




"""simple docstring"""





from dataclasses import dataclass

_lowercase	      =    """0.1"""
_lowercase	      =    """Lucia Harcekova"""

@dataclass
class       __lowercase       :


				'''simple docstring'''


				_lowerCAmelCase				=42
				_lowerCAmelCase				=42
				_lowerCAmelCase				=42

				def __repr__(   self   :     int      ):
										return F'''({self.offset}, {self.length}, {self.indicator})'''







class       __lowercase       :


				'''simple docstring'''


				def __init__(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     int = 1_3      ,SCREAMING_SNAKE_CASE_   :     int = 6      ):
										snake_case__       :					str										=					window_size
										snake_case__       :					Any										=					lookahead_buffer_size
										snake_case__       :					Tuple										=					self.window_size - self.lookahead_buffer_size

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     str      ):
										snake_case__       :					Optional[Any]										=					[]
										snake_case__       :					Tuple										=					''''''

										# 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)
																snake_case__       :					List[Any]										=					self._find_encoding_token(SCREAMING_SNAKE_CASE_      ,SCREAMING_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(SCREAMING_SNAKE_CASE_      ) > self.search_buffer_size:
																						snake_case__       :					int										=					search_buffer[-self.search_buffer_size :]

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

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

										return output

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     list[Token]      ):
										snake_case__       :					Dict										=					''''''

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

										return output

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     str      ):

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

										# Initialise result parameters to default values
										snake_case__       :					Optional[Any]										=					0, 0

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

										for i, character in enumerate(SCREAMING_SNAKE_CASE_      ):
																snake_case__       :					List[str]										=					len(SCREAMING_SNAKE_CASE_      ) - i
																if character == text[0]:
																						snake_case__       :					Tuple										=					self._match_length_from_index(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,0      ,SCREAMING_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:
																												snake_case__       :					str										=					found_offset, found_length

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





				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     int      ):
										if not text or text[text_index] != window[window_index]:
																return 0
										return 1 + self._match_length_from_index(
										    SCREAMING_SNAKE_CASE_      ,window + text[text_index]      ,text_index + 1      ,window_index + 1      )


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

							testmod()
							# Initialize compressor class
							_lowercase	      =    LZaaCompressor(window_size=13, lookahead_buffer_size=6)

							# Example
							_lowercase	      =    """cabracadabrarrarrad"""
							_lowercase	      =    lzaa_compressor.compress(TEXT)
							print(lzaa_compressor.compress("""ababcbababaa"""))
							_lowercase	      =    lzaa_compressor.decompress(compressed_text)
							assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."








"""simple docstring"""





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


_lowercase	      =    get_logger(__name__)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase = None      ,			):
						'''simple docstring'''


						for i, state in enumerate(lowerCamelCase		):
												snake_case__       :					List[Any]										=					f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
												snake_case__       :					Tuple										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												save(lowerCamelCase      ,			lowerCamelCase		)
												logger.info(f'''Model weights saved in {output_model_file}'''		)
						# Optimizer states
						for i, opt in enumerate(lowerCamelCase		):
												snake_case__       :					str										=					opt.state_dict()
												snake_case__       :					Optional[Any]										=					f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
												snake_case__       :					str										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												save(lowerCamelCase      ,			lowerCamelCase		)
												logger.info(f'''Optimizer state saved in {output_optimizer_file}'''		)
						# Scheduler states
						for i, scheduler in enumerate(lowerCamelCase		):
												snake_case__       :					int										=					scheduler.state_dict()
												snake_case__       :					List[Any]										=					f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
												snake_case__       :					int										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												save(lowerCamelCase      ,			lowerCamelCase		)
												logger.info(f'''Scheduler state saved in {output_scheduler_file}'''		)
						# GradScaler state
						if scaler is not None:
												snake_case__       :					Tuple										=					scaler.state_dict()
												snake_case__       :					Tuple										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												torch.save(lowerCamelCase      ,			lowerCamelCase		)
												logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''		)
						# Random number generator states
						snake_case__       :					Dict										=					{}
						snake_case__       :					Dict										=					f'''{RNG_STATE_NAME}_{process_index}.pkl'''
						snake_case__       :					Dict										=					random.getstate()
						snake_case__       :					List[str]										=					np.random.get_state()
						snake_case__       :					str										=					torch.get_rng_state()
						if is_xpu_available():
												snake_case__       :					List[Any]										=					torch.xpu.get_rng_state_all()
						else:
												snake_case__       :					Union[str, Any]										=					torch.cuda.get_rng_state_all()
						if is_tpu_available():
												snake_case__       :					int										=					xm.get_rng_state()
						snake_case__       :					int										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
						torch.save(lowerCamelCase      ,			lowerCamelCase		)
						logger.info(f'''Random states saved in {output_states_file}'''		)
						return output_dir

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase=None      ,			lowerCamelCase=None      ,			**lowerCamelCase      ,			):
						'''simple docstring'''


						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:
												snake_case__       :					str										=					'''cpu'''
						elif map_location == "on_device":
												snake_case__       :					List[Any]										=					PartialState().device
						# Model states
						for i, model in enumerate(lowerCamelCase		):
												snake_case__       :					Optional[int]										=					f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
												snake_case__       :					int										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												models[i].load_state_dict(torch.load(lowerCamelCase      ,			map_location=lowerCamelCase		)      ,			**lowerCamelCase		)
						logger.info('''All model weights loaded successfully'''		)

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

						# Scheduler states
						for i, scheduler in enumerate(lowerCamelCase		):
												snake_case__       :					Optional[int]										=					f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
												snake_case__       :					Optional[Any]										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												scheduler.load_state_dict(torch.load(lowerCamelCase		)		)
						logger.info('''All scheduler states loaded successfully'''		)

						# GradScaler state
						if scaler is not None:
												snake_case__       :					Optional[Any]										=					os.path.join(lowerCamelCase      ,			lowerCamelCase		)
												scaler.load_state_dict(torch.load(lowerCamelCase		)		)
												logger.info('''GradScaler state loaded successfully'''		)

						# Random states
						try:
												snake_case__       :					Optional[int]										=					torch.load(os.path.join(lowerCamelCase      ,			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 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase = 0		):
						'''simple docstring'''


						snake_case__       :					Union[str, Any]										=					Path(lowerCamelCase		) / f'''custom_checkpoint_{index}.pkl'''
						logger.info(f'''Saving the state of {get_pretty_name(lowerCamelCase		)} to {save_location}'''		)
						torch.save(obj.state_dict()      ,			lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase = 0		):
						'''simple docstring'''


						snake_case__       :					Any										=					f'''{path}/custom_checkpoint_{index}.pkl'''
						logger.info(f'''Loading the state of {get_pretty_name(lowerCamelCase		)} from {load_location}'''		)
						obj.load_state_dict(torch.load(lowerCamelCase      ,			map_location='''cpu'''		)		)


