'''simple docstring'''







import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


UpperCamelCase_      	=	logging.get_logger(__name__)




class _SCREAMING_SNAKE_CASE:
						A_			:							List[Any]     		= None


@experimental
def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  Any				,						UpperCAmelCase__				:  Optional[int]		)			->	List[Any]:


							'''simple docstring'''






							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    lowerCamelCase       (			UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  Optional[Any]				,						UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  List[Any]		)			->	Optional[Any]:


							'''simple docstring'''






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

							return mapped


def    lowerCamelCase       (			UpperCAmelCase__				:  Tuple				,						UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  Any				,						UpperCAmelCase__				:  int		)			->	List[str]:


							'''simple docstring'''






							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    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	Dict:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = 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__     :Optional[Any]						    = None







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_      	=	{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BlipImageProcessor''']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BlipModel''',
		    '''BlipPreTrainedModel''',
		    '''BlipForConditionalGeneration''',
		    '''BlipForQuestionAnswering''',
		    '''BlipVisionModel''',
		    '''BlipTextModel''',
		    '''BlipForImageTextRetrieval''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFBlipModel''',
		    '''TFBlipPreTrainedModel''',
		    '''TFBlipForConditionalGeneration''',
		    '''TFBlipForQuestionAnswering''',
		    '''TFBlipVisionModel''',
		    '''TFBlipTextModel''',
		    '''TFBlipForImageTextRetrieval''',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






