

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 					_UpperCAmelCase       (  *_SCREAMING_SNAKE_CASE					:		List[str]       ):





				"""simple docstring"""


				if not isinstance(__a ,   __a       ):
								SCREAMING_SNAKE_CASE_									=						list(__a       )
				for i in range(len(__a       )       ):
								SCREAMING_SNAKE_CASE_									=						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       (  _SCREAMING_SNAKE_CASE					:		Exception       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						[
				    '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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		callable = None ,   _SCREAMING_SNAKE_CASE					:		int = 128       ):





				"""simple docstring"""


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

				SCREAMING_SNAKE_CASE_									=						starting_batch_size

				def decorator(*_SCREAMING_SNAKE_CASE					:		int ,   **_SCREAMING_SNAKE_CASE					:		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()
								SCREAMING_SNAKE_CASE_									=						list(inspect.signature(__a       ).parameters.keys()       )
								# Guard against user error
								if len(__a       ) < (len(__a       ) + 1):
												SCREAMING_SNAKE_CASE_									=						', '.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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__  :      str			=    logging.get_logger(__name__)

UpperCamelCase__  :      Optional[int]			=    {
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Optional[int]						 =  'dpr'

  def __init__(					self						,		_A=30522						,		_A=768						,		_A=12						,		_A=12						,		_A=3072						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=512						,		_A=2						,		_A=0.0_2						,		_A=1E-12						,		_A=0						,		_A="absolute"						,		_A = 0						,		**_A						,		):
      super().__init__(pad_token_id=_A						,		**_A)

      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						type_vocab_size
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						projection_dim
      SCREAMING_SNAKE_CASE_									=						position_embedding_type


