

import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_lowerCamelCase      :					str					    =      logging.get_logger(__name__)





class        lowerCAmelCase__  :



       '''simple docstring'''







       lowercase_						=    None





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

      return mapped





def      A__					(			__A  :						Optional[Any]     ,				__A  :						int     ,				__A  :						Optional[int]     ,				__A  :						Optional[Any]     ,				__A  :						str     ,				__A  :						Optional[Any]     ,				__A  :						str			)					->str:
      # 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=__A			):
            return joblib.Parallel()(
                joblib.delayed(__A			)((function, obj, types, None, True, None)			) for obj in iterable			)





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








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

