"""simple docstring"""



from dataclasses import dataclass

_UpperCamelCase         =		"0.1"
_UpperCamelCase         =		"Lucia Harcekova"
@dataclass
class 	__UpperCAmelCase						:







			'''simple docstring'''
			_UpperCamelCase     :	int
			_UpperCamelCase     :	int
			_UpperCamelCase     :	str





			def __repr__( self       ):







										'''simple docstring'''


										return F'''({self.offset}, {self.length}, {self.indicator})'''





class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = 13      ,			snake_case_ = 6       ):







										'''simple docstring'''


										A__					:    str														= window_size
										A__					:    Optional[int]														= lookahead_buffer_size
										A__					:    Optional[Any]														= self.window_size - self.lookahead_buffer_size





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    str														= []
										A__					:    Any														= """"""

										# 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)
																	A__					:    Optional[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:
																								A__					:    Any														= search_buffer[-self.search_buffer_size :]

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

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

										return output





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= """"""

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

										return output





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


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

										# Initialise result parameters to default values
										A__							,       A__					:    List[Any]														= 0, 0

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

										for i, character in enumerate(snake_case_       ):
																	A__					:    Tuple														= len(snake_case_       ) - i
																	if character == text[0]:
																								A__					:    Tuple														= 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:
																															A__							,       A__					:    List[str]														= found_offset, found_length

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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''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         =		LZaaCompressor(window_size=13, lookahead_buffer_size=6)

				# Example
				_UpperCamelCase         =		"cabracadabrarrarrad"
				_UpperCamelCase         =		lzaa_compressor.compress(TEXT)
				print(lzaa_compressor.compress("ababcbababaa"))
				_UpperCamelCase         =		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


_UpperCamelCase         =		get_logger(__name__)

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = None ,     ):
							# Model states
							for i, state in enumerate(lowerCAmelCase       ):
														A__					:    Tuple														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= 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       ):
														A__					:    Tuple														= opt.state_dict()
														A__					:    Union[str, Any]														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Tuple														= 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       ):
														A__					:    List[str]														= scheduler.state_dict()
														A__					:    Dict														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[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:
														A__					:    Dict														= scaler.state_dict()
														A__					:    Optional[int]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														torch.save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''       )
							# Random number generator states
							A__					:    Tuple														= {}
							A__					:    Any														= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
							A__					:    Any														= random.getstate()
							A__					:    Any														= np.random.get_state()
							A__					:    int														= torch.get_rng_state()
							if is_xpu_available():
														A__					:    List[str]														= torch.xpu.get_rng_state_all()
							else:
														A__					:    Union[str, Any]														= torch.cuda.get_rng_state_all()
							if is_tpu_available():
														A__					:    str														= xm.get_rng_state()
							A__					:    str														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
							torch.save(lowerCAmelCase ,     lowerCAmelCase       )
							logger.info(F'''Random states saved in {output_states_file}'''       )
							return output_dir

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase=None ,     lowerCAmelCase=None ,     **lowerCAmelCase ,     ):
							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:
														A__					:    Union[str, Any]														= """cpu"""
							elif map_location == "on_device":
														A__					:    str														= PartialState().device
							# Model states
							for i, model in enumerate(lowerCAmelCase       ):
														A__					:    List[str]														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Optional[Any]														= 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       ):
														A__					:    str														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														A__					:    List[str]														= 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       ):
														A__					:    Any														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[int]														= 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:
														A__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														scaler.load_state_dict(torch.load(lowerCAmelCase       )       )
														logger.info("""GradScaler state loaded successfully"""       )

							# Random states
							try:
														A__					:    Union[str, Any]														= 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    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    int														= 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    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    List[str]														= 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"""       )       )





