




import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


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







class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		None

@experimental
def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: int			,							lowercase			: Union[str, Any]			,							lowercase			: int			,							lowercase			: List[Any]			,							lowercase			: Optional[int]			,							lowercase			: str  ):


      '''simple docstring'''



      if ParallelBackendConfig.backend_name is None:
            return _map_with_multiprocessing_pool(
                lowercase			,							lowercase			,							lowercase			,							lowercase			,							lowercase			,							lowercase			,							lowercase  )

      return _map_with_joblib(lowercase			,							lowercase			,							lowercase			,							lowercase			,							lowercase			,							lowercase			,							lowercase  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: str			,							lowercase			: List[Any]			,							lowercase			: List[str]			,							lowercase			: Optional[int]			,							lowercase			: List[str]			,							lowercase			: List[Any]  ):


      '''simple docstring'''



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

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

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

      return mapped

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Optional[Any]			,							lowercase			: int			,							lowercase			: List[str]			,							lowercase			: Any			,							lowercase			: str			,							lowercase			: List[str]  ):


      '''simple docstring'''



      import joblib

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






@experimental
@contextlib.contextmanager
def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



      lowerCamelCase_	  =					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_	  =					None









import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      return EnvironmentCommand()







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @staticmethod
 def        a__				(       A_      :						ArgumentParser						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					parser.add_parser('env'						)
       download_parser.set_defaults(func=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					huggingface_hub.__version__

       lowerCamelCase_	  =					'not installed'
       lowerCamelCase_	  =					'NA'
       if is_torch_available():
             import torch

             lowerCamelCase_	  =					torch.__version__
             lowerCamelCase_	  =					torch.cuda.is_available()

       lowerCamelCase_	  =					'not installed'
       if is_transformers_available():
             import transformers

             lowerCamelCase_	  =					transformers.__version__

       lowerCamelCase_	  =					'not installed'
       if is_accelerate_available():
             import accelerate

             lowerCamelCase_	  =					accelerate.__version__

       lowerCamelCase_	  =					'not installed'
       if is_xformers_available():
             import xformers

             lowerCamelCase_	  =					xformers.__version__

       lowerCamelCase_	  =					{
           '`diffusers` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
           'Huggingface_hub version': hub_version,
           'Transformers version': transformers_version,
           'Accelerate version': accelerate_version,
           'xFormers version': xformers_version,
           'Using GPU in script?': '<fill in>',
           'Using distributed or parallel set-up in script?': '<fill in>',
       }

       print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'						)
       print(self.format_dict(A_						)						)

       return info





 @staticmethod
 def        a__				(       A_      :						Dict						)     ->			Any:

       """simple docstring"""






       return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"



