


"""simple docstring"""
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available







def 				_a			( *_SCREAMING_SNAKE_CASE			)      ->			int:
							if not isinstance(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														snake_case_          =						list(_SCREAMING_SNAKE_CASE			)
							for i in range(len(_SCREAMING_SNAKE_CASE			)			):
														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 				_a			( _SCREAMING_SNAKE_CASE			)      ->			bool:
							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(_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 = 128			)      ->			Any:
							if function is None:
														return functools.partial(_SCREAMING_SNAKE_CASE			,	starting_batch_size=_SCREAMING_SNAKE_CASE			)

							snake_case_          =						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()
														snake_case_          =						list(inspect.signature(_SCREAMING_SNAKE_CASE			).parameters.keys()			)
														# Guard against user error
														if len(_SCREAMING_SNAKE_CASE			) < (len(_SCREAMING_SNAKE_CASE			) + 1):
																					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(_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


__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      int						=      {
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[str]							 =      """canine"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=768						,    UpperCAmelCase_							: Union[str, Any]=12						,    UpperCAmelCase_							: Optional[Any]=12						,    UpperCAmelCase_							: Optional[Any]=3_072						,    UpperCAmelCase_							: List[Any]="gelu"						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Any=0.1						,    UpperCAmelCase_							: List[str]=16_384						,    UpperCAmelCase_							: Tuple=16						,    UpperCAmelCase_							: int=0.02						,    UpperCAmelCase_							: Tuple=1E-12						,    UpperCAmelCase_							: str=0						,    UpperCAmelCase_							: int=0XE000						,    UpperCAmelCase_							: Optional[int]=0XE001						,    UpperCAmelCase_							: Dict=4						,    UpperCAmelCase_							: List[Any]=4						,    UpperCAmelCase_							: List[Any]=8						,    UpperCAmelCase_							: Dict=16_384						,    UpperCAmelCase_							: Optional[int]=128						,    **UpperCAmelCase_							: Any						,    )   ->int:



														"""simple docstring"""



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

														snake_case_          =						max_position_embeddings
														snake_case_          =						hidden_size
														snake_case_          =						num_hidden_layers
														snake_case_          =						num_attention_heads
														snake_case_          =						intermediate_size
														snake_case_          =						hidden_act
														snake_case_          =						hidden_dropout_prob
														snake_case_          =						attention_probs_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						type_vocab_size
														snake_case_          =						layer_norm_eps

														# Character config:
														snake_case_          =						downsampling_rate
														snake_case_          =						upsampling_kernel_size
														snake_case_          =						num_hash_functions
														snake_case_          =						num_hash_buckets
														snake_case_          =						local_transformer_stride



