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 :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      debug_launcher(partial(a_,					num_processes=1,					step_scheduler_with_optimizer=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      debug_launcher(partial(a_,					num_processes=1,					step_scheduler_with_optimizer=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """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 :	Union[str, 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 :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					step_scheduler_with_optimizer=a_					)					)
  
   def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					step_scheduler_with_optimizer=a_					)					)
  
   @require_huggingface_suite
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					num_processes=1					)					)
      debug_launcher(a_					)