


import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


snake_case__				:	Optional[Any]					       =					logging.get_logger(__name__)





class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							None







@experimental
def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: int						,						A__						: Dict						,						A__						: str						,						A__						: Tuple						,						A__						: int						,						A__						: str					)     ->    List[Any]:
   if ParallelBackendConfig.backend_name is None:
      return _map_with_multiprocessing_pool(
          __A						,						__A						,						__A						,						__A						,						__A						,						__A						,						__A					)

   return _map_with_joblib(__A						,						__A						,						__A						,						__A						,						__A						,						__A						,						__A					)


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

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

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

   return mapped


def    __lowerCamelCase   (      A__						: List[str]						,						A__						: int						,						A__						: Union[str, Any]						,						A__						: Optional[Any]						,						A__						: str						,						A__						: Optional[int]						,						A__						: int					)     ->    Union[str, Any]:
   import joblib

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



@experimental
@contextlib.contextmanager
def    __lowerCamelCase   (      A__						: str					)     ->    Optional[int]:
   lowerCamelCase_						:   Optional[Any]		= 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:
      lowerCamelCase_						:   Any		= None










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_						:    Any				=      logging.get_logger(__name__)

A_						:    Optional[int]				=      {
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= '''xmod'''





	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple=30_522     ,       _SCREAMING_SNAKE_CASE				:       Tuple=768     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=12     ,       _SCREAMING_SNAKE_CASE				:       List[str]=12     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=3_072     ,       _SCREAMING_SNAKE_CASE				:       Any="gelu"     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=0.1     ,       _SCREAMING_SNAKE_CASE				:       Dict=0.1     ,       _SCREAMING_SNAKE_CASE				:       str=512     ,       _SCREAMING_SNAKE_CASE				:       Tuple=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=1E-1_2     ,       _SCREAMING_SNAKE_CASE				:       List[str]=1     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]="absolute"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       Dict=2     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       int=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Tuple=("en_XX",)     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]     ,       )						->	List[str]:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE						:							Any							=     vocab_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							str							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							List[Any]							=     hidden_act
					SCREAMING_SNAKE_CASE						:							Any							=     intermediate_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							str							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							str							=     type_vocab_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     initializer_range
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     layer_norm_eps
					SCREAMING_SNAKE_CASE						:							Tuple							=     position_embedding_type
					SCREAMING_SNAKE_CASE						:							int							=     use_cache
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     classifier_dropout
					SCREAMING_SNAKE_CASE						:							List[Any]							=     pre_norm
					SCREAMING_SNAKE_CASE						:							int							=     adapter_reduction_factor
					SCREAMING_SNAKE_CASE						:							List[Any]							=     adapter_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     adapter_reuse_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     ln_before_adapter
					SCREAMING_SNAKE_CASE						:							Tuple							=     list(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     default_language




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@property
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Mapping[str, Mapping[int, str]]:





					"""simple docstring"""


					if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     {0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
									SCREAMING_SNAKE_CASE						:							List[str]							=     {0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ]    )





