

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand

def 		UpperCamelCase   (lowercase_:					Dict   )	-> str:
	return EnvironmentCommand()

def 		UpperCamelCase   (lowercase_:					str   )	-> Union[str, Any]:
	return EnvironmentCommand(args.accelerate_config_file   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					@staticmethod
					def     __A     (					A__     ):
						A__				:       Union[str, Any]	  =						parser.add_parser("""env"""     )
						download_parser.set_defaults(func=A__     )
						download_parser.add_argument(
						    """--accelerate-config_file"""       ,			default=A__       ,			help="""The accelerate config file to use for the default values in the launching script."""       ,			)
						download_parser.set_defaults(func=A__     )






					def __init__(					self       ,			A__       ,			*A__     ):
						A__				:       int	  =						accelerate_config_file






					def     __A     (					self     ):
						A__				:       Any	  =						"""not installed"""
						if is_safetensors_available():
							import safetensors

							A__				:       Optional[int]	  =						safetensors.__version__
						elif importlib.util.find_spec("""safetensors"""     ) is not None:
							import safetensors

							A__				:       Union[str, Any]	  =						F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

						A__				:       Union[str, Any]	  =						"""not installed"""
						A__				:       str	  =						"""not found"""
						if is_accelerate_available():
							import accelerate
							from accelerate.commands.config import default_config_file, load_config_from_file

							A__				:       str	  =						accelerate.__version__
							# Get the default from the config file.
							if self._accelerate_config_file is not None or os.path.isfile(A__     ):
								A__				:       Union[str, Any]	  =						load_config_from_file(self._accelerate_config_file     ).to_dict()

							A__				:       List[Any]	  =						(
							    """\n""".join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]     )
							    if isinstance(A__       ,			A__     )
							    else F"""\t{accelerate_config}"""
							)

						A__				:       int	  =						"""not installed"""
						A__				:       int	  =						"""NA"""
						if is_torch_available():
							import torch

							A__				:       int	  =						torch.__version__
							A__				:       Optional[Any]	  =						torch.cuda.is_available()

						A__				:       int	  =						"""not installed"""
						A__				:       List[str]	  =						"""NA"""
						if is_tf_available():
							import tensorflow as tf

							A__				:       Optional[int]	  =						tf.__version__
							try:
								# deprecated in v2.1
								A__				:       List[Any]	  =						tf.test.is_gpu_available()
							except AttributeError:
								# returns list of devices, convert to bool
								A__				:       List[Any]	  =						bool(tf.config.list_physical_devices("""GPU"""     )     )

						A__				:       Tuple	  =						"""not installed"""
						A__				:       Any	  =						"""not installed"""
						A__				:       List[Any]	  =						"""not installed"""
						A__				:       List[Any]	  =						"""NA"""
						if is_flax_available():
							import flax
							import jax
							import jaxlib

							A__				:       Any	  =						flax.__version__
							A__				:       Optional[Any]	  =						jax.__version__
							A__				:       Union[str, Any]	  =						jaxlib.__version__
							A__				:       str	  =						jax.lib.xla_bridge.get_backend().platform

						A__				:       Tuple	  =						{
						    """`transformers` version""": version,
						    """Platform""": platform.platform(),
						    """Python version""": platform.python_version(),
						    """Huggingface_hub version""": huggingface_hub.__version__,
						    """Safetensors version""": F"""{safetensors_version}""",
						    """Accelerate version""": F"""{accelerate_version}""",
						    """Accelerate config""": F"""{accelerate_config_str}""",
						    """PyTorch version (GPU?)""": F"""{pt_version} ({pt_cuda_available})""",
						    """Tensorflow version (GPU?)""": F"""{tf_version} ({tf_cuda_available})""",
						    """Flax version (CPU?/GPU?/TPU?)""": F"""{flax_version} ({jax_backend})""",
						    """Jax version""": F"""{jax_version}""",
						    """JaxLib version""": F"""{jaxlib_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__     ):
						return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]     ) + "\n"






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A_			:					Any    			=     logging.get_logger(__name__)



class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  str    				=			None



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

	return mapped

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Dict       , lowercase_:					str       , lowercase_:					Union[str, Any]       , lowercase_:					List[Any]       , lowercase_:					Dict       , lowercase_:					Optional[Any]   )	-> Union[str, Any]:
	# 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=lowercase_   ):
		return joblib.Parallel()(
		    joblib.delayed(lowercase_   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 		UpperCamelCase   (lowercase_:					str   )	-> str:
	A__				:       int	  =						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__				:       Optional[Any]	  =						None



