




'''simple docstring'''







import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
      import torch_xla.core.xla_model as xm  # noqa: F401

      lowercase			:      Any			=					True
except ImportError:
      lowercase			:      Optional[int]			=					False


# Cache this result has it's a C FFI call which can be pretty time-consuming
lowercase			:      int			=					torch.distributed.is_available()



def 					lowerCamelCase__							(			__lowercase							):
     # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
     snake_case :			Any					   = importlib.util.find_spec(__lowercase							) is not None
     if package_exists:
          try:
               snake_case :			List[str]					   = importlib.metadata.metadata(__lowercase							)
               return True
          except importlib.metadata.PackageNotFoundError:
               return False



def 					lowerCamelCase__							(			):
     return _torch_distributed_available



def 					lowerCamelCase__							(			):
     try:
          pass
     except ImportError:
          print(
              """Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"""
              """ detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"""
              """ Bindings for PyTorch*."""							)
     return (
         importlib.util.find_spec("""torch_ccl"""							) is not None
         or importlib.util.find_spec("""oneccl_bindings_for_pytorch"""							) is not None
     )



def 					lowerCamelCase__							(			):
     return importlib.metadata.version("""oneccl_bind_pt"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""transformer_engine"""							)



@lru_cache
def 					lowerCamelCase__							(			__lowercase=True							):
     # Due to bugs on the amp series GPUs, we disable torch-xla on them
     if torch.cuda.is_available():
          return False
     if _tpu_available and check_device:
          try:
               # Will raise a RuntimeError if no XLA configuration is found
               snake_case :			List[Any]					   = xm.xla_device()
               return True
          except RuntimeError:
               return False
     return _tpu_available



def 					lowerCamelCase__							(			):
     return _is_package_available("""deepspeed"""							)



def 					lowerCamelCase__							(			__lowercase=False							):
     if is_tpu_available():
          return not ignore_tpu
     if torch.cuda.is_available():
          return torch.cuda.is_bfaa_supported()
     if is_npu_available():
          return False
     return True



def 					lowerCamelCase__							(			):
     snake_case :			Union[str, Any]					   = _is_package_available("""bitsandbytes"""							)
     if package_exists:
          snake_case :			Optional[Any]					   = version.parse(importlib.metadata.version("""bitsandbytes"""							)							)
          return compare_versions(__lowercase    ,      """>="""    ,      """0.39.0"""							)
     return False



def 					lowerCamelCase__							(			):
     snake_case :			str					   = _is_package_available("""bitsandbytes"""							)
     if package_exists:
          snake_case :			Any					   = version.parse(importlib.metadata.version("""bitsandbytes"""							)							)
          return compare_versions(__lowercase    ,      """>="""    ,      """0.37.2"""							)
     return False



def 					lowerCamelCase__							(			):
     return _is_package_available("""bitsandbytes"""							)



def 					lowerCamelCase__							(			):
     if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""    ,      """False"""							)							) == 1:
          snake_case :			int					   = importlib.util.find_spec("""megatron"""							) is not None
          if package_exists:
               try:
                    snake_case :			str					   = parse(importlib.metadata.version("""megatron-lm"""							)							)
                    return compare_versions(__lowercase    ,      """>="""    ,      """2.2.0"""							)
               except Exception as e:
                    warnings.warn(F'''Parse Megatron version failed. Exception:{e}'''							)
                    return False



def 					lowerCamelCase__							(			):
     return _is_package_available("""safetensors"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""transformers"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""datasets"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""aim"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""tensorboard"""							) or _is_package_available("""tensorboardX"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""wandb"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""comet_ml"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""boto3"""							)



def 					lowerCamelCase__							(			):
     if _is_package_available("""rich"""							):
          if "ACCELERATE_DISABLE_RICH" in os.environ:
               warnings.warn(
                   """`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."""							)
               return not parse_flag_from_env("""ACCELERATE_DISABLE_RICH"""    ,      __lowercase							)
          return parse_flag_from_env("""ACCELERATE_ENABLE_RICH"""    ,      __lowercase							)
     return False



def 					lowerCamelCase__							(			):
     return _is_package_available("""sagemaker"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""tqdm"""							)



def 					lowerCamelCase__							(			):
     return _is_package_available("""mlflow"""							)



def 					lowerCamelCase__							(			):
     return is_torch_version(""">="""    ,      """1.12"""							) and torch.backends.mps.is_available() and torch.backends.mps.is_built()



def 					lowerCamelCase__							(			):
     def get_major_and_minor_from_version(__lowercase							):
          return str(version.parse(__lowercase							).major							) + "." + str(version.parse(__lowercase							).minor							)

     snake_case :			Any					   = importlib.metadata.version("""torch"""							)
     if importlib.util.find_spec("""intel_extension_for_pytorch"""							) is None:
          return False
     snake_case :			Optional[int]					   = """N/A"""
     try:
          snake_case :			str					   = importlib.metadata.version("""intel_extension_for_pytorch"""							)
     except importlib.metadata.PackageNotFoundError:
          return False
     snake_case :			str					   = get_major_and_minor_from_version(_torch_version							)
     snake_case :			int					   = get_major_and_minor_from_version(_ipex_version							)
     if torch_major_and_minor != ipex_major_and_minor:
          warnings.warn(
              F'''Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'''
              F''' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'''							)
          return False
     return True



@lru_cache
def 					lowerCamelCase__							(			__lowercase=False							):
     if importlib.util.find_spec("""torch"""							) is None or importlib.util.find_spec("""torch_npu"""							) is None:
          return False

     import torch
     import torch_npu  # noqa: F401

     if check_device:
          try:
               # Will raise a RuntimeError if no NPU is found
               snake_case :			List[Any]					   = torch.npu.device_count()
               return torch.npu.is_available()
          except RuntimeError:
               return False
     return hasattr(__lowercase    ,      """npu"""							) and torch.npu.is_available()







@lru_cache
def 					lowerCamelCase__							(			__lowercase=False							):
     if not parse_flag_from_env("""ACCELERATE_USE_XPU"""    ,      default=__lowercase							):
          return False
     if is_ipex_available():
          import torch

          if is_torch_version("""<="""    ,      """1.12"""							):
               return False
     else:
          return False

     import intel_extension_for_pytorch  # noqa: F401

     if check_device:
          try:
               # Will raise a RuntimeError if no XPU  is found
               snake_case :			int					   = torch.xpu.device_count()
               return torch.xpu.is_available()
          except RuntimeError:
               return False
     return hasattr(__lowercase    ,      """xpu"""							) and torch.xpu.is_available()









'''simple docstring'''







from dataclasses import dataclass

lowercase			:      Tuple			=					"""0.1"""
lowercase			:      Optional[Any]			=					"""Lucia Harcekova"""







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    str







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







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a = 13       ,__a = 6       )			->							None:
          snake_case :			Dict					   = window_size
          snake_case :			Any					   = lookahead_buffer_size
          snake_case :			str					   = self.window_size - self.lookahead_buffer_size







     def  snake_case_			(							self       ,__a       )			->							list[Token]:
          snake_case :			Tuple					   = []
          snake_case :			Optional[int]					   = """"""

          # 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 :			str					   = 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:
                    snake_case :			List[Any]					   = search_buffer[-self.search_buffer_size :]

               # update the text
               snake_case :			str					   = text[token.length + 1 :]

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

          return output







     def  snake_case_			(							self       ,__a       )			->							str:
          snake_case :			Union[str, Any]					   = """"""

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

          return output







     def  snake_case_			(							self       ,__a       ,__a       )			->							Token:

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

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

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

          for i, character in enumerate(__a       ):
               snake_case :			Tuple					   = len(__a       ) - i
               if character == text[0]:
                    snake_case :			List[Any]					   = 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:
                         snake_case   ,		snake_case :			Optional[Any]					   = found_offset, found_length

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







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       )			->							int:
          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			:      int			=					LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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



