


import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
a_ :Tuple  							=			object()

# For specifying empty leaf dict `{}`
a_ :str  							=			object()




def    a   (							A__      ,    A__							)	-> Optional[int]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		tuple((re.compile(x + '''$'''							) for x in qs)							)
      for i in range(len(A__							) - len(A__							) + 1							):
            SCREAMING_SNAKE_CASE__       :  str						     =		[x.match(A__							) for x, y in zip(A__      ,    ks[i:]							)]
            if matches and all(A__							):
                  return True
      return False




def    a   (							A__							)	-> Dict:




      '''simple docstring'''

      def replace(A__      ,    A__							):
            for rule, replacement in rules:
                  if _match(A__      ,    A__							):
                        return replacement
            return val

      return replace




def    a   (							)	-> Optional[int]:




      '''simple docstring'''

      return [
          # embeddings
          (("transformer", "wpe", "embedding"), P('''mp'''      ,    A__							)),
          (("transformer", "wte", "embedding"), P('''mp'''      ,    A__							)),
          # atention
          (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(A__      ,    '''mp'''							)),
          (("attention", "out_proj", "kernel"), P('''mp'''      ,    A__							)),
          (("attention", "out_proj", "bias"), None),
          # mlp
          (("mlp", "c_fc", "kernel"), P(A__      ,    '''mp'''							)),
          (("mlp", "c_fc", "bias"), P('''mp'''							)),
          (("mlp", "c_proj", "kernel"), P('''mp'''      ,    A__							)),
          (("mlp", "c_proj", "bias"), None),
          # layer norms
          ((r"ln_\d+", "bias"), None),
          ((r"\d+", r"ln_\d+", "scale"), None),
          (("ln_f", "bias"), None),
          (("ln_f", "scale"), None),
      ]





def    a   (							A__							)	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  int						     =		_get_partition_rules()
      SCREAMING_SNAKE_CASE__       :  Dict						     =		_replacement_rules(A__							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		{k: _unmatched for k in flatten_dict(A__							)}
      SCREAMING_SNAKE_CASE__       :  str						     =		{k: replace(A__      ,    A__							) for k, v in initd.items()}
      assert _unmatched not in result.values(), "Incomplete partition spec."
      return freeze(unflatten_dict(A__							)							)









"""simple docstring"""


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def   _A					(	*_a	:						int				):



      """simple docstring"""






      if not isinstance(_a     ,   _a				):
            A				  =		list(_a				)
      for i in range(len(_a				)				):
            A				  =		None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects



def   _A					(	_a	:						Exception				):



      """simple docstring"""






      A				  =		[
          """CUDA out of memory.""",  # CUDA OOM
          """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
          """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
      ]
      if isinstance(_a     ,   _a				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False





def   _A					(	_a	:						callable = None     ,   _a	:						int = 1_2_8				):



      """simple docstring"""






      if function is None:
            return functools.partial(_a     ,   starting_batch_size=_a				)

      A				  =		starting_batch_size

      def decorator(*_a	:						Union[str, Any]     ,   **_a	:						List[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            A				  =		list(inspect.signature(_a				).parameters.keys()				)
            # Guard against user error
            if len(_a				) < (len(_a				) + 1):
                  A				  =		""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]     ,   args[1:]				)]				)
                  raise TypeError(
                      f'Batch size was passed into `{function.__name__}` as the first argument when called.'
                      f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError("""No executable batch size found, reached zero."""				)
                  try:
                        return function(_a     ,   *_a     ,   **_a				)
                  except Exception as e:
                        if should_reduce_batch_size(_a				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator




