






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

   A_	:	int				=				True
except ImportError:
   A_	:	int				=				False


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

def 						snake_case      (UpperCAmelCase__						) ->       Dict:

  '''simple docstring'''





  UpperCamelCase_:      List[str]					   =       importlib.util.find_spec(UpperCAmelCase__						) is not None
  if package_exists:
    try:
      UpperCamelCase_:      Dict					   =       importlib.metadata.metadata(UpperCAmelCase__						)
      return True
    except importlib.metadata.PackageNotFoundError:
      return False

def 						snake_case      () ->       bool:

  '''simple docstring'''





  return _torch_distributed_available

def 						snake_case      () ->       List[Any]:

  '''simple docstring'''





  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 						snake_case      () ->       str:

  '''simple docstring'''





  return importlib.metadata.version('oneccl_bind_pt'						)

def 						snake_case      () ->       str:

  '''simple docstring'''





  return _is_package_available('transformer_engine'						)

@lru_cache
def 						snake_case      (UpperCAmelCase__=True						) ->       List[str]:

  '''simple docstring'''





  if torch.cuda.is_available():
    return False
  if _tpu_available and check_device:
    try:
      # Will raise a RuntimeError if no XLA configuration is found
      UpperCamelCase_:      Tuple					   =       xm.xla_device()
      return True
    except RuntimeError:
      return False
  return _tpu_available

def 						snake_case      () ->       int:

  '''simple docstring'''





  return _is_package_available('deepspeed'						)

def 						snake_case      (UpperCAmelCase__=False						) ->       int:

  '''simple docstring'''





  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 						snake_case      () ->       List[Any]:

  '''simple docstring'''





  UpperCamelCase_:      Optional[int]					   =       _is_package_available('bitsandbytes'						)
  if package_exists:
    UpperCamelCase_:      int					   =       version.parse(importlib.metadata.version('bitsandbytes'						)						)
    return compare_versions(UpperCAmelCase__			,					'>='			,					'0.39.0'						)
  return False

def 						snake_case      () ->       str:

  '''simple docstring'''





  UpperCamelCase_:      Optional[Any]					   =       _is_package_available('bitsandbytes'						)
  if package_exists:
    UpperCamelCase_:      Tuple					   =       version.parse(importlib.metadata.version('bitsandbytes'						)						)
    return compare_versions(UpperCAmelCase__			,					'>='			,					'0.37.2'						)
  return False

def 						snake_case      () ->       Dict:

  '''simple docstring'''





  return _is_package_available('bitsandbytes'						)

def 						snake_case      () ->       Dict:

  '''simple docstring'''





  if strtobool(os.environ.get('ACCELERATE_USE_MEGATRON_LM'			,					'False'						)						) == 1:
    UpperCamelCase_:      Union[str, Any]					   =       importlib.util.find_spec('megatron'						) is not None
    if package_exists:
      try:
        UpperCamelCase_:      Optional[Any]					   =       parse(importlib.metadata.version('megatron-lm'						)						)
        return compare_versions(UpperCAmelCase__			,					'>='			,					'2.2.0'						)
      except Exception as e:
        warnings.warn(F'''Parse Megatron version failed. Exception:{e}'''						)
        return False

def 						snake_case      () ->       str:

  '''simple docstring'''





  return _is_package_available('safetensors'						)

def 						snake_case      () ->       Union[str, Any]:

  '''simple docstring'''





  return _is_package_available('transformers'						)

def 						snake_case      () ->       str:

  '''simple docstring'''





  return _is_package_available('datasets'						)

def 						snake_case      () ->       str:

  '''simple docstring'''





  return _is_package_available('aim'						)

def 						snake_case      () ->       Union[str, Any]:

  '''simple docstring'''





  return _is_package_available('tensorboard'						) or _is_package_available('tensorboardX'						)

def 						snake_case      () ->       Optional[Any]:

  '''simple docstring'''





  return _is_package_available('wandb'						)

def 						snake_case      () ->       str:

  '''simple docstring'''





  return _is_package_available('comet_ml'						)

def 						snake_case      () ->       int:

  '''simple docstring'''





  return _is_package_available('boto3'						)

def 						snake_case      () ->       Union[str, Any]:

  '''simple docstring'''





  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'			,					UpperCAmelCase__						)
    return parse_flag_from_env('ACCELERATE_ENABLE_RICH'			,					UpperCAmelCase__						)
  return False

def 						snake_case      () ->       str:

  '''simple docstring'''





  return _is_package_available('sagemaker'						)

def 						snake_case      () ->       str:

  '''simple docstring'''





  return _is_package_available('tqdm'						)

def 						snake_case      () ->       List[str]:

  '''simple docstring'''





  return _is_package_available('mlflow'						)

def 						snake_case      () ->       Optional[Any]:

  '''simple docstring'''





  return is_torch_version('>='			,					'1.12'						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()

def 						snake_case      () ->       int:

  '''simple docstring'''





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

  UpperCamelCase_:      List[str]					   =       importlib.metadata.version('torch'						)
  if importlib.util.find_spec('intel_extension_for_pytorch'						) is None:
    return False
  UpperCamelCase_:      Optional[int]					   =       'N/A'
  try:
    UpperCamelCase_:      Any					   =       importlib.metadata.version('intel_extension_for_pytorch'						)
  except importlib.metadata.PackageNotFoundError:
    return False
  UpperCamelCase_:      Dict					   =       get_major_and_minor_from_version(_torch_version						)
  UpperCamelCase_:      str					   =       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 						snake_case      (UpperCAmelCase__=False						) ->       List[Any]:

  '''simple docstring'''





  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
      UpperCamelCase_:      int					   =       torch.npu.device_count()
      return torch.npu.is_available()
    except RuntimeError:
      return False
  return hasattr(UpperCAmelCase__			,					'npu'						) and torch.npu.is_available()

@lru_cache
def 						snake_case      (UpperCAmelCase__=False						) ->       Dict:

  '''simple docstring'''





  if not parse_flag_from_env('ACCELERATE_USE_XPU'			,					default=UpperCAmelCase__						):
    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
      UpperCamelCase_:      str					   =       torch.xpu.device_count()
      return torch.xpu.is_available()
    except RuntimeError:
      return False
  return hasattr(UpperCAmelCase__			,					'xpu'						) and torch.xpu.is_available()







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_reformer import ReformerTokenizer
else:
   A_	:	Union[str, Any]				=				None


A_	:	List[Any]				=				logging.get_logger(__name__)


A_	:	List[str]				=				'▁'

A_	:	int				=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A_	:	Optional[Any]				=				{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

A_	:	List[Any]				=				{
    'google/reformer-crime-and-punishment': 524288,
}




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	int								=VOCAB_FILES_NAMES
       a       :	Optional[Any]								=PRETRAINED_VOCAB_FILES_MAP
       a       :	List[Any]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a       :	Tuple								=['''input_ids''', '''attention_mask''']
       a       :	Optional[Any]								=ReformerTokenizer





       def __init__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase="</s>"						,    _lowerCamelCase="<unk>"						,    _lowerCamelCase=[]						,    **_lowerCamelCase						,    ):
         super().__init__(
             _lowerCamelCase						,    tokenizer_file=_lowerCamelCase						,    eos_token=_lowerCamelCase						,    unk_token=_lowerCamelCase						,    additional_special_tokens=_lowerCamelCase						,    **_lowerCamelCase						,    )

         UpperCamelCase_:      Any					   =       vocab_file
         UpperCamelCase_:      str					   =       False if not self.vocab_file else True





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase = None							):
         if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'							)

         if not os.path.isdir(_lowerCamelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         UpperCamelCase_:      str					   =       os.path.join(
             _lowerCamelCase						,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
           copyfile(self.vocab_file						,    _lowerCamelCase							)

         return (out_vocab_file,)