
import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


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




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	_UpperCAmelCase	:		Optional[Any]          = None






@experimental
def 	a_		(	__lowercase       :     int	, __lowercase       :     int	, __lowercase       :     Tuple	, __lowercase       :     Optional[int]	, __lowercase       :     Optional[int]	, __lowercase       :     List[Any]	, __lowercase       :     Optional[int]			)			->  Union[str, Any]:
			if ParallelBackendConfig.backend_name is None:
						return _map_with_multiprocessing_pool(
						    __lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase			)

			return _map_with_joblib(__lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     int	, __lowercase       :     int	, __lowercase       :     Union[str, Any]	, __lowercase       :     Union[str, Any]	, __lowercase       :     Optional[Any]	, __lowercase       :     Any	, __lowercase       :     Union[str, Any]			)			->  Tuple:
			_snake_case          =      num_proc if num_proc <= len(__lowercase			) else len(__lowercase			)
			_snake_case          =      []  # We organize the splits ourselve (contiguous splits)
			for index in range(__lowercase			):
						_snake_case          =      len(__lowercase			) // num_proc
						_snake_case          =      len(__lowercase			) % num_proc
						_snake_case          =      div * index + min(__lowercase	, __lowercase			)
						_snake_case          =      start + div + (1 if index < mod else 0)
						split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)			)

			if len(__lowercase			) != sum(len(i[1]			) for i in split_kwds			):
						raise ValueError(
						    f'''Error dividing inputs iterable among processes. '''
						    f'''Total number of objects {len(__lowercase			)}, '''
						    f'''length: {sum(len(i[1]			) for i in split_kwds			)}'''			)

			logger.info(
			    f'''Spawning {num_proc} processes for {len(__lowercase			)} objects in slices of {[len(i[1]			) for i in split_kwds]}'''			)
			_snake_case     , _snake_case          =      None, None
			if not disable_tqdm:
						_snake_case     , _snake_case          =      (RLock(),), tqdm.set_lock
			with Pool(__lowercase	, initargs=__lowercase	, initializer=__lowercase			) as pool:
						_snake_case          =      pool.map(__lowercase	, __lowercase			)
			logger.info(f'''Finished {num_proc} processes'''			)
			_snake_case          =      [obj for proc_res in mapped for obj in proc_res]
			logger.info(f'''Unpacked {len(__lowercase			)} objects'''			)

			return mapped






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[int]	, __lowercase       :     Dict	, __lowercase       :     Optional[int]	, __lowercase       :     Any	, __lowercase       :     List[Any]	, __lowercase       :     str			)			->  List[Any]:
			# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
			# and it requires monkey-patching joblib internal classes which is subject to change
			import joblib

			with joblib.parallel_backend(ParallelBackendConfig.backend_name	, n_jobs=__lowercase			):
						return joblib.Parallel()(
						    joblib.delayed(__lowercase			)((function, obj, types, None, True, None)			) for obj in iterable			)






@experimental
@contextlib.contextmanager
def 	a_		(	__lowercase       :     str			)			->  int:
			_snake_case          =      backend_name

			if backend_name == "spark":
						from joblibspark import register_spark

						register_spark()

						# TODO: call create_cache_and_write_probe if "download" in steps
						# TODO: raise NotImplementedError when Dataset.map etc is called

			try:
						yield
			finally:
						_snake_case          =      None

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


_lowerCamelCase	:    Tuple          =      logging.get_logger(__name__)

_lowerCamelCase	:    Union[str, Any]          =      {
    '''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        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "canine"



	def __init__(  self     :   int      ,  lowercase     :   Optional[int]=768      ,  lowercase     :   Tuple=12      ,  lowercase     :   Union[str, Any]=12      ,  lowercase     :   Optional[int]=3_072      ,  lowercase     :   Tuple="gelu"      ,  lowercase     :   Optional[Any]=0.1      ,  lowercase     :   Tuple=0.1      ,  lowercase     :   int=16_384      ,  lowercase     :   Optional[int]=16      ,  lowercase     :   Optional[int]=0.02      ,  lowercase     :   Optional[Any]=1E-12      ,  lowercase     :   Optional[Any]=0      ,  lowercase     :   Dict=0xE000      ,  lowercase     :   Optional[Any]=0xE001      ,  lowercase     :   Union[str, Any]=4      ,  lowercase     :   str=4      ,  lowercase     :   Optional[int]=8      ,  lowercase     :   List[str]=16_384      ,  lowercase     :   Union[str, Any]=128      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


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

				_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