





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available





def      _UpperCamelCase  (*a__ :Tuple							):






							"""simple docstring"""





							if not isinstance(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_							):
														UpperCamelCase__					  =      list(SCREAMING_SNAKE_CASE_							)
							for i in range(len(SCREAMING_SNAKE_CASE_							)							):
														UpperCamelCase__					  =      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      _UpperCamelCase  (a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      [
							    """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      _UpperCamelCase  (a__ :str = None ,      a__ :Optional[int] = 128							):






							"""simple docstring"""





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

							UpperCamelCase__					  =      starting_batch_size

							def decorator(*a__ :Optional[Any] ,      **a__ :List[str]							):
														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()
														UpperCamelCase__					  =      list(inspect.signature(SCREAMING_SNAKE_CASE_							).parameters.keys()							)
														# Guard against user error
														if len(SCREAMING_SNAKE_CASE_							) < (len(SCREAMING_SNAKE_CASE_							) + 1):
																					UpperCamelCase__					  =      """, """.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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


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

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

