'''simple docstring'''


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available

def   A_      (       *SCREAMING_SNAKE_CASE_						)			->List[Any]:
  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       list(SCREAMING_SNAKE_CASE_						)
  for i in range(len(SCREAMING_SNAKE_CASE_						)						):
    lowercase_     =       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_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  lowercase_     =       [
      """CUDA out of memory.""",  # CUDA OOM
      """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
      """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
  ]
  if isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) and len(exception.args						) == 1:
    return any(err in exception.args[0] for err in _statements						)
  return False




def   A_      (       SCREAMING_SNAKE_CASE_ = None ,	SCREAMING_SNAKE_CASE_ = 1_28						)			->Tuple:
  if function is None:
    return functools.partial(SCREAMING_SNAKE_CASE_ ,	starting_batch_size=SCREAMING_SNAKE_CASE_						)

  lowercase_     =       starting_batch_size

  def decorator(*SCREAMING_SNAKE_CASE_ ,	**SCREAMING_SNAKE_CASE_						):
    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()
    lowercase_     =       list(inspect.signature(SCREAMING_SNAKE_CASE_						).parameters.keys()						)
    # Guard against user error
    if len(SCREAMING_SNAKE_CASE_						) < (len(SCREAMING_SNAKE_CASE_						) + 1):
      lowercase_     =       """, """.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(SCREAMING_SNAKE_CASE_ ,	*SCREAMING_SNAKE_CASE_ ,	**SCREAMING_SNAKE_CASE_						)
      except Exception as e:
        if should_reduce_batch_size(SCREAMING_SNAKE_CASE_						):
          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


'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''camembert'''




  def __init__(		self      :       Union[str, Any]   ,				lowercase_      :       Union[str, Any]=30_522   ,				lowercase_      :       Optional[Any]=768   ,				lowercase_      :       Tuple=12   ,				lowercase_      :       Dict=12   ,				lowercase_      :       Tuple=3_072   ,				lowercase_      :       Optional[Any]="gelu"   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       List[Any]=512   ,				lowercase_      :       Optional[int]=2   ,				lowercase_      :       str=0.0_2   ,				lowercase_      :       int=1e-12   ,				lowercase_      :       str=1   ,				lowercase_      :       List[str]=0   ,				lowercase_      :       int=2   ,				lowercase_      :       Union[str, Any]="absolute"   ,				lowercase_      :       Optional[Any]=True   ,				lowercase_      :       List[Any]=None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       hidden_act
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       max_position_embeddings
    lowercase_     =       type_vocab_size
    lowercase_     =       initializer_range
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       use_cache
    lowercase_     =       classifier_dropout






class       _a    (		__a      ):


  """simple docstring"""

  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    if self.task == "multiple-choice":
      lowercase_     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
      lowercase_     =       {0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
        ]      )

