


from dataclasses import dataclass

__UpperCamelCase					:	str          =				'0.1'
__UpperCamelCase					:	str          =				'Lucia Harcekova'





@dataclass
class 			lowerCAmelCase__:






    '''simple docstring'''





    A_	:  int
    A_	:  int
    A_	:  str





    def __repr__(						self					: int						):





       '''simple docstring'''
       return f'''({self.offset}, {self.length}, {self.indicator})'''





class 			lowerCAmelCase__:






    '''simple docstring'''





    def __init__(						self					: str							, __snake_case					: int = 13							, __snake_case					: int = 6						):





       '''simple docstring'''
       UpperCAmelCase_				:   Any							= window_size
       UpperCAmelCase_				:   List[str]							= lookahead_buffer_size
       UpperCAmelCase_				:   Optional[int]							= self.window_size - self.lookahead_buffer_size





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: str						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= []
       UpperCAmelCase_				:   str							= ''''''

       # 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_				:   Union[str, Any]							= self._find_encoding_token(__snake_case							, __snake_case						)

          # 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(__snake_case						) > self.search_buffer_size:
             UpperCAmelCase_				:   Dict							= search_buffer[-self.search_buffer_size :]

          # update the text
          UpperCAmelCase_				:   Any							= text[token.length + 1 :]

          # append the token to output
          output.append(__snake_case						)

       return output





    def        _lowerCamelCase			(						self					: Tuple							, __snake_case					: list[Token]						):





       '''simple docstring'''
       UpperCAmelCase_				:   Tuple							= ''''''

       for token in tokens:
          for _ in range(token.length						):
             output += output[-token.offset]
          output += token.indicator

       return output





    def        _lowerCamelCase			(						self					: Dict							, __snake_case					: str							, __snake_case					: str						):





       '''simple docstring'''

       if not text:
          raise ValueError('''We need some text to work with.'''						)

       # Initialise result parameters to default values
       UpperCAmelCase_		,   UpperCAmelCase_				:   int							= 0, 0

       if not search_buffer:
          return Token(__snake_case							, __snake_case							, text[length]						)

       for i, character in enumerate(__snake_case						):
          UpperCAmelCase_				:   Union[str, Any]							= len(__snake_case						) - i
          if character == text[0]:
             UpperCAmelCase_				:   Any							= self._match_length_from_index(__snake_case							, __snake_case							, 0							, __snake_case						)
             # 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_				:   List[Any]							= found_offset, found_length

       return Token(__snake_case							, __snake_case							, text[length]						)





    def        _lowerCamelCase			(						self					: Union[str, Any]							, __snake_case					: str							, __snake_case					: str							, __snake_case					: int							, __snake_case					: int						):





       '''simple docstring'''
       if not text or text[text_index] != window[window_index]:
          return 0
       return 1 + self._match_length_from_index(
           __snake_case							, window + text[text_index]							, text_index + 1							, window_index + 1						)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       # Initialize compressor class
       __UpperCamelCase					:	Tuple          =				LZaaCompressor(window_size=13, lookahead_buffer_size=6)

       # Example
       __UpperCamelCase					:	Any          =				'cabracadabrarrarrad'
       __UpperCamelCase					:	Union[str, Any]          =				lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('ababcbababaa'))
       __UpperCamelCase					:	int          =				lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__UpperCamelCase					:	int          =				get_logger(__name__)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase = None      ,							):
   # Model states
   for i, state in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Model weights saved in {output_model_file}'''							)
   # Optimizer states
   for i, opt in enumerate(__lowercase							):
      UpperCAmelCase_				:   Tuple							= opt.state_dict()
      UpperCAmelCase_				:   Any							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Optimizer state saved in {output_optimizer_file}'''							)
   # Scheduler states
   for i, scheduler in enumerate(__lowercase							):
      UpperCAmelCase_				:   Tuple							= scheduler.state_dict()
      UpperCAmelCase_				:   Tuple							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   Tuple							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Scheduler state saved in {output_scheduler_file}'''							)
   # GradScaler state
   if scaler is not None:
      UpperCAmelCase_				:   Union[str, Any]							= scaler.state_dict()
      UpperCAmelCase_				:   Union[str, Any]							= os.path.join(__lowercase      ,							__lowercase							)
      torch.save(__lowercase      ,							__lowercase							)
      logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''							)
   # Random number generator states
   UpperCAmelCase_				:   Union[str, Any]							= {}
   UpperCAmelCase_				:   Tuple							= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
   UpperCAmelCase_				:   List[Any]							= random.getstate()
   UpperCAmelCase_				:   Any							= np.random.get_state()
   UpperCAmelCase_				:   List[Any]							= torch.get_rng_state()
   if is_xpu_available():
      UpperCAmelCase_				:   Optional[int]							= torch.xpu.get_rng_state_all()
   else:
      UpperCAmelCase_				:   Any							= torch.cuda.get_rng_state_all()
   if is_tpu_available():
      UpperCAmelCase_				:   List[str]							= xm.get_rng_state()
   UpperCAmelCase_				:   Any							= os.path.join(__lowercase      ,							__lowercase							)
   torch.save(__lowercase      ,							__lowercase							)
   logger.info(F'''Random states saved in {output_states_file}'''							)
   return output_dir
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase=None      ,							__lowercase=None      ,							**__lowercase      ,							):
   if map_location not in [None, "cpu", "on_device"]:
      raise TypeError(
          '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
   if map_location is None:
      UpperCAmelCase_				:   Optional[Any]							= '''cpu'''
   elif map_location == "on_device":
      UpperCAmelCase_				:   Optional[int]							= PartialState().device
   # Model states
   for i, model in enumerate(__lowercase							):
      UpperCAmelCase_				:   List[Any]							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= os.path.join(__lowercase      ,							__lowercase							)
      models[i].load_state_dict(torch.load(__lowercase      ,							map_location=__lowercase							)      ,							**__lowercase							)
   logger.info('''All model weights loaded successfully'''							)

   # Optimizer states
   for i, opt in enumerate(__lowercase							):
      UpperCAmelCase_				:   List[str]							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      UpperCAmelCase_				:   Tuple							= torch.load(__lowercase      ,							map_location=__lowercase							)
      optimizers[i].load_state_dict(__lowercase							)
   logger.info('''All optimizer states loaded successfully'''							)

   # Scheduler states
   for i, scheduler in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= os.path.join(__lowercase      ,							__lowercase							)
      scheduler.load_state_dict(torch.load(__lowercase							)							)
   logger.info('''All scheduler states loaded successfully'''							)

   # GradScaler state
   if scaler is not None:
      UpperCAmelCase_				:   List[str]							= os.path.join(__lowercase      ,							__lowercase							)
      scaler.load_state_dict(torch.load(__lowercase							)							)
      logger.info('''GradScaler state loaded successfully'''							)

   # Random states
   try:
      UpperCAmelCase_				:   Optional[Any]							= torch.load(os.path.join(__lowercase      ,							F'''{RNG_STATE_NAME}_{process_index}.pkl'''							)							)
      random.setstate(states['''random_state''']							)
      np.random.set_state(states['''numpy_random_seed''']							)
      torch.set_rng_state(states['''torch_manual_seed''']							)
      if is_xpu_available():
         torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
      else:
         torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
      if is_tpu_available():
         xm.set_rng_state(states['''xm_seed''']							)
      logger.info('''All random states loaded successfully'''							)
   except Exception:
      logger.info('''Could not load random states'''							)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Optional[int]							= Path(__lowercase							) / F'''custom_checkpoint_{index}.pkl'''
   logger.info(F'''Saving the state of {get_pretty_name(__lowercase							)} to {save_location}'''							)
   torch.save(obj.state_dict()      ,							__lowercase							)


def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Dict							= F'''{path}/custom_checkpoint_{index}.pkl'''
   logger.info(F'''Loading the state of {get_pretty_name(__lowercase							)} from {load_location}'''							)
   obj.load_state_dict(torch.load(__lowercase      ,							map_location='''cpu'''							)							)