





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)

class    lowerCAmelCase							:
 UpperCAmelCase__													=		None


@experimental
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Any:
    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    lowerCamelCase__    :      Union[str, Any]					 =		num_proc if num_proc <= len(_UpperCAmelCase ) else len(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		[]  # We organize the splits ourselve (contiguous splits)
    for index in range(_UpperCAmelCase ):
        lowerCamelCase__    :      Dict					 =		len(_UpperCAmelCase ) // num_proc
        lowerCamelCase__    :      List[Any]					 =		len(_UpperCAmelCase ) % num_proc
        lowerCamelCase__    :      List[str]					 =		div * index + min(_UpperCAmelCase						,	_UpperCAmelCase )
        lowerCamelCase__    :      str					 =		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]}""" )
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		None, None
    if not disable_tqdm:
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		(RLock(),), tqdm.set_lock
    with Pool(_UpperCAmelCase						,	initargs=_UpperCAmelCase						,	initializer=_UpperCAmelCase ) as pool:
        lowerCamelCase__    :      Union[str, Any]					 =		pool.map(_UpperCAmelCase						,	_UpperCAmelCase )
    logger.info(F"""Finished {num_proc} processes""" )
    lowerCamelCase__    :      List[Any]					 =		[obj for proc_res in mapped for obj in proc_res]
    logger.info(F"""Unpacked {len(_UpperCAmelCase )} objects""" )

    return mapped


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Dict:
    # 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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[str]:
    lowerCamelCase__    :      List[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__    :      List[Any]					 =		None












import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""M-CLIP"""
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]=1024			,     UpperCAmelCase  :      Tuple=768			,     **UpperCAmelCase  :      Optional[int]	)			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		transformerDimSize
     lowerCamelCase__    :      Optional[Any]					 =		imageDimSize
     super().__init__(**UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		MCLIPConfig
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      Dict			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     super().__init__(UpperCAmelCase			,     *UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		XLMRobertaModel(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.nn.Linear(
         in_features=config.transformerDimensions			,     out_features=config.numDims	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Dict	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.transformer(input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase	)[0]
     lowerCamelCase__    :      int					 =		(embs * attention_mask.unsqueeze(2	)).sum(dim=1	) / attention_mask.sum(dim=1	)[:, None]
     return self.LinearTransformation(UpperCAmelCase	), embs





