"""simple docstring"""




import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def        lowerCAmelCase_			(					*__A							)					->    List[Any]:







      '''simple docstring'''


      if not isinstance(__A,  __A							):
            UpperCAmelCase__								=  list(__A							)
      for i in range(len(__A							)							):
            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        lowerCAmelCase_			(					__A							)					->    bool:







      '''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(__A,  __A							) and len(exception.args							) == 1:
            return any(err in exception.args[0] for err in _statements							)
      return False







def        lowerCAmelCase_			(					__A = None,  __A = 128							)					->    Tuple:







      '''simple docstring'''


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

      UpperCAmelCase__								=  starting_batch_size

      def decorator(*__A,  **__A							):
            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(__A							).parameters.keys()							)
            # Guard against user error
            if len(__A							) < (len(__A							) + 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(__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




from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Optional[int]              =      'xlm-prophetnet'
   __UpperCAmelCase			:			int              =      ['past_key_values']
   __UpperCAmelCase			:			Optional[int]              =      {
       'num_attention_heads': 'num_encoder_attention_heads',
   }


   def __init__(self					:						str	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[Union[str, Callable]] = "gelu"	,  __UpperCAmelCase					:						Optional[int] = 3_0_5_2_2	,  __UpperCAmelCase					:						Optional[int] = 1_0_2_4	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[int] = 5_1_2	,  __UpperCAmelCase					:						Optional[float] = 0.02	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 2	,  __UpperCAmelCase					:						Optional[int] = 3_2	,  __UpperCAmelCase					:						Optional[int] = 1_2_8	,  __UpperCAmelCase					:						Optional[bool] = False	,  __UpperCAmelCase					:						Optional[float] = 0.0	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						Optional[int] = 2	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  encoder_ffn_dim
         UpperCAmelCase__								=  num_encoder_layers
         UpperCAmelCase__								=  num_encoder_attention_heads
         UpperCAmelCase__								=  decoder_ffn_dim
         UpperCAmelCase__								=  num_decoder_layers
         UpperCAmelCase__								=  num_decoder_attention_heads
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  init_std  # Normal(0, this parameter)
         UpperCAmelCase__								=  activation_function

         # parameters for xlmprophetnet
         UpperCAmelCase__								=  ngram
         UpperCAmelCase__								=  num_buckets
         UpperCAmelCase__								=  relative_max_distance
         UpperCAmelCase__								=  disable_ngram_loss
         UpperCAmelCase__								=  eps

         # 3 Types of Dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  dropout

         UpperCAmelCase__								=  use_cache

         super().__init__(
             pad_token_id=__UpperCAmelCase	,  bos_token_id=__UpperCAmelCase	,  eos_token_id=__UpperCAmelCase	,  is_encoder_decoder=__UpperCAmelCase	,  add_cross_attention=__UpperCAmelCase	,  decoder_start_token_id=__UpperCAmelCase	,  **__UpperCAmelCase	,  )


   @property
   def       lowercase_				(self					:						int		)						->    int:
         """simple docstring"""



         return self.num_encoder_layers + self.num_decoder_layers





   @num_hidden_layers.setter
   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         raise NotImplementedError(
             "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
             " `num_decoder_layers`."		)



