import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A   :     Optional[int]       =				logging.get_logger(__name__)


class      lowerCamelCase    :
  """simple docstring"""







  lowerCamelCase__   =  None



@experimental
def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      if ParallelBackendConfig.backend_name is None:
            return _map_with_multiprocessing_pool(
                __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )

      return _map_with_joblib(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     num_proc if num_proc <= len(__UpperCamelCase ) else len(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     []  # We organize the splits ourselve (contiguous splits)
      for index in range(__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     len(__UpperCamelCase ) // num_proc
            SCREAMING_SNAKE_CASE_      =     len(__UpperCamelCase ) % num_proc
            SCREAMING_SNAKE_CASE_      =     div * index + min(__UpperCamelCase   ,					__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     start + div + (1 if index < mod else 0)
            split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )

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

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

      return mapped


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      # 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=__UpperCamelCase ):
            return joblib.Parallel()(
                joblib.delayed(__UpperCamelCase )((function, obj, types, None, True, None) ) for obj in iterable )





@experimental
@contextlib.contextmanager
def    a__       (						__UpperCamelCase ):
      SCREAMING_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:
            SCREAMING_SNAKE_CASE_      =     None



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


A   :     str       =				logging.get_logger(__name__)

# TODO: upload to AWS
A   :     Dict       =				{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        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_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


